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url: https://www.aiprm.com/ai-art-statistics/
title: AI in Art Statistics 2024 · AIPRM
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# AI in **Art Statistics 2024**

![Image of a robot using a paintbrush alongside the title &lsquo;AI in Art Statistics 2024&rsquo;](/ai-art-statistics/AI-Art-Stats-HRP-FI_hu16aae853a1380b8b3c58a117fe8de72a_161267_1024x0_resize_q85_h2_box_3.webp)

Since the groundbreaking release of ChatGPT in late 2022, there has been an increasing focus on the generative AI tool’s growing ability to replicate creative pursuits traditionally carried out by humans.

While initially the focus was centered around generative AI’s ability to perform text-based tasks like article writing, poetry, and story creation, the rise of text-to-image platforms like Midjourney has seen more people than ever using the technology to produce visual art in the style of drawings, paintings, and photographs.

But what does the future hold for the use of AI in art? To shed more light on this increasingly relevant topic, [ AIPRM](https://www.aiprm.com/blog/) collated this AI art statistics page covering market size, facts about AI in art, and more.

Top 10 AI art statistics>

## Top 10 AI art statistics  #

  * The global AI image market is projected to be worth over $0.9 billion by 2030, a 254% increase from 2022 ($0.26 billion).
  * In 2030, at least one blockbuster film is expected to be released, with 90% of the content generated by AI.
  * Nearly half of artists (45.7%) found text-to-image technology very useful in their artistic process, with just under a third (31.5%) finding it somewhat useful.
  * Around one in ten (11.2%) artists had used text-to-image technology to create something resembling a fully digital work.
  * Less than one in ten (8.2%) artists said they planned to showcase their text-to-image generated work in art venues.
  * More than half (53.6%) of artists felt that they made a fundamental input to the artwork created while using text-to-image technology.
  * Midjourney is the most popular AI generator for creating images, holding more than a quarter of the market (26.8%).
  * Dall-E 2 was the most popular text-to-image tool among artists, with more than a quarter (28.3%) citing it as their preferred tool.
  * Nearly a third of text-to-image AI users considered the technology to be a major advancement for visual arts.
  * 70% of US adults think that artists should be compensated when generative AI uses their work to produce images.



AI in art market statistics>

## AI in art market statistics  #

The latest AI art statistics predict that the global AI image market could be worth over $0.9 billion by 2030. If projections are correct, it will mean the industry will more than triple in value (+254%) from 2022, when it was worth just under $0.26 billion.

A breakdown of the value of the global AI image market in 2022 and the projected values for 2023 and 2030>

#### A breakdown of the value of the global AI image market in 2022 and the projected values for 2023 and 2030  #

![ Bar chart showing the global value of the AI image market in 2022 and the projected values in 2023 and 2030](/ai-art-statistics/AI-Art-Stats-HRP-1_hu35151bf41c8ccd5e8ac78295d8400ac0_59447_1024x0_resize_q85_h2_box_3.webp)

* Figures from 2023 onwards are market projections

The projected industry value for 2023 stood at just under $0.3 billion ($0.29 billion), representing a rise of just under 12% from 2022. Should 2030 projections prove right, the AI art market size will grow by around 217% between 2023 and 2030.

How long has AI art been around?>

### How long has AI art been around?  #

While AI art may feel like a new phenomenon, its origins can be traced back to decades before the recent generative AI boom. Artist Harold Cohen began exploring the concept of using AI to create art in the early 1970s, resulting in the emergence of his groundbreaking Aaron program in 1973.

The primary function of Aaron was to teach a robot to create drawings, with Cohen honing and refining the program over 50 years to increase its accuracy and scope. While Aaron could only produce monochrome line drawings that were hand-coloured by Cohen in its early years, he later refined the program so that it could apply the colors independently. This resulted in the production of digital prints which are the complete work of Aaron.

![Mini infographic breaking down the development of early AI art tool Aaron.](/ai-art-statistics/AI-Art-Stats-HRP-14_hue5fe71017a3a542864dfff9072e62d4d_493138_1024x0_resize_q85_h2_box_3.webp)

The development continued over the next few decades, with Aaron able to produce increasingly abstract and sophisticated works by the 2000s. As a result, Cohen became an inspiration to many of the contemporary AI artists of the 21st century.

AI in art usage statistics>

## AI in art usage statistics  #

AI art stats from Gartner predicted that by 2030, at least one major blockbuster film will be released, with 90% of the film generated by AI (from text to video). With these groundbreaking developments on the horizon, Platform.io surveyed over 500 artists in various fields to get their views on text-to-image technology.

Nearly half (45.7%) claimed they found it very useful in their artistic process, with just under a third (31.5%) claiming it was somewhat useful but did not integrate into their workflow.

A breakdown of the opinions of text-to-image technology from surveyed artists>

#### A breakdown of the opinions of text-to-image technology from surveyed artists  #

![Pie chart breaking the opinions on the usefulness of text-to-image technology among artists.](/ai-art-statistics/AI-Art-Stats-HRP-2_hue00c555c2b79a167b51d159610adb5da_140821_1024x0_resize_q85_h2_box_3.webp)

Less than a fifth (16.1%) found text-to-image technology fun to play with while not useful — nearly three times less than the number that found it very useful. Meanwhile, over one in 20 (6.7%) considered the technology useless, around five times less than those who found it somewhat useful.

How are artists using AI?>

### How are artists using AI?  #

Analysis of AI art statistics found that more than one in ten (11.2%) surveyed artists have used text-to-image technology to create something resembling a fully digital work. This is almost three times less than the number of artists who have created image assets for use in other projects.

A breakdown of the usage levels of text-to-image technology among surveyed artists>

#### A breakdown of the usage levels of text-to-image technology among surveyed artists  #

![Donut chart breaking down the usage levels of text-to-image technology among surveyed artists.](/ai-art-statistics/AI-Art-Stats-HRP-3_hu4d502df34305539985815b619f037e55_156239_1024x0_resize_q85_h2_box_3.webp)

Over a third (35.1%) of artists have used a text-to-image platform to explore and develop new ideas, making this the most common reason overall. Meanwhile, just over 14% said they’d used the technology for fun — less than half the number who’d used it to create image assets for other projects.

A breakdown of the ways surveyed artists plan to showcase their text-to-image creations>

#### A breakdown of the ways surveyed artists plan to showcase their text-to-image creations  #

![Waffle chart breaking down the ways artists planned to showcase their text-to-image creations.](/ai-art-statistics/AI-Art-Stats-HRP-4_hu0c9ce44e621843dc3b6128e4d90b8c5a_85054_1024x0_resize_q85_h2_box_3.webp)

Despite the growing use of AI in art, less than one in ten (8.2%) surveyed artists said they plan to showcase their text-to-image artwork in art venues. Similarly, just over one in twenty (6.8%) claim they plan to sell their AI art creations as NFTs.

More than a third of respondents said they plan to share their text-to-image artwork on social media. This is around four times more than the number planning to exhibit their work in art venues and the most popular answer overall.

Meanwhile, over a third of artists claim they do not plan to showcase their AI art — around five times more than the number planning to sell their work as NFTs.

How much do humans contribute to the creation of AI art?>

### How much do humans contribute to the creation of AI art?  #

More than half (53.6%) of surveyed artists feel that they made a fundamental input to the artwork produced while using text-to-image technology, according to [ generative AI statistics](https://www.aiprm.com/generative-ai-statistics/). This is over 14% more than any other response, bearing testament to the power of [ effective prompts](https://www.aiprm.com/blog/how-to-write-ai-prompts/) when producing AI-based art.

A breakdown of the level of personal contribution surveyed artists felt they had when producing text-to-image artwork>

#### A breakdown of the level of personal contribution surveyed artists felt they had when producing text-to-image artwork  #

![Donut chart breaking down how much input surveyed artists felt they had when producing text-to-image artwork.](/ai-art-statistics/AI-Art-Stats-HRP-5_hua1e8f19c3e704c9f9a88ea14f0751509_122772_1024x0_resize_q85_h2_box_3.webp)

Nearly two-fifths (39%) feel they had very little contribution to the work produced, while less than one in ten (7.4%) feel they had no contribution. According to AI art statistics, over 92% of artists feel they had at least some influence on the artwork produced when using text-to-image technology.

AI art generator tools statistics>

## AI art generator tools statistics  #

Recent AI art statistics found that Midjourney is the most popular AI generator for creating images. Midjourney is responsible for over a quarter (26.8%) of the global market for generative AI image tools — over 2% more than any other platform.

A breakdown of the global market share of the biggest generative AI image tools in 2024>

#### A breakdown of the global market share of the biggest generative AI image tools in 2024  #

![Waffle chart showing the global market share of the biggest generative AI image tools in 2024](/ai-art-statistics/AI-Art-Stats-HRP-6_hu206f5f0615969ac8473ea57f63d08f06_88953_1024x0_resize_q85_h2_box_3.webp)

Dall-E holds the next highest market share, accounting for just under a quarter of the global market (24.35%) — 0.83% more than third-placed Nightcafe.

Starry AI and Stable Diffusion round out the top five, with market shares of 12.75% and 12.52%, respectively. This means that, combined, the five biggest generative AI image platforms are responsible for over 99% of the global market.

Which AI tools have created the most images?>

### Which AI tools have created the most images?  #

As of 2024, models based on the Stable Diffusion platform have created more than 12.5 billion images. This is over 12 times more than the next highest platform, making Stable Diffusion the only company responsible for more than 10 billion AI images.

A breakdown of the total AI images created by various AI art generators, as of 2024>

#### A breakdown of the total AI images created by various AI art generators, as of 2024  #

![Horizontal bar chart showing the total AI images created by the biggest AI art generators, as of 2024.](/ai-art-statistics/AI-Art-Stats-HRP-7_hu55e765a86b07c2fef2432089c2aac1c3_90111_1024x0_resize_q85_h2_box_3.webp)

Adobe Firefly was the only other platform to have reached 1 billion AI images as of 2024. This was around 4% more than Midjourney and 9% more than DALL-E 2 — the platforms with the third and fourth highest totals, respectively.

What are the most popular AI tools among artists?>

### What are the most popular AI tools among artists?  #

Recent [ AI statistics](https://www.aiprm.com/ai-statistics/) found more than a quarter (28.3%) of artists citing Dall-E 2 as their preferred AI image creation tool in 2023, making it the most popular tool among artists. This was around 1% more than Midjourney (27.1%), making these platforms the only ones chosen by over a fifth of artists.

It’s worth noting that Dall-E 2 has since been updated by Dall-E 3, which was released in August 2023.

A breakdown of the most popular AI art tools among artists>

#### A breakdown of the most popular AI art tools among artists  #

![Pie chart showing the most popular AI art tools among artists](/ai-art-statistics/AI-Art-Stats-HRP-8_hu1e9d32967041bba1dc7eaf4bc2896650_140145_1024x0_resize_q85_h2_box_3.webp)

Over one in ten said Playform is their preferred AI tool for image creation, which is less than half the number for Midjourney and Dall-E 2. Meanwhile, just over one in twenty opted for Starry AI, 7% less than the number who cited Playform.

AI draws from databases that collect digitized works of human creativity. LAION-5B, an [ online set of nearly 6 billion pictures](https://laion.ai/blog/laion-5b/), has enabled computer models like Stable Diffusion to make derivative images, such as headshot avatars remixed into superheroic or anime styles that went viral on Twitter in 2022.

> AI art draws from large databases that store digitized versions of work created by humans. LAION-5B is a large-scale data set that has been used to power leading platforms like Stable Diffusion. The platform draws on a set of nearly 6 billion images to produce a wide range of artwork and imagery.

Attitudes towards AI in art>

## Attitudes towards AI in art  #

A 2023 report from Pew Research found that nearly a third (31%) of people who’d used text-to-image AI software considered it a major advancement for visual arts. Additionally, nearly two-fifths (39%) considered it a minor advancement, meaning seven in ten users believe the software has advanced visual arts to some degree.

A 2023 survey from Book an Artist found that nearly three-quarters (74%) of artists consider the process of AI scraping artwork from the internet to be unethical, with a further 89% worried that copyright laws are outdated.

More than half (54.6%) fear that AI’s impact on artists could result in a decrease in income, with almost three-quarters (73%) saying they want to be asked for permission before their artwork is used to train an algorithm.

![Mini infographic outlining the number of text-to-image AI software users who consider it an advancement for visual arts.](/ai-art-statistics/AI-Art-Stats-HRP-15_hu411a424574de6196e7f7051e906f5e2f_509359_1024x0_resize_q85_h2_box_3.webp)

Should artists be compensated when AI copies their work?>

### Should artists be compensated when AI copies their work?  #

The latest AI art statistics found that seven out of ten (70%) US adults believe that artists should be compensated in instances where generative AI has utilized their work to produce images.

A breakdown of the attitudes of surveyed US adults on the use of generative AI for copying artists’ work in April 2023>

#### A breakdown of the attitudes of surveyed US adults on the use of generative AI for copying artists’ work in April 2023  #

![Horizontal bar chart showing the attitudes of surveyed artists around AI copying artists work.](/ai-art-statistics/AI-Art-Stats-HRP-9_hu1390991292774e0dd364af4d14342635_114615_1024x0_resize_q85_h2_box_3.webp)

Over two-fifths (44%) of surveyed adults claimed they’d asked AI to copy an artist’s style, and a similar number (43%) believed that companies should ban the practice.

A breakdown of the attitudes of US adults around artistic credit for AI-based art>

#### A breakdown of the attitudes of US adults around artistic credit for AI-based art  #

![Proportional circles chart showing the crediting attitudes of surveyed artists around AI image creations.](/ai-art-statistics/AI-Art-Stats-HRP-10_hud26174f891e11568d83ac213dee109e3_159110_1024x0_resize_q85_h2_box_3.webp)

Less than a tenth (9%) of respondents in a YouGov survey believe that the person entering the prompts when creating AI art deserves to be credited. This is 6% lower than the number who believe the developers of the tool should be credited (15%).

Over a tenth (13%) believe that the artist whose work the tool has been trained on deserves to be credited — 4% more than those who believe credit belongs to the prompt writer.

More than a fifth (21%) believe the credit should be shared between the prompt writer, the original artist, and the tool. This is 8% more than the number who believe that credit lies solely with the AI program, making it the most popular position overall.

Can AI in art cause the spread of more fake news/misinformation?>

### Can AI in art cause the spread of more fake news/misinformation?  #

There is considerable concern around image-based AI’s capacity to spread misinformation, with over half (53%) of respondents in a YouGov survey believing AI image tools are very or somewhat likely to contribute to the spreading of fake news.

A breakdown of the percentage of adults in the US who feel AI image tools will be used to spread fake news>

#### A breakdown of the percentage of adults in the US who feel AI image tools will be used to spread fake news  #

![Circle chart showing the attitudes of surveyed artists around AI image tools&rsquo; potential to spread fake news.](/ai-art-statistics/AI-Art-Stats-HRP-11_hu8b0fbd993ab6d99b2c0ca74831591398_163246_1024x0_resize_q85_h2_box_3.webp)

This was 40% more than the number of people who believe AI image tools are very or somewhat unlikely to spread fake news (13%), with over a third (34%) saying they are unsure.

Should AI-generated art be considered art?>

### Should AI-generated art be considered art?  #

In 2023, an art competition at the Colorado State Fair was won by a piece of digital artwork. Created by Jason M. Allen, the piece, titled Théâtre D’opéra Spatial or Space Opera Theater, was created using Midjourney.

The result sparked a lot of controversy, with many debating whether AI-generated images should be considered art.

> Visit our [ blog](https://www.aiprm.com/blog/) for expert advice on a range of topics including an [ artificial intelligence guide for creatives](https://www.aiprm.com/education/an-artificial-intelligence-guide-for-creatives/).

A breakdown of the percentage of survey respondents who believe AI-generated images should be considered art>

#### A breakdown of the percentage of survey respondents who believe AI-generated images should be considered art  #

![Comparative circle chart showing the percentage of surveyed artists who believe AI-generated images should be considered art.](/ai-art-statistics/AI-Art-Stats-HRP-12_hu2727f17776cdcae8f2a533d0a18e8670_96350_1024x0_resize_q85_h2_box_3.webp)

A 2023 survey from news outlet KOAA found that over three-quarters (76%) of people believed AI-generated content like Allen’s piece should not be considered art. This was more than three times more than the number of people who believed that AI generated images should be considered art (24%).

How easy is it to distinguish AI art from human art?>

## How easy is it to distinguish AI art from human art?  #

In a bid to distinguish the difficulty in distinguishing AI art from human art, seven researchers from the University of Chicago conducted a study incorporating 280 human artwork creations and 320 AI-generated images.

They then put each image through six prominent AI detection tools to compare the accuracy levels of each tool.

AI in art value statistics>

## AI in art value statistics  #

As of July 2024, the record for the most expensive piece of AI art ever sold stands at $432,500, according to AI art statistics. The piece, titled _Portrait of Edmond de Bellamy_ , was sold for this fee on the 25th of October 2018 at a Christie’s auction in New York.

Elsewhere, one of the best-selling AI-generated NFT artists is Botto, an AI art tool which uses algorithms to create ideas based on the feedback and ideas of its community members.

After just a month of existence, Botto created and sold four AI-generated NFTs for $1.1 million. By 2024, the program had generated over $4 million in sales in the three years since it was launched.

![Mini infographic talking about the most expensive piece of AI art ever sold](/ai-art-statistics/AI-Art-Stats-HRP-16_hubbadadddba0a736d2bd8cec3a53ca26c_453564_1024x0_resize_q85_h2_box_3.webp)

The future of AI in art>

## The future of AI in art  #

Recent AI art statistics found that nearly half (45.6%) of artists feel text-to-image software will have a dramatic positive influence on creative practices. This was the most common response, with over a third (37.4%) believing the creative process would be somewhat affected.

A breakdown of the levels surveyed artists feel that text-to-image software will affect their creative practice in the future>

#### A breakdown of the levels surveyed artists feel that text-to-image software will affect their creative practice in the future  #

![Donut chart breaking down the levels surveyed artists feel text-to-image software will affect their creative practice in the future.](/ai-art-statistics/AI-Art-Stats-HRP-13_hu91bb52ee41c26b13a66fb7a23aa3f7f9_168607_1024x0_resize_q85_h2_box_3.webp)

Just over one in twenty (6.6%) believe text-to-image software would dramatically affect creative practice negatively — around 40% less than those predicting a dramatic positive change.

Meanwhile, nearly a tenth (9%) believe the technology will have no effect on creativity, with just one in 100 (1.4%) believing text-to-image software will completely kill creative practices.

AI in art FAQs>

## AI in art FAQs  #

What is the top-rated AI art generator?

Dall-E 2 was the AI art generator cited by most artists as their preferred platform in 2023. Over a quarter (28.3%) of surveyed artists picked Dall-E 2 as their top choice — 1.2% more than second-placed Midjourney. It’s worth noting that Dall-E 2 has since been updated by Dall-E 3, which was released in August 2023.

What percentage of people like AI art?

A survey from YouGov found that 56% of respondents who’d seen AI art claimed they liked it, compared to just 19% who didn’t.

Where does AI art get data from?

Text-to-image generative AI platforms are built on large databases made up of numerous image and text-based sources. By training on these vast sets of data, the programs are then able to accurately decipher the type of imagery a human is requesting based on their prompts.

When did AI art become popular?

While the concept of AI and art has been experimented with for decades, the generative AI boom towards the end of 2022 dramatically increased knowledge and interest in AI art. Since then, many artists have experimented with technology as a tool to enhance their existing artistic process, while numerous non-artists have used these programs as image creation tools, both for work activities and leisure.

Will AI take over art?

Although it seems unlikely that AI will replace the traditional artistic process in the future, it will likely play a key role in the development and evolution of art in the coming decades.

By working on human prompts, AI has the power to enhance the ideation process for artists, bringing to life ideas that can then be developed further by the artists using their traditional methods.

Also, the growing interest in AI-based art shows no sign of waning, meaning it’s likely that the AI art market will continue to generate its own interest separately from the traditional art industry.

Why is AI-generated art bad?

Critics of AI-generated art believe that it lacks the emotion, originality, and intent associated with the best human art. As the technology relies on interpreting data from existing work, many critics feel that it lacks the ability to innovate in the way that traditional art can.

Will AI art replace artists?

While AI may replace certain image creation tasks performed by artists in workplaces, it’s unlikely that they will replace human artists completely. Because image-based AI relies on interpreting existing work created by humans to produce its own art, it cannot create truly independent artwork in the same way that a human can.

How to use prompts in AI art?

Prompts work in AI art by taking a descriptive written theme from a human and interpreting it into an image. Therefore, making your prompts as specific and descriptive as possible will increase the likelihood of the AI program accurately interpreting your idea.

Glossary>

## Glossary  #

Chat-GPT>

### Chat-GPT  #

Chat-GPT is a chatbot and virtual assistant released by Open AI in November 2022. The program is trained on large language models to answer questions and perform text-based tasks based on human prompts.

Generative AI>

### Generative AI  #

Generative AI is a type of artificial intelligence used to create content like text, images, audio, and video. Generative AI programs interpret requests by humans known as prompts, to produce content based on large data sets.

Prompt>

### Prompt  #

A prompt is a text-written human command given to an AI program, that the program will use to create a piece of text or imagery.

Text-to-image software>

### Text-to-image software  #

Text-to-image software is a type of generative AI program in which images and artwork are created by an AI based on its interpretation of human prompts.

Sources>

## Sources  #

[ https://www.fortunebusinessinsights.com/ai-image-generator-market-108604](https://www.fortunebusinessinsights.com/ai-image-generator-market-108604)

[ https://www.katevassgalerie.com/blog/harold-cohen-aaron-computer-art](https://www.katevassgalerie.com/blog/harold-cohen-aaron-computer-art)

[ https://en.wikipedia.org/wiki/AARON](https://en.wikipedia.org/wiki/AARON)

[ https://www.playform.io/editorial/survey](https://www.playform.io/editorial/survey)

[ https://www.gartner.com/en/articles/beyond-chatgpt-the-future-of-generative-ai-for-enterprises](https://www.gartner.com/en/articles/beyond-chatgpt-the-future-of-generative-ai-for-enterprises) [ https://www.statista.com/forecasts/1423989/world-generative-ai-image-tool-market-share](https://www.statista.com/forecasts/1423989/world-generative-ai-image-tool-market-share)

[ https://www.similarweb.com/website/midjourney.com/](https://www.similarweb.com/website/midjourney.com/)

[ https://whatsthebigdata.com/ai-art-generator-statistics/](https://whatsthebigdata.com/ai-art-generator-statistics/)

[ https://www.popsci.com/technology/artificial-intelligence-art-statistics/](https://www.popsci.com/technology/artificial-intelligence-art-statistics/)

[ https://www.pewresearch.org/short-reads/2023/02/22/how-americans-view-emerging-uses-of-artificial-intelligence-including-programs-to-generate-text-or-art/](https://www.pewresearch.org/short-reads/2023/02/22/how-americans-view-emerging-uses-of-artificial-intelligence-including-programs-to-generate-text-or-art/)

[ https://bookanartist.co/blog/2023-artists-survey-on-ai-technology/](https://bookanartist.co/blog/2023-artists-survey-on-ai-technology/)

[ https://www.statista.com/statistics/1400650/perception-of-copying-artists-work-using-ai-us/](https://www.statista.com/statistics/1400650/perception-of-copying-artists-work-using-ai-us/)

[ https://business.yougov.com/content/45765-half-americans-think-ai-art-will-be-used-spread-fa](https://business.yougov.com/content/45765-half-americans-think-ai-art-will-be-used-spread-fa)

[ https://yaledailynews.com/blog/2023/01/23/what-ai-art-means-for-society-according-to-yale-experts/](https://yaledailynews.com/blog/2023/01/23/what-ai-art-means-for-society-according-to-yale-experts/)

[ https://www.koaa.com/news/news5-originals/koaa-survey-should-ai-generated-images-be-considered-art](https://www.koaa.com/news/news5-originals/koaa-survey-should-ai-generated-images-be-considered-art)

[ https://www.christies.com/en/stories/a-collaboration-between-two-artists-one-human-one-a-machine-0cd01f4e232f4279a525a446d60d4cd1](https://www.christies.com/en/stories/a-collaboration-between-two-artists-one-human-one-a-machine-0cd01f4e232f4279a525a446d60d4cd1)

[ https://beincrypto.com/ai-artist-made-3-million-selling-nfts/](https://beincrypto.com/ai-artist-made-3-million-selling-nfts/)

[ https://academyofanimatedart.com/ai-art-statistics/](https://academyofanimatedart.com/ai-art-statistics/)

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url: https://bytemedirk.medium.com/the-ethical-implications-of-ai-on-creative-professionals-38ec6ed983e2
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# The Ethical Implications of AI on Creative Professionals

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Expanding on my current studies in Ethical Computing Technologies in Society, I decided it would be a good idea to compile my research and talks into what I hope will be a useful discussion. As an IT professional with many years of experience in the industry, as well as someone who grew up in a family of successful writers, artists, and musicians, I find this topic especially important to me as a professional who plays (at least a small) role in the adoption and development of AI technologies today. It is reasonable to assume that all IT professionals have a role in the integration of AI tools in business and personal life, and I feel that talks like these are critical as our IT landscapes expand and change for the better or worse.

OpenAI’s ChatGPT and DALL-E have transformed AI into digital content and art creation, blurring the line between human and machine innovation. This has raised concerns about AI models plagiarizing existing artists’ work and replacing their skills with AI models that can produce artwork quickly. Nightshade, a tool that protects artists’ intellectual property rights by “poisoning” AI models like Midjourney, GPT, and DALL-E, has emerged as a disruptive tool in the face of an overabundance of AI models attempting to duplicate human originality and artistic ability. However, the use of AI for creative output may stifle the development of unique and inventive ideas, potentially jeopardizing future opportunities for painters, writers, content creators, and other artists in various lucrative industries (Coeckelbergh, 2023).

The ethical implications of AI models in art creation include concerns about the authenticity and originality of artistic works, as well as the potential harm to AI model authors’ intellectual property. The introduction of Nightshade and similar tools has serious ethical and legal ramifications for the creative industry, as well as job losses and future job opportunities in an evolving AI landscape (Peter, 2023).

Canva.com AI Generated Art with Prompt “A golden sculpture of a rabbit, red background”

Nightshade, an AI model, is used to protect artists’ intellectual property rights by “poisoning” models like Midjourney, GPT, and DALL-E by altering their training data through website scraping. This results in a degradation of their performance and outputs. The poisoned art is then disseminated to the internet, where models systematically scrape for new content consume it as training data. This degrades the AI model’s output, preventing individuals and businesses from effectively adopting these models (Heikkilä, 2023).

The University of Chicago’s Glaze and Nightshade team showcases images produced by both the poisoned and clean AI models.

Nightshade’s research and development include data poisoning techniques, which introduce unexpected behaviors into machine learning models while training. These samples, visually identical to benign images but containing significant differences in image data, impair the model’s ability to generate meaningful images. For example, if the prompt “Dog” is given, the poisoned model could return a picture of a cat instead.

Artists can now download Nightshade from the University of Chicago’s website to test their digitized artwork before it is published online. The tool, along with Glaze, adds digital “noise” to an image, making it unreadable by AI, so to speak. These tools enable artists to influence AI model training by disrupting the traditional process. This is similar to how AI model creators scraped the web without giving artists control. Artists inject poisoned data onto the web, destabilizing corporations’ work without giving them control (Shan et al., 2023).

## **Some Real-world Examples & Implications**

In 2022, the Colorado State Fair’s annual art competition featured AI-generated artwork, including Jason M. Allen’s “Théâtre D’opéra Spatial.” This was the first time AI-generated artwork had won a prize, prompting outrage among artists who accused Allen of cheating. AI’s ability to mimic established artists’ styles and create customizable artworks using text prompts has raised concerns about originality, validity, ownership, and the future of artist employment opportunities. AI can analyze large amounts of existing artwork, blurring the line between inspiration and reproduction, undermining the importance of human creativity and originality (Roose, 2022).

Théâtre D’opéra Spatial  
Jason M. Allen — Colorado State Fair

The New York Times has criticized OpenAI and Microsoft for stealing copyrighted articles to build their AI models, sparking debates about data privacy and ownership. This has resulted in legal disputes between writers, authors, and artists who are fighting back against the systematic appropriation of their work by tech business AI models. While copyright law protects authors’ rights, AI-generated content has the potential to expand artistic possibilities and accessibility. However, recent events highlight the importance of regulating AI technology, as the line between inspiration and imitation blurs, potentially leading to conflicts and issues of intellectual property, copyright ownership, fair use, and the loss of creative job opportunities (De Vynck & Izadi, 2023).

The Washington Post “New York Times sues OpenAI, Microsoft for using articles to train AI”

In January 2023, artists sued text-to-image model creators Midjourney and Stable Diffusion for stealing and profiting from their work without permission. The disregard for artists’ rights and livelihoods creates a risky precedent for the creative community and industry. It is critical to investigate artists’ well-being and the consequences of exploitation of their work, as well as the future role of businesses in providing visibility and distribution options (Tangermann, 2023).

Futurism “Artists Sue Stable Diffusion and Midjourney for Using Their Work to Train AI That Steals Their Jobs”

The UK Supreme Court ruled in December 2023 that an AI contribution to a patent cannot be considered the invention of the patent, instead allowing only natural persons to be designated as inventors. The debate revolves around whether AI should be granted intellectual property rights, with some arguing that AI should serve society rather than intellectual ownership (Reporter, 2023).

The Guardian “AI cannot be named as patent ‘inventor’, UK supreme court rules”

## The Ethical Considerations

The ethical implications of AI-generated art include authorship, originality, intellectual property infringement, and job loss. AI-generated art challenges traditional notions of creativity and the value of human artists. It can disrupt traditional creative markets and raise concerns about human labor in the creative process. AI-generated art can also diminish the originality of human creation, violating human authors’ intellectual property rights by using existing works to create new art.

Originality is another concern, as AI-generated art has the potential to replicate the uniqueness and distinction of human artists. Companies like Microsoft and OpenAI have been accused of exploiting artists through their AI art generation technologies, disrupting job opportunities for creatives. This raises ethical concerns about intellectual property rights, fair compensation, and the impact on the artistic community and their work. As AI technologies advance, questions of ownership, responsibility, and accountability arise, necessitating the establishment of transparency, guidelines, regulations, and laws to protect artists, their livelihoods, rights, and creativity.

Artists must strike a balance between using tools for creative advancement while remaining authentic and ethical, and using tools for commercial espionage. The proper application of AI tools requires a thorough examination of how they affect artistic expression, originality, and the broader implications for the creative community.

## So, how do we deal with it?

Artists have expressed concerns about the impact of AI art generators on the industry, ethical issues, and the potential devaluation of creative effort. Critics argue that AI-generated art reduces job opportunities, undervalues artists’ work, and lacks originality. Some artists have even called for boycotts to protect artistic integrity and human creativity. Artists are actively advocating for stronger rights for themselves and their work through activism, educational initiatives, and negotiations with AI generator platforms (Shaffi, 2023).

The intersection of AI enterprises and artists has created moral issues due to the complicated relationship between technological innovation, artistic integrity, and ethical considerations. AI art generators present challenges by potentially undervaluing human artists’ work, exploiting labor without fair compensation, and blurring the distinction between genuine artistic creation and AI-generated replicas. Artists navigate the use of AI technology by advocating for transparency, ethical practices, and fair compensation. A balanced approach is required to protect artists’ rights and interests while maximizing AI’s potential for artistic expression (Guild, 2023).

The rise of AI-generated art has profound implications for the artistic community, challenging traditional notions of creativity, originality, and authorship, as well as undermining the ethical implications of individuals’ ability to earn a living while maintaining the integrity of their craft. The shift in artistic practices and impact on artistic identity will result in difficulties in expressing their uniqueness and the worth of their work, resulting in a loss of artistic identity, jobs, authorship, and purpose.

Ethical AI development should prioritize transparency, data protection, and accountability to reduce the possibility of AI-generated art infringing on creatives’ rights while ensuring technology breakthroughs meet ethical standards. Collaboration among creatives, politicians, and AI developers is critical for developing a better understanding of the ethical issues raised by AI-generated art.

## References

Abbott, R. (2021, September 10). [_https://artificialinventor.com/first-patent-granted-to-the-artificial-inventor-project/_](https://artificialinventor.com/first-patent-granted-to-the-artificial-inventor-project/). Retrieved March 5, 2024, from <https://artificialinventor.com/first-patent-granted-to-the-artificial-inventor-project/>

Coeckelbergh. (2023). _The Work of Art in the Age of AI Image Generation: Aesthetics and Human-Technology Relations as Process and Performance_. <https://journals.open.tudelft.nl/jhtr/article/view/7025>

De Vynck, G., & Izadi, E. (2023, December 28). _New York Times Sues OpenAI, Microsoft for Using Articles to Train AI_. Washington Post. Retrieved March 5, 2024, from <https://www.washingtonpost.com/technology/2023/12/27/new-york-times-sues-openai-chatgpt/>

Heikkilä, M. (2023, December 19). [_https://www.technologyreview.com/2023/10/23/1082189/data-poisoning-artists-fight-generative-ai/_](https://www.technologyreview.com/2023/10/23/1082189/data-poisoning-artists-fight-generative-ai/). MIT Technology Review. Retrieved March 5, 2024, from <https://www.technologyreview.com/2023/10/23/1082189/data-poisoning-artists-fight-generative-ai/>

Shan, S., Cryan, J., Wenger, E., Zheng, H., Hanocka, R., & Zhao, B. Y. (2023, February 8). _Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models_. arXiv.org. <https://arxiv.org/abs/2302.04222>

Peter. (2023). _Generative AI tools in art education: Exploring prompt engineering and iterative processes for enhanced creativity_. <https://digitalcommons.lindenwood.edu/faculty-research-papers/477/>

Reporter, G. S. (2023, December 20). _AI Cannot Be Named as Patent ‘Inventor’, UK Supreme Court Rules_. the Guardian. Retrieved March 5, 2024, from <https://www.theguardian.com/technology/2023/dec/20/ai-cannot-be-named-as-patent-inventor-uk-supreme-court-rules>

Roose, K. (2022, September 2). _An A.I.-Generated Picture Won an Art Prize. Artists Aren’t Happy. (Published 2022)_. The New York Times. Retrieved March 5, 2024, from <https://www.nytimes.com/2022/09/02/technology/ai-artificial-intelligence-artists.html>

Tangermann, V. (2023, January 18). _Artists Sue Stable Diffusion and Midjourney for Using Their Work to Train AI That Steals Their Jobs_. Futurism. Retrieved March 5, 2024, from <https://futurism.com/artists-sue-stabile-diffusion-midjourney>

Vincent, J. (2018, October 23). [_https://www.theverge.com/2018/10/23/18013190/ai-art-portrait-auction-christies-belamy-obvious-robbie-barrat-gans_](https://www.theverge.com/2018/10/23/18013190/ai-art-portrait-auction-christies-belamy-obvious-robbie-barrat-gans). The Verge. Retrieved March 5, 2024, from <https://www.theverge.com/2018/10/23/18013190/ai-art-portrait-auction-christies-belamy-obvious-robbie-barrat-gans>

Wadhwani, P., & Pathak, A. (1970, January 1). _AI Image Generator Market Size, Share Analysis Report 2032_. Global Market Insights Inc. Retrieved March 5, 2024, from <https://www.gminsights.com/industry-analysis/ai-image-generator-market>

Shan, S., Ding, W., Passananti, J., Wu, S., Zheng, H., & Zhao, B. Y. (2023, October 20). Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models. arXiv.org. <https://arxiv.org/abs/2310.13828>

Thubron, R. (2023, October 25). Nightshade tool can &quot;poison&quot; images to thwart AI training and help protect artists. TechSpot. <https://www.techspot.com/news/100618-nightshade-tool-poisons-images-thwart-generative-ais-help.html>

Shaffi, S. (2023, January 23). _‘It’s the opposite of art’: why illustrators are furious about AI_. The Guardian. <https://www.theguardian.com/artanddesign/2023/jan/23/its-the-opposite-of-art-why-illustrators-are-furious-about-ai>

Guild, G. A. (2023, September 27). _AI Art Generators: Where We Stand — The Graphic Artist Guild_. The Graphic Artist Guild. <https://graphicartistsguild.org/ai-art-generators-where-we-stand/>

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url: http://arxiv.org/abs/2307.04028v1
title: [2307.04028v1] Measuring the Success of Diffusion Models at Imitating Human Artists
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# Computer Science > Computer Vision and Pattern Recognition

**arXiv:2307.04028v1** (cs) 

[Submitted on 8 Jul 2023]

# Title:Measuring the Success of Diffusion Models at Imitating Human Artists

Authors:[Stephen Casper](https://arxiv.org/search/cs?searchtype=author&query=Casper,+S), [Zifan Guo](https://arxiv.org/search/cs?searchtype=author&query=Guo,+Z), [Shreya Mogulothu](https://arxiv.org/search/cs?searchtype=author&query=Mogulothu,+S), [Zachary Marinov](https://arxiv.org/search/cs?searchtype=author&query=Marinov,+Z), [Chinmay Deshpande](https://arxiv.org/search/cs?searchtype=author&query=Deshpande,+C), [Rui-Jie Yew](https://arxiv.org/search/cs?searchtype=author&query=Yew,+R), [Zheng Dai](https://arxiv.org/search/cs?searchtype=author&query=Dai,+Z), [Dylan Hadfield-Menell](https://arxiv.org/search/cs?searchtype=author&query=Hadfield-Menell,+D)

View a PDF of the paper titled Measuring the Success of Diffusion Models at Imitating Human Artists, by Stephen Casper and 7 other authors

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> Abstract:Modern diffusion models have set the state-of-the-art in AI image generation. Their success is due, in part, to training on Internet-scale data which often includes copyrighted work. This prompts questions about the extent to which these models learn from, imitate, or copy the work of human artists. This work suggests that tying copyright liability to the capabilities of the model may be useful given the evolving ecosystem of generative models. Specifically, much of the legal analysis of copyright and generative systems focuses on the use of protected data for training. As a result, the connections between data, training, and the system are often obscured. In our approach, we consider simple image classification techniques to measure a model's ability to imitate specific artists. Specifically, we use Contrastive Language-Image Pretrained (CLIP) encoders to classify images in a zero-shot fashion. Our process first prompts a model to imitate a specific artist. Then, we test whether CLIP can be used to reclassify the artist (or the artist's work) from the imitation. If these tests match the imitation back to the original artist, this suggests the model can imitate that artist's expression. Our approach is simple and quantitative. Furthermore, it uses standard techniques and does not require additional training. We demonstrate our approach with an audit of Stable Diffusion's capacity to imitate 70 professional digital artists with copyrighted work online. When Stable Diffusion is prompted to imitate an artist from this set, we find that the artist can be identified from the imitation with an average accuracy of 81.0%. Finally, we also show that a sample of the artist's work can be matched to these imitation images with a high degree of statistical reliability. Overall, these results suggest that Stable Diffusion is broadly successful at imitating individual human artists. 

Comments: | Accepted to the 1 st Workshop on Generative AI and Law  
---|---  
Subjects: |  Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)  
Cite as: | [arXiv:2307.04028](https://arxiv.org/abs/2307.04028) [cs.CV]  
  | (or  [arXiv:2307.04028v1](https://arxiv.org/abs/2307.04028v1) [cs.CV] for this version)   
  |  <https://doi.org/10.48550/arXiv.2307.04028> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Stephen Casper [[view email](/show-email/ba333bcd/2307.04028)]   
**[v1]** Sat, 8 Jul 2023 18:31:25 UTC (9,451 KB)  


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url: https://artsmart.ai/blog/ai-image-generator-market-statistics/
title: AI Image Generator Market Statistics: 2024 Growth & Trends
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  3. AI Image Generator Market Statistics: An Analysis




# AI Image Generator Market Statistics: An Analysis

![Shalwa](https://secure.gravatar.com/avatar/a8074a74e4bcc5941be86bf1c032f133?s=96&d=mm&r=g)

by Shalwa

September, 12, 2024 

The AI image generator market is witnessing unprecedented growth, revolutionizing creative industries and captivating businesses and consumers alike. This article provides an in-depth examination of the latest statistics, offering a comprehensive view of market size, user adoption, and the impact of this transformative technology across various sectors.

## **Market Size and Projected Growth**

In the near future, the AI-powered image generation is expected to experience substantial growth due to rapid technological advancements and widespread adoption across various industries. This cutting-edge technology holds great promise for revolutionizing visual content creation and delivery.

**The global AI image generator market is expected to grow from $257.2 million in 2022 to $917.4 million by 2030.**  

This impressive growth trajectory represents a compound annual growth rate (CAGR) of 17.4%, highlighting the rapidly increasing demand for AI-generated visual content.

**The broader generative AI market, which includes image generation, is valued at $66.66 billion in 2024.**  

This figure underscores the massive scale of the generative AI ecosystem, with image generation playing a crucial role in its expansion.

**By 2030, the generative AI market is projected to reach $207 billion.**  

The substantial growth forecast for the overall [generative AI market](https://artsmart.ai/blog/generative-ai-statistics-2024/) sector indicates sustained interest and investment in these technologies, with image generation likely to remain a key component.

to content ↑

## **Image Generation Volume**

The sheer volume of AI-generated images produced daily is staggering, demonstrating the widespread adoption and use of these tools. This exponential growth in AI-generated imagery reflects the rapid advancements and growing reliance on AI technology in our daily lives, and highlights the need for continued exploration and understanding of its capabilities and implications.

**Over 15 billion AI-generated images have been created since 2022.**  

This astronomical figure highlights the explosive popularity of AI image generators and their integration into various workflows and creative processes.

**Approximately 34 million new AI-generated images are created every day.**  

The daily production rate showcases the ongoing demand for AI-generated visual content across industries and applications.

to content ↑

## **User Adoption and Perception**

The implementation of AI image generators, goes beyond simply creating a large number of images. These advanced tools offer valuable insights into the intricate nuances of user preferences and perceptions concerning visual content.

![americans perceive ai-generated art survey for ai image generator article](https://lh7-rt.googleusercontent.com/docsz/AD_4nXdFpEwECt9LAXxCQdIXAt9sHtueo16foMag4NJCPACTDjQkSP5yTpczTnRq04WVJR2QHORufwBZqB9OnEC8QBEW4Sfe41C90mqexOG1FFmn60f0HLrVOJzX4FBQG8rHj2ZhntwIaYJmj9BYLKhA_VqBt8TM?key=WVmD4ODPlqy5IOn5Q4qQ9g)

Source: TechReport.com

**56% of people who have encountered AI-generated art reported enjoying it.**  

This positive reception suggests a growing acceptance of AI-created visuals among the general public.

**34% of those who have seen AI-generated art consider it superior to human-created art.**  

While this statistic may be concerning for some artists, it underscores the improving quality and appeal of AI-generated images.

### **Generational Perspectives**

Interestingly, there are notable differences in how different age groups perceive AI-generated art:

**48% of American millennials believe AI-generated art should be considered "real" art.**  

This statistic suggests a greater openness to AI-created content among younger generations.

**42% of Gen Z members share the belief that AI-generated art qualifies as real art.**  

While slightly lower than millennials, this figure still indicates significant acceptance of AI art among younger demographics.

to content ↑

## **Industry Adoption and Use Cases**

The adoption of AI image generators extends beyond just volume, with interesting insights into user preferences and perceptions. As AI image generators become more prevalent in various industries, understanding user adoption and perception is crucial for their successful integration and acceptance in the market. User adoption reflects how readily users are embracing AI image generators in their workflows, while user perception delves into their attitudes, beliefs, and interpretations of the generated content. 

![use cases of generative ai survey for ai image generator article](https://lh7-rt.googleusercontent.com/docsz/AD_4nXe96C7N5sFsPFWPReFRYSaNrgYRGnsEzTPdDvE6loTdOWs4OpbqFSNXGDVerStrb8bZl87e-kaTPzhiKCTq4LOepgUDUyVzvQ8iQukDq8SbVKlZiL3XIpjXDVCuwXrPP-0VUfUqBFqOycaz9WbQNIQiCsHu?key=WVmD4ODPlqy5IOn5Q4qQ9g)

Contents

  * **Market Size and Projected Growth**
  * **Image Generation Volume**
  * **User Adoption and Perception**
  * **Industry Adoption and Use Cases**
  * **Geographical Insights**
  * **Challenges and Concerns**
  * **Conclusion**



Source: Statista

**39% of US marketers use AI to create social media images.**  

This statistic highlights the significant role AI is playing in streamlining social media content creation for marketing professionals.

**36% of US marketers utilize AI image generators for website visuals.**  

The adoption of AI for website imagery demonstrates its value in creating diverse and engaging online experiences.

**Only 16% of PR professionals worldwide reported using AI for image generation.**  

This lower adoption rate in PR suggests potential room for growth and education about the benefits of AI image generation in this sector.

### **Consumer Usage**

Beyond professional applications, consumers are also embracing AI image generators:

**34% of consumers primarily use AI image generators for entertainment purposes.**  

This statistic underscores the technology's appeal as a creative tool for personal use and enjoyment.

**71% of images shared on social media globally were AI-generated.**  

This strikingly high percentage demonstrates the pervasive influence of [AI-generated content](https://artsmart.ai/blog/ai-generated-content-statistics-2024/) in everyday social media interactions.

**In APAC and Canada, the percentage of AI-generated images shared on social media rises to 77%.**  

This regional variation highlights differing adoption rates and comfort levels with AI-generated content across global markets.

to content ↑

## **Geographical Insights**

The adoption and impact of AI image generators vary across different regions:

**North America held a 39.99% market share in the AI image generation market in 2022.**  

This dominant position establishes North America as a major hub for AI image generation technology and adoption.

**The Asia Pacific region is experiencing rapid growth in AI image generation adoption.**  

Driven by technological advancements and increasing digital transformation efforts, the APAC region is poised to be a significant player in the global AI image generation market.

## **Challenges and Concerns**

Despite the rapid growth and adoption, the AI image generation market faces several challenges and ethical concerns:

**A 2024 Yale University study found that 54% of people could distinguish between AI-generated and human-made art.**  

This statistic suggests that while AI-generated images are becoming increasingly sophisticated, there are still noticeable differences that many people can detect.

**Only 27% of people in the US believe they've encountered AI-generated art.**  

This low percentage indicates a potential lack of awareness about the prevalence of AI-generated content, which could lead to issues of transparency and [public trust](https://artsmart.ai/blog/public-trust-in-ai-statistics/).

### **Ethical and Legal Considerations**

The rapid advancement of AI image generation technology has raised several ethical and legal questions:

  * Concerns over data privacy and the potential misuse of personal images in training datasets.
  * The risk of deepfakes and other forms of visual misinformation.
  * Copyright issues related to the use of existing artworks in training AI models.
  * The need for clear regulations and guidelines governing the use and disclosure of AI-generated content.



### **Impact on Creative Industries**

The widespread adoption of AI image generators is having a significant impact on various creative sectors:

  * Advertising and marketing are seeing increased efficiency and creative possibilities.
  * The gaming industry is utilizing AI for rapid prototyping and content creation.
  * Fashion and e-commerce are exploring AI-generated product images and virtual try-on experiences.
  * The healthcare sector is investigating AI image generation for medical imaging and visualization.

to content ↑

## **Conclusion**

The AI image generation market is experiencing remarkable growth, with far-reaching implications across industries and society. As the technology continues to evolve, we can expect to see even more innovative applications and use cases emerge. However, addressing ethical concerns and establishing clear guidelines will be crucial to ensuring the responsible development and use of AI image generation tools. As we move forward, the balance between harnessing the creative potential of AI and preserving the value of human artistry will likely be a key focus for stakeholders in this rapidly expanding field.

Sources:

[Tech Report](https://techreport.com/statistics/software-web/ai-image-generator-market-statistics/)

[Fortune Business Insights](https://www.fortunebusinessinsights.com/ai-image-generator-market-108604)

[Journal Every Pixel](https://journal.everypixel.com/ai-image-statistics)

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title: The evolution of generative AI for the artist | TechTarget
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# The evolution of generative AI for the artist

## At the start of the hype era of image-generating tools like Stable Diffusion and Midjourney, many feared creatives would lose their jobs. Two years later, things are different.

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By

  * [Esther Shittu,](https://www.techtarget.com/contributor/Esther-Ajao) News Writer



Published: 12 Dec 2024

NEW YORK -- Over the last two years, views about the effect of generative AI on artists, creatives and designers have changed.

At the beginning of the generative AI hype cycle, the fear was that artists would lose their jobs, especially because [image-generating tools](https://www.techtarget.com/searchenterpriseai/definition/AI-art-artificial-intelligence-art) like OpenAI's Dall-E, Stable Diffusion and Midjourney made it easy for people unskilled in the arts to produce images.

"A lot of that fear that happened with designers and creatives was because executives were getting so excited about generative AI that they were putting all this stuff out there about [how] 'we're going to use AI to make all the games and replace people,'" said Mitu Khandaker, a game designer and associate arts professor at the NYU Game Center, during an interview at the AI Summit conference in New York City this week.

Since then, some artists' perspectives have changed.

## __Lost jobs

While some creatives and artists have found opportunities spawned by generative AI, others have been displaced by it.

"It would probably be irresponsible to say that artists haven't been affected by this, because they definitely have," said Kent Keirsey, founder and CEO at Invoke, a vendor with a generative AI platform for professional visual media studios, in an interview.

Artists who were commissioned to work on big game projects such as Dungeons and Dragons are no longer being sought for those types of jobs, Keirsey said. Freelancers and early-career artists have also seen work taken away by generative AI technology, he added.

The elimination of those jobs is because the AI generators are able to create products that those artists would have done, he said.

## __New level of excellence

On the other hand, generative AI tools have raised the skill level that is expected in many professional visual media settings, Keirsey said.

> It's going to be harder to compete because it means you have to be excellent to compete with these tools that are coming out.  **Kent Keirsey** Founder and CEO, Invoke  __

"It's going to be harder to compete because it means you have to be excellent to compete with these tools that are coming out," he said.

Helping artists and enterprises in the creative world achieve such a level of excellence is what Invoke seeks to do. While Invoke does not have its own large language models, it seeks to be an "AI-era Photoshop," Keirsey said, referring to the popular Adobe program.

The Invoke platform includes a studiolike interface that gives users tools for editing, developing color palettes and blending the images they generate with LLMs. It also uses an automation workflow that organizations can use for complex image-generation tasks.

While it operates in a market that is dominated by digital media and design vendor [Adobe and its Firefly model](https://www.techtarget.com/searchcustomerexperience/news/366555136/Adobe-unveils-Firefly-AI-models-to-quickly-make-vivid-images), Invoke differentiates itself by not training or storing customer data. Enterprises or individual artists can bring whatever model they feel comfortable with and work with it on the Invoke platform.

## __AI tools as an accelerant

The availability of platforms and vendors such as Invoke and Adobe reflects a shift in the generative AI market for artists and creatives.

"Most businesses are really looking at this as an accelerant rather than a replacement for their teams," Keirsey said. "We're not going to have the CEO generating all the art for our game or all the designs for our products."

Many individual artists have realized the need for generative AI as a tool to improve and speed up the creation of their products. Moreover, publishers are no longer interested in artists who rely solely on AI image tools instead of trying to use the tools as assistants to help create a work of art, Khandaker said.

"Now, if people see AI-generated art in pitch decks and things like that, if publishers see that, that's not necessarily a good thing anymore," she said.

Generally, purely generative AI-created images are not trusted. Instead, game developers and artists are moving toward using generative AI tools that assist in the creative process, but don't create the images themselves.

"We are hopefully moving toward this understanding that it is like co-creation with AI," Khandaker continued. "It's not necessarily getting AI to do the thing for you."

## __Game development

Using AI technology as an assistant is familiar to Khandaker, who uses an LLM from French vendor [Mistral AI](https://www.techtarget.com/searchenterpriseai/news/366614077/Mistrals-new-small-AI-models-target-phones-laptops) to develop a game. She said she is using the model to help develop the conversational aspect of a character in the game based on herself.

"I've been fine-tuning and training the model on me," she said. "There's a lot of conversation about 'oh yeah, we'll just put these AI-driven characters in games.' But then, to what end? Are people really going to enjoy talking to them? How do you actually still apply narrative design principles that craft a good story to these things?"

To help answer some of these questions, Khandaker has been performing play tests with other people, in which they have conversations to model what characters say or do in a game.

## __AI and filmmaking

The concept of generative AI tools as assistants speaks to the need for humanity in the arts and graphic design, and the interest of humans in each other's work.

"I just personally don't think we'll ever not be interested in what actors and humans do," said Cameron Kit, founder and CEO of Yoyos, a documentary storytelling company. "Generative AI is just going to continue to be a tool."

__Cameron Kit, founder and CEO of documentary storytelling company Yoyos, discusses generative AI and filmmaking at AI Summit.

__

As a filmmaker, Kit was at first skeptical about the usefulness of generative AI tools. However, she said she later became convinced after seeing what a friend was able to achieve with Midjourney.

She and her friend were also able to create a film for just $200 in a month. Usually, the same work would cost $2,000.

But using generative AI tools doesn't always equate to speed, Kit added.

"It's still not at the point where it's going to read your mind, so you still want to spend a ton of time with a prompt to make it look good," she said. Besides Midjourney, Kit uses the Luma AI and Runway video generators for her films.

## __Scaling enterprise projects

On a larger scale, some enterprises work with generative AI vendors to help reach a wider product audience.

For example, in September, Qatar Airways partnered with Akool, vendor of a generative AI platform for personalized video marketing and advertising, to launch an airline commercial in which viewers can become the stars of the film.

Akool uses OpenAI's GPT technology for its language model and another AI vendor for its voice model. However, it develops its video model itself.

The application of Akool's technology shows how generative AI systems can enable the humanization of digital media, said Marisol Tamaro, chief marketing officer at E. Gluck Corporation, a watch manufacturer based in New York, after watching the Qatar Airways commercial that used Akool's technology.

"The ability to change images and people, it's a great way to drive efficiency," Tamaro said. "Whether it's people or models or photography, you don't have to shoot 10 different people if you can apply technology to look like you're representing a diverse population."

"The technology has come such a long way that it looks so real," she added.

_Esther Shittu is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems._

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# AI Art Statistics: The Ultimate List in 2025

![Shalwa](https://secure.gravatar.com/avatar/a8074a74e4bcc5941be86bf1c032f133?s=96&d=mm&r=g)

by Shalwa

September, 8, 2024 

AI-generated art is reshaping the creative landscape, sparking debates about its legitimacy and impact on traditional artistry. From breathtaking digital paintings to cutting-edge NFTs, AI art is proving to be more than just a trend—it’s a revolution. In 2025, the growing prominence of AI-generated art is evidenced by its increasing visibility, widespread public awareness, and financial influence.

This article explores compelling AI art statistics, its adoption, its reception by the public and artists, and what the future holds for this technology.

### Important AI Art Statistics For 2025

  * \- 27% of Americans have seen AI-generated art, though many don’t recognize it.
  * \- 76% of people don’t believe AI-generated works should be called “art.”
  * \- 89% of artists fear current copyright laws are outdated for handling AI art.
  * \- The highest-valued piece of AI art, Portrait of Edmon Belamy, sold for $432,000.
  * \- 55% of artists believe AI will negatively impact their income.
  * \- Influencers worldwide named Canva as the leading AI image-generation tool (51.8%), followed by Photoshop (36.4%).
  * \- The generative AI market is projected to grow from $11 billion in 2020 to $128 billion in 2024 and more than $1.3 trillion by 2032.



to content ↑

Contents

  * **How Popular Is AI-Generated Art?**
  * **How Has AI Art Affected Artists?**
  * ****Will AI Art Replace cHuman Artists?****
  * What AI Art Means for Society
  * **Conclusion**



## **How Popular Is AI-Generated Art?**

[AI art](https://artsmart.ai/) is becoming increasingly mainstream, captivating audiences while sparking debates about its place in the art world. Despite being a relatively new phenomenon, AI-generated art has already begun to shape perceptions of creativity and innovation.

### **How Aware Is the Public of AI Art?**

AI art is gradually entering public consciousness, though its presence is not always recognized.

Let's see what recent statistics data we've gathered:

![Perception of AI Art  tables](https://lh7-rt.googleusercontent.com/docsz/AD_4nXdcuMwuQMj3CbpSFk7ZZXHKPwfa8P4VpreED-mcQDyh2cLOxqrC8-3lglzkhTK5_5lJ_wd5L-KHgIn51yhEqchemyKEzkpZ41U745SxbWj5qsOp0uUtS532bZw8G357D86og2fHuSaXtS4fMU1mep5VvBA?key=290MBTN7Lk_R8b2Uf2hFDw)

#### 1\. **27% of Americans say that they’ve seen at least one piece of AI-generated art.**

(Academy of Animated Art)

A survey found that only about a quarter of Americans are aware they've encountered AI-generated art, highlighting that AI art is still emerging into public consciousness. 

#### **2\. 56% of those who have seen AI-generated art say they enjoy it.**

(YouGov)

Among those who have encountered AI art, a majority find it enjoyable, suggesting that as exposure increases, so might appreciation for AI-generated works. 

#### 3\. **31% say that AI can create artwork that’s just as good as human artwork.**

(YouGov)

Nearly one-third of Americans believe that AI can match human artistic abilities, showing a shift towards accepting AI as a legitimate artistic medium. 

#### 4\. **31% view AI as a major advancement in visual arts.**

(Pew Research)

This statistic highlights the growing belief that AI art is a significant innovation, although it's still seen as less impactful compared to fields like medicine or weather forecasting. 

### **What Is the Public’s Perception of AI Art?**

Public sentiment toward AI art remains mixed, with debates about its authenticity and ethical implications.

#### 5\. **76% don’t believe that AI-generated art should be called art.**

(Academy of Animated Art)

A significant number of people are hesitant to recognize AI creations as true art, often [feeling that AI lacks the soul and intention of human creativity](https://artsmart.ai/blog/why-ai-art-is-bad/).

#### 6\. **53% of Americans worry that AI-generated images will spread fake news.**

(Academy of Animated Art)

Over half of Americans express concern that AI-generated visuals could be used to mislead the public, underscoring fears about misinformation. 

#### 7\. **54% can tell the difference between AI-generated images and those done by humans.**

(Academy of Animated Art)

Despite AI's advancements, over half of the viewers can still differentiate between art created by humans and machines. 

A separate survey in the United States (April 2024) found **61%** of respondents believe AI-created images should **not** be considered art because they are not made by humans.

to content ↑

## **How Has AI Art Affected Artists?**

AI’s rise in the art world has introduced both opportunities and challenges for artists. While some see it as a tool for creative exploration, others are concerned about ethical, [legal](https://artsmart.ai/blog/is-it-legal-to-sell-ai-generated-art/https://artsmart.ai/blog/is-it-legal-to-sell-ai-generated-art/) and financial implications.

### **What Ethical Concerns Do Artists Have About AI Art?**

The ethics of AI art are a significant source of contention, especially regarding its use of existing works without permission.

#### 8\. **74% of artists say AI artwork is unethical.**

(YouGov)

Many artists feel that AI art crosses ethical boundaries, especially when it uses existing artworks without permission or credit. 

#### 9\. **89% of artists worry that copyright laws are outdated.**

(Academy of Animated Art)

As AI evolves, artists fear that [current copyright protections won’t adequately defend](https://artsmart.ai/blog/is-ai-art-copyrighted/) their work against unauthorized AI use, leaving them vulnerable. 

### ****How Are Artists Using AI as a Creative Tool?****

Despite ethical concerns, many artists are integrating AI into their creative processes, using it as a tool to explore new ideas and styles.

![AI art creative tools charts](https://lh7-rt.googleusercontent.com/docsz/AD_4nXfe6NYi5hXjT1-W0qr6UvhKcJrJDjDBeKRvxcpYQOrrVkzYqRGTuiRTrcuPPzUxrcBBZGSdEUK-xLcrQjxaxLRuESrPnyp7y0q5a09B8oW3GOS32OBH3HpzHLigemxlo-Eqti_Ew7sw7f3zYgC6uyQEHAC0?key=290MBTN7Lk_R8b2Uf2hFDw)

#### 10\. **65% of artists have used text-to-image AI to brainstorm new ideas.**

(Academy of Animated Art)

A significant number of artists are using AI to jump-start their creative process, blending traditional artistry with digital innovation.

#### 11\. **36% of artists say AI accurately reflects their artistry.**

(Academy of Animated Art)

Over a third of artists believe AI can capture their artistic vision, seeing it as a complement rather than a competitor. 

#### 12\. **Nearly 2 in 5 artists worry they’ll infringe on others’ art if they use AI.**

(Academy of Animated Art)

Many artists express concerns about unintentional copyright infringement when using AI tools that might replicate existing works.

#### **13\. 28% are concerned about the originality of AI-generated artworks.**

(YouGov)

Just over a quarter of artists worry that AI art may lack originality and be too similar to other generated pieces. 

#### 14\. **73% of artists say they want to be asked for permission before their artwork is used to train algorithms.**

(Academy of Animated Art)

With many AI models trained on publicly available artworks, most artists demand greater control and compensation when their work is used. 

to content ↑

## ****Will AI Art Replace cHuman Artists?****

The potential for AI to rival human artists raises critical questions about the future of art. While AI can produce impressive works, debates continue over whether it will ever truly replace human creativity.

### ****How Has AI Art Performed Financially?****

AI art has already made significant financial inroads, setting records and sparking intrigue in traditional art markets.

#### 15\. **The highest-valued piece of AI art ever sold traditionally was bought for $432,000.**

(Academy of Animated Art)

The Portrait of Edmon Belamy’s record-setting sale at Christie’s showcases AI’s potential to disrupt the traditional art world. 

#### 16\. **The highest-valued AI-generated NFTs sold for $1.1 million.**

(CNN)

AI-generated NFTs, like those created by Botto, are fetching staggering prices, highlighting the growing market for digital and AI-generated art.

#### 17\. **On OpenSea, AI NFTs range in price from $244 to $1,631.**

(Academy of Animated Art)

While some AI-generated NFTs have sold for millions, the average prices on platforms like OpenSea are much more varied, making them accessible to a broader audience. 

### ****How Does AI Affect Artists’ Income?****

Many artists fear that AI’s ability to create art quickly and cheaply could undermine their financial stability.

#### 18\. **55% of artists think that AI will negatively impact their ability to generate income.**

(Academy of Animated Art)

Over half of artists worry that AI will drive down demand for original artworks as clients opt for cheaper, AI-generated alternatives. Popular AI Art Generators

to content ↑

## What AI Art Means for Society

AI-generated art doesn’t exist in a vacuum; it has the potential to reshape social norms, cultural values, and even the economy at large. Here are some broader insights on AI art’s societal impact:

#### 19\. Younger vs. Older Generations

In an April 2024 U.S. survey, 48% of Millennials said AI-created images or videos should be considered art, even though they’re not made by humans, while 52% disagreed.  
In contrast, 61% of the general U.S. adult population said AI-generated images should not be considered art.  
(Statista, 2024)

#### 20\. Ethical and Social Implications

AI systems can replicate and amplify human biases, leading to social inequalities.  
Concerns about misinformation and deepfakes continue to grow; nearly 70% of global respondents say they are concerned about deepfakes.  
(Statista, 2024)

#### 21\. Growth of the Generative AI Market

The global generative AI market is forecast to surpass $1.3 trillion by 2032, reflecting explosive growth in sectors from entertainment to marketing.  
In the United States alone, generative AI is projected to reach $20.29 billion by 2025.  
(Statista, 2024)

#### 22\. Influencer and Marketing Usage

51.8% of influencers worldwide report using Canva’s AI features, while 36.4% use Photoshop’s AI for image generation.  
67.1% of influencers who use AI said they disclose it to their followers, sparking discussions about transparency in social media content.  
(Statista, 2024)

#### 23\. Industry Adoption Variances

Usage of AI image-generation tools varies widely by region and industry. For instance, over 15% of respondents in Spain used OpenAI’s DALL-E, while only 7% used Midjourney.  
In Norway, the information and communication sector leads in AI-driven image recognition and processing (8%), with other industries lagging behind.  
(Statista, 2024)

#### 24\. Employment Concerns and Equity

AI’s capacity to automate tasks may displace workers, heightening concerns about job security, particularly in creative fields.  
Growing inequalities could result as advanced economies reap more benefits from AI adoption than developing regions.

As AI art continues to mature, it forces society to confront questions about originality, authenticity, and the essence of creativity.

to content ↑

## **Conclusion**

AI-generated art is reshaping the landscape of visual creativity, blending technology with traditional artistry in new and sometimes controversial ways. While AI brings exciting new possibilities, it also raises critical ethical and economic questions that the art world will need to address. Whether AI will ever fully replace human artists remains uncertain, but one thing is clear: AI art is here to stay, redefining the possibilities of what art can be.

Sources:

  * [Statista](https://www.statista.com/)
  * Pew Research
  * [Academy of Animated Art](https://academyofanimatedart.com/ai-art-statistics/)
  * [AI Plus Info](https://www.aiplusinfo.com/blog/most-expensive-piece-of-ai-art/#:~:text=The%20most%20expensive%20piece%20of%20AI%20art%20is%20the%20portrait,house%20in%20New%20York%20City)
  * [CNN](https://edition.cnn.com/2023/04/02/tech/ai-generated-images-social-media/index.html)
  * [YouGov](https://business.yougov.com/content/45765-half-americans-think-ai-art-will-be-used-spread-fa)



_Note: All statistics and survey data have been drawn from sources listed above. Publication dates range from 2020 to 2024._

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# New Report Confirms Worst Fears: AI Will Disrupt Countless Animation Jobs Over Next 3 Years

By [Amid Amidi](https://www.cartoonbrew.com/author/amid "Amid Amidi") | 01/31/2024 1:47 pm | [Be the First to Comment!](https://www.cartoonbrew.com/artist-rights/union-study-says-generative-ai-will-disrupt-204000-jobs-three-years-237495.html#cmnt)

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There is little doubt that the emergence of generative artificial intelligence models will massively disrupt the future of the entertainment industry. This week, a new report outlined just how devastating the impact of Generative AI (GenAI) could be to artists over the next three years.

The report (downloadable [here as a PDF](https://animationguild.org/wp-content/uploads/2024/01/Future-Unscripted-The-Impact-of-Generative-Artificial-Intelligence-on-Entertainment-Industry-Jobs-pages.pdf)) warns that GenAI “signifies a large-scale transition from existing techniques into new processes” and it will “likely rebalance the demand for labor and capital across the entertainment industries.”

For creative workers, this means they will be “facing an era of disruption, defined by the consolidation of some job roles, the replacement of existing job roles with new ones, and the elimination of many jobs entirely.” 

The survey was conducted by consulting firm CVL Economics and co-commissioned by The Animation Guild IATSE Local 839, the Concept Art Association, the Human Artistry Campaign, and the National Cartoonists Society Foundation. It polled 300 bosses from six entertainment industries, including C-suite executives, senior executives, and mid-level managers. It was conducted between November 17 and December 22, 2023.

##### **GenAI Is Not A Future Technology – It Is Here Today**

Easily the most alarming takeaway from the study: GenAI is not a hypothetical technology that could impact film, gaming, and vfx artists at some distant date in the future. It is available **right now** and it is upending the entertainment industry **today**. In fact, two-thirds of the 300 business leaders who were surveyed expect GenAI to play a role in consolidating or replacing existing job titles in the coming three years (2024-2026).

An estimated 204,000 entertainment industry jobs will be significantly disrupted by generative AI _**over the next three years**_. This figure doesn’t include freelancer and contract workers, so the actual number of disrupted jobs is likely to be even greater than the scope of the survey. (The report considers a job disrupted when a sufficient number of tasks are either “consolidated, replaced, or eliminated by GenAI.”)

[![GenAI and entertainment industry impact study](https://www.cartoonbrew.com/wp-content/uploads/2024/01/ai_study_04.jpg)](https://www.cartoonbrew.com/wp-content/uploads/2024/01/ai_study_04.jpg)

Of the 204,000 affected jobs, 118,500 of them are in the film, television, and animation industries, which represents 21.4% of the 555,000 jobs in the three areas. An additional 52,400 disrupted jobs are in the gaming industry, representing 13.4% of the 390,500 employed in the sector. 

The most affected state is California, the hub of the American entertainment industry, which will see 62,000 creative jobs impacted, followed by New York (26,000 jobs) and Georgia (7,800 jobs).

##### **How Open Are Entertainment Companies To Using AI?**

Entertainment executives are literally frothing at the mouth to start implementing GenAI into their pipelines. Ninety-nine per cent of the people who took the survey said they plan to implement AI in the next three years. In fact, a quarter of companies surveyed indicated that they already had one or more GenAI program(s) in place, while 15% said they had concerns about GenAI programs and would only implement once those issues were resolved. 

One percent of the survey takers said they weren’t planning to use GenAI within the next three years. 

##### **Who’ll Be Affected First?**

Certain jobs in the animation and vfx industries will be more impacted than others. For example, among the one-quarter of companies that have already implemented GenAI programs, 44% of them are using the tech to assist in generating 3d models while 39% are generating character and environment design tasks. 

[![GenAI and entertainment industry impact study](https://www.cartoonbrew.com/wp-content/uploads/2024/01/ai_study_02.jpg)](https://www.cartoonbrew.com/wp-content/uploads/2024/01/ai_study_02.jpg)

Further, 33% of the survey takers predict that 3d modelers will be affected in the next three years, while 25% believed that compositors were vulnerable over the same time period. Only 15% said that storyboarders, animators, illustrators, and look/surface/material artists woulds experience job displacement by 2026. 

Equally revealing is how entertainment firms are planning to use GenAI. Nearly half of respondents (47%) expect to use it for developing 3D assets, while 38% will use it for 2d concept art and storyboards. Thirty-five percent want to use it for creating animated characters (synthetic actors, in their terminology) for film/tv, while 31% want to use it for writing scripts. 

[![GenAI and entertainment industry impact study](https://www.cartoonbrew.com/wp-content/uploads/2024/01/ai_study_03.jpg)](https://www.cartoonbrew.com/wp-content/uploads/2024/01/ai_study_03.jpg)

##### **More Takeaways And Conclusion**

For its part, The Animation Guild issued a list of key findings from the report that included some eyebrow-raising responses:

  * Three-fourths of respondents said that GenAI tools, software, and/or models have already supported the elimination, reduction, or consolidation of jobs in their business division.
  * Over 90% of respondents believe GenAI will play a larger role in the entertainment industry, though only 26% of surveytakers felt that their company was fully prepared for the integration of GenAI into their workflows.



The report ends with this suggestion for entertainment industry decision-makers:

> The future is not yet written, and it needn’t be generated by AI. It is important to remember that GenAI output is constrained by its inputs. If the responsibility to generate content shifts away from humans to machines, which can currently only formulate output based on previously created content, the availability and uniqueness of new content brought into the world will become more limited. It is critical that those in leadership positions, especially in entertainment industries, keep this top of mind and ideate on ways that new technologies can expand human creativity, not replace it

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Read More: [Artificial intelligence](https://www.cartoonbrew.com/tag/artificial-intelligence)   [Concept Art Association](https://www.cartoonbrew.com/tag/concept-art-association)   [Human Artistry Campaign](https://www.cartoonbrew.com/tag/human-artistry-campaign)   [National Cartoonists Society Foundation](https://www.cartoonbrew.com/tag/national-cartoonists-society-foundation)   [The Animation Guild](https://www.cartoonbrew.com/tag/the-animation-guild)

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url: https://magai.co/generative-ai-has-transformed-creative-work/
title: Generative AI in Creative Work: Shocking Study • Magai
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# How Generative AI Has Transformed Creative Work: A Comprehensive Study

Written by:

Dustin W. Stout

![It features artists or professionals engaged in creative work enhanced by AI technology.](https://magai.co/wp-content/uploads/2025/01/magai-featured-photo-on-grenerative-ai-in-creative-work-scaled.jpg)

Creative professionals are rapidly embracing generative AI, a technology that is redefining the boundaries of creative work, including AI-enhanced creativity and innovative AI-creative intelligence techniques. Recent studies reveal that 83% of creative professionals have incorporated AI tools into their processes, revolutionizing not just efficiency, but also expanding creative potential. This adoption signals a pivotal transformation in the creative landscape where human ingenuity meets technological brilliance.

The utilization of AI in creative fields goes beyond mere automation. It empowers artists, writers, and musicians to break free from traditional constraints, explore uncharted territories, and achieve unprecedented productivity. However, this technological shift raises intriguing questions about maintaining the authenticity and spirit of human creativity amidst these advancements.

The impact of this transformation extends beyond individual creators. According to [MIT Sloan Management Review](https://sloanreview.mit.edu/article/when-generative-ai-meets-product-development/), organizations implementing generative AI in their creative processes have seen remarkable improvements in both efficiency and creative output, with some creators experiencing up to 26% improvement in their creative capabilities. This enhancement is particularly pronounced among professionals who previously struggled with creative blocks or technical limitations.

As creative professionals continue to integrate generative AI into their workflows, we can observe notable trends and statistics that highlight its transformative impact.

![Infographic showing AI adoption metrics in creative industries with two bar charts. Top chart displays current adoption rates: 83% professional adoption, 26% AI-augmented tasks, and 62% faster task completion. Bottom chart shows quality improvements: 66% better content quality, 26% creative capabilities improvement, and 70% faster asset variation speed. Magai logo included.](https://magai.co/wp-content/uploads/2024/12/AD_4nXcDgTjRYJALMSut6VRr3fqLT-EMMp39kgKeQSkElIax2dGY4Sqyv7ym3uxxpOn-qpaLm8knZn4kR7TODAmt2gb8KRMebIV1IlDkvx8o3m5KZ2xlF47og9SoVm1ZtAi4PwL08eegkeyRfkejzIXv-T-NNPLpLxI1oIY.jpg)

## **The Current State of AI Adoption in Creative Industries**

The creative industry's embrace of generative AI has been remarkably swift and comprehensive. Beyond the headline figure of 83% professional adoption, we're seeing a deeper integration across various creative disciplines. According to recent industry analysis from PYMNTS, this transformation is reshaping everything from graphic design to content creation, with up to 26% of creative tasks now augmented by AI assistance.

This adoption isn't merely about replacing existing tools; it's about expanding creative possibilities. Creative professionals are discovering that AI can serve as a collaborative partner, offering new perspectives and accelerating the ideation process. The technology is particularly valuable in eliminating repetitive tasks, allowing creators to focus on higher-value creative decisions.

Exploring deeper into the creative industries, generative AI is being utilized in diverse and innovative ways across different creative fields.

### **Common Applications Across Creative Fields**

The implementation of generative AI varies significantly across creative disciplines, each finding unique ways to leverage the technology. Visual artists are using AI for initial concept generation and style exploration, while [writers employ it](https://magai.co/chatgpt-writing-process-steps/) for research synthesis and content structuring. In the music industry, composers are discovering new melodic possibilities and sound combinations previously unexplored.

What's particularly noteworthy is how these applications are evolving beyond simple automation. Creative professionals are developing sophisticated workflows that combine AI capabilities with human expertise. This hybrid approach is proving especially effective in areas like:

  * Rapid prototyping of design concepts
  * Content personalization at scale
  * Complex pattern recognition in creative works
  * Automated asset variation generation



The integration of AI tools has also led to the emergence of new creative methodologies. Rather than replacing human creativity, these tools are enabling creators to explore broader creative possibilities while maintaining their unique artistic vision. This shift represents a significant evolution in how creative work is approached, moving from a purely human-driven process to a collaborative partnership between human creativity and artificial intelligence.

Learn more about [AI's impact on various creative mediums](https://magai.co/ai-and-the-future-of-art-music-and-film/).

## **Transforming Creative Efficiency and Productivity**

The impact of generative AI on creative productivity isn't just anecdotal - it's quantifiable. According to recent findings from Adobe's creative professional survey, 62% of creative professionals report reducing their task completion time by approximately 20%. This efficiency gain translates to nearly one full workday saved per week, allowing creators to focus on higher-value creative activities.

These productivity improvements manifest across various aspects of creative work:

**Creative Task**| **Efficiency Improvement**| **Impact on Workflow**  
---|---|---  
Initial Concept Generation| 40-50% faster| More time for refinement  
Asset Variations| 60-70% faster| Increased client options  
Research & Reference| 30-40% faster| Better-informed creativity  
  
Beyond raw speed improvements, creative professionals are experiencing enhanced creative output quality. The time saved through automation of routine tasks allows for deeper exploration of creative concepts and more thorough refinement of final deliverables.

### **Workflow Integration and Optimization**

The true power of generative AI in creative workflows lies in its ability to remove creative blocks and streamline iterative processes. Research from StreamLife highlights how tools like Google's Magenta project are helping artists overcome creative blocks by providing novel starting points and unexpected combinations that spark new ideas.

Successful workflow integration typically follows this pattern:

  1. **Initial Ideation:** AI generates multiple concept variations based on initial parameters
  2. **Human Curation:** Creative professionals select and refine the most promising options
  3. **AI Enhancement:** Selected concepts are further developed using AI tools
  4. **Professional Refinement:** Final human touches ensure authenticity and brand alignment



This hybrid approach maintains creative authenticity while maximizing efficiency. Creative professionals are finding that AI tools serve as powerful collaborators rather than replacements, augmenting human creativity with computational efficiency.

The optimization of creative workflows through AI has led to an interesting phenomenon: increased experimentation and risk-taking. With the safety net of rapid iteration and easy modifications, creators are more willing to explore bold new directions in their work. Explore more about [AI-enhanced content creation](https://magai.co/ai-content-creation-the-future-of-digital-storytelling/) workflows.

![a high tech robot working on creative work improvement](https://magai.co/wp-content/uploads/2025/01/magai-genrative-ai-creative-work-1-1024x572.jpg)

## **Impact on Creative Output Quality and Volume**

The question of quality in AI-enhanced creative work has moved beyond theoretical debate to measurable outcomes. According to Adobe's comprehensive study, 66% of creative professionals report producing better content with generative AI tools. This improvement isn't just about technical perfection – it reflects a deeper enhancement in creative expression and conceptual sophistication.

Quality improvements are manifesting across several key dimensions:

**Quality Aspect**| **Improvement Area**| **Impact on Final Output**  
---|---|---  
Conceptual Depth| Enhanced exploration of ideas| More nuanced creative solutions  
Technical Execution| Refined detail management| Higher production values  
Creative Variation| Broader style exploration| More diverse creative options  
  
These improvements stem from AI's ability to handle technical complexities while allowing creators to focus on strategic creative decisions. The technology serves as both a quality control mechanism and a creative catalyst, enabling professionals to maintain consistently high standards across larger volumes of work.

### **Scaling Creative Production** with Generative AI

The ability to scale creative production without sacrificing quality represents one of generative AI's most significant impacts. Research from 360info suggests we're entering a new creative renaissance, where increased production capacity is leading to greater experimentation and innovation rather than mere volume.

Creative professionals are achieving this scale through:

  * **Parallel Processing:** Simultaneously developing multiple creative directions
  * **Rapid Iteration:** Quick refinement of concepts based on feedback
  * **Consistent Quality:** Maintaining standards across increased output
  * **Resource Optimization:** Better allocation of creative energy



This scaling capability isn't just about producing more – it's about producing better work more consistently. Creative professionals are finding they can maintain their high standards across larger projects and more diverse deliverables, leading to expanded creative opportunities and market reach.

The increased production capacity has also democratized access to high-quality creative work, allowing smaller organizations and individual creators to compete at higher levels. This democratization is fostering a more diverse creative ecosystem where innovation can come from any direction. Discover more about [AI's role in expanding creative possibilities](https://magai.co/generative-ai-for-art-creation/).

## **Navigating Professional Challenges and Opportunities**

The creative industry is experiencing a fundamental transformation in how roles are defined and executed. According to McKinsey's analysis, approximately 12 million workers may need to transition to new roles by 2030 due to AI integration. However, this shift isn't simply about job displacement – it's about role evolution and the emergence of new creative positions.

This transformation is creating several new professional categories:

**Emerging Role**| **Primary Responsibilities**| **Growth Potential**  
---|---|---  
AI-Creative Director| Orchestrating AI tools with human creativity| High  
Creative Prompt Engineer| Optimizing AI interactions for creative output| Very High  
Creative Technology Integrator| Bridging traditional and AI-powered workflows| Moderate  
  
Rather than replacing creative professionals, AI is elevating their roles to focus on higher-level creative strategy and unique human insights. This evolution requires a blend of traditional creative skills with new technological competencies.

![three high tech robots develop strategic thinking for creative work](https://magai.co/wp-content/uploads/2025/01/magai-strategic-thinking-1024x572.jpg)

### **Required Skills in the AI Era**

The integration of AI in creative work has introduced a new skills paradigm. Research indicates that up to 26% of creative sector tasks could be automated, necessitating a shift in professional skill sets. This transformation demands a hybrid skill set that combines creative expertise with technical proficiency.

Essential skills for the AI-enhanced creative professional include:

  * **Strategic Thinking:** Ability to guide AI tools toward meaningful creative outcomes
  * **Technical Fluency:** Understanding of AI capabilities and limitations
  * **Creative Direction:** Expertise in refining and enhancing AI-generated content
  * **Quality Control:** Skill in evaluating and ensuring creative excellence



The most successful creative professionals are those who view AI as a collaborative tool rather than a replacement technology. They're developing what might be called "AI-creative intelligence" – the ability to leverage AI capabilities while maintaining human creative vision and authenticity.

Professional development in this new landscape requires continuous learning and adaptation. Creative professionals are finding success by:

  1. Developing expertise in AI tool selection and implementation
  2. Building skills in prompt engineering and AI direction
  3. Maintaining core creative competencies while adding technical skills
  4. Focusing on uniquely human creative abilities



Learn more about adapting to [AI-enhanced productivity tools](https://magai.co/ai-assistants-are-revolutionizing-productivity/)

## **Ethical Considerations and Best Practices**

The rapid adoption of generative AI in creative industries has sparked urgent discussions about intellectual property rights and fair compensation. Creative professionals are increasingly concerned about the unauthorized use of their work in training AI models, leading to a growing movement for stronger legal protections and clearer guidelines.

Key legal considerations in the AI-creative landscape include:

**Legal Aspect**| **Current Challenge**| **Emerging Solutions**  
---|---|---  
Copyright Protection| Unclear boundaries in AI training data| Opt-out registries and consent frameworks  
Attribution Rights| Difficulty tracking AI contributions| Blockchain-based provenance tracking  
Fair Compensation| Value attribution for training data| Licensing models for AI training  
  
The creative community is actively working to establish new frameworks that protect creators' rights while fostering innovation. This includes the development of opt-out mechanisms allowing creators to exclude their work from AI training datasets and the creation of fair compensation models for work used in AI development.

### **Ethical Implementation Guidelines**

Beyond legal considerations, the ethical implementation of AI in creative work requires careful attention to authenticity, transparency, and fair practice. Creative professionals and organizations are developing best practices to ensure responsible AI integration:

  * **Transparency Requirements:**
    * Clear disclosure of AI use in creative works
    * Documentation of AI contribution levels
    * Attribution of original sources when applicable
  * **Quality Standards:**
    * Regular human review of AI-generated content
    * Maintenance of brand authenticity
    * Quality control processes for AI outputs



Organizations implementing AI in their creative workflows should consider adopting these ethical guidelines:

  1. Establish clear policies for AI tool usage and content attribution
  2. Implement robust review processes for AI-generated content
  3. Maintain transparency with clients and audiences about AI involvement
  4. Ensure fair compensation for human creators
  5. Respect intellectual property rights in AI training and implementation



The development of ethical frameworks isn't just about compliance – it's about building sustainable creative practices that benefit both creators and consumers. By establishing clear guidelines and best practices, the creative industry can harness AI's potential while preserving the value of human creativity. Check out more about [ethical AI content creation](https://magai.co/ai-content-creation-the-future-of-digital-storytelling/).

![a high tech robot working on a glass desk in a glowing pink room](https://magai.co/wp-content/uploads/2025/01/magai-future-of-creative-work-1024x572.jpg)

## **The Future of Creative Work with Generative AI**

The creative industry stands at the threshold of unprecedented technological advancement. As AI tools become more sophisticated, we're seeing the emergence of new creative possibilities that blend human intuition with computational power. This evolution isn't just about better tools – it's about fundamentally new ways of approaching creative work.

Key technological trends shaping the future include:

**Technology Trend**| **Creative Impact**| **Timeline**  
---|---|---  
Multimodal AI| Cross-medium creative synthesis| Near-term  
Adaptive AI| Context-aware creative assistance| Mid-term  
Collaborative AI| Enhanced human-AI creative partnership| Ongoing  
  
These advancements are enabling creative professionals to push boundaries in ways previously unimaginable, while maintaining the essential human elements that make creative work meaningful and impactful.

### **Future Collaboration Models**

The future of creative work lies in the development of sophisticated collaboration models between human creators and AI systems. This partnership approach is already yielding promising results in various creative fields:

  * **Adaptive Workflows:**
    * Dynamic response to creative needs
    * Personalized tool optimization
    * Real-time creative assistance
  * **Enhanced Creative Processes:**
    * Automated technical optimization
    * Expanded creative exploration
    * Rapid prototyping and iteration



The evolution of these collaboration models suggests a future where AI serves as an extension of human creativity rather than a replacement. This symbiotic relationship will likely lead to new forms of creative expression and professional opportunities. [Discover more about the future of AI in creative industries](https://magai.co/ai-and-the-future-of-art-music-and-film/)

## **Conclusion**

The transformation of creative work through generative AI represents both a challenge and an opportunity for the creative industry. As we've explored throughout this analysis, the impact is multifaceted, affecting everything from daily workflows to fundamental questions about the nature of creativity itself.

Key takeaways from our exploration include:

  * AI integration is enhancing rather than replacing human creativity
  * New professional roles and skills are emerging in response to technological changes
  * Ethical considerations and best practices are crucial for sustainable implementation
  * The future lies in collaborative models that combine human and artificial intelligence



As we look to the future, the success of creative professionals will increasingly depend on their ability to harness AI tools while maintaining their unique creative vision. The key is not to resist this transformation but to shape it in ways that enhance human creativity and maintain the authenticity of creative expression.

## **Frequently Asked Questions**

### **How is generative AI affecting creative jobs?**

While some creative tasks are being automated, generative AI is primarily transforming rather than eliminating creative roles. The technology is creating new opportunities for professionals who can effectively combine creative expertise with AI tool proficiency.

### **What are the main benefits of using AI in creative work?**

Key benefits include increased productivity (with professionals saving up to 11 hours per week), enhanced creative exploration capabilities, and the ability to scale creative production while maintaining quality standards.

### **How can creative professionals prepare for AI integration?**

Professionals should focus on developing hybrid skill sets that combine traditional creative expertise with technical proficiency in AI tools. This includes understanding prompt engineering, quality control, and strategic implementation of AI in creative workflows.

### **What are the primary concerns about AI in creative industries?**

Major concerns include copyright protection, fair compensation for creative work used in AI training, maintaining authentic human creativity, and ensuring ethical implementation of AI tools.

### **How can businesses balance AI efficiency with creative authenticity?**

Businesses should establish clear guidelines for AI usage, maintain robust human oversight, ensure transparency in AI implementation, and focus on using AI to enhance rather than replace human creative decision-making.

Jan 23, 2025 2:36 PM

Category: [Generative AI](https://magai.co/generative-ai/)

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url: http://arxiv.org/abs/2501.02222v1
title: [2501.02222v1] A Toffoli Gate Decomposition via Echoed Cross-Resonance Gates
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# Quantum Physics

**arXiv:2501.02222v1** (quant-ph) 

[Submitted on 4 Jan 2025]

# Title:A Toffoli Gate Decomposition via Echoed Cross-Resonance Gates

Authors:[M. AbuGhanem](https://arxiv.org/search/quant-ph?searchtype=author&query=AbuGhanem,+M)

View a PDF of the paper titled A Toffoli Gate Decomposition via Echoed Cross-Resonance Gates, by M. AbuGhanem

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> Abstract:Quantum computing has garnered significant interest for its potential to solve certain computational problems much faster than the best-known classical algorithms. A fully functional and scalable quantum computer could transform various fields such as scientific research, material science, chemistry, and drug discovery. However, in the noisy intermediate-scale quantum (NISQ) era, quantum hardware faces challenges including decoherence, gate infidelity, and restricted qubit connectivity. Efficient implementation of multi-qubit gates is critical to advancing quantum computing, especially given the constraints of near-term quantum hardware, such as the absence of all-to-all qubit connectivity. Among these gates, the Toffoli gate (or CCNOT gate) plays a pivotal role in a wide range of quantum algorithms and error correction schemes. While various decomposition strategies have been proposed, they often assume idealized all-to-all connectivity, which is not available on most NISQ hardware. This paper introduces a novel decomposition of the Toffoli gate using Echoed Cross-Resonance (ECR) gates, a native operation for many superconducting qubit architectures, including IBM Quantum's hardware. By leveraging the inherent compatibility of ECR gates with superconducting qubit technology, this approach is intended to facilitate the implementation of the Toffoli gate, potentially reducing circuit depth and enhancing the efficiency of quantum circuits implementations on near-term quantum hardware. 

Subjects: |  Quantum Physics (quant-ph)  
---|---  
Cite as: | [arXiv:2501.02222](https://arxiv.org/abs/2501.02222) [quant-ph]  
  | (or  [arXiv:2501.02222v1](https://arxiv.org/abs/2501.02222v1) [quant-ph] for this version)   
  |  <https://doi.org/10.48550/arXiv.2501.02222> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: M AbuGhanem [[view email](/show-email/bdf22d53/2501.02222)]   
**[v1]** Sat, 4 Jan 2025 07:55:32 UTC (481 KB)  


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url: http://arxiv.org/abs/2111.15352v5
title: [2111.15352v5] Understanding Quantum Technologies 2024
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# Quantum Physics

**arXiv:2111.15352v5** (quant-ph) 

[Submitted on 24 Nov 2021 ([v1](https://arxiv.org/abs/2111.15352v1)), last revised 6 Nov 2024 (this version, v5)]

# Title:Understanding Quantum Technologies 2024

Authors:[Olivier Ezratty](https://arxiv.org/search/quant-ph?searchtype=author&query=Ezratty,+O)

View a PDF of the paper titled Understanding Quantum Technologies 2024, by Olivier Ezratty

[View PDF](/pdf/2111.15352v5)

> Abstract:Understanding Quantum Technologies 2024 is the 7th update of a creative-commons ebook that provides a 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs and manufacturing process, raw materials), unconventional computing (potential alternatives to quantum and classical computing), quantum computing algorithms, software development tools, resource estimate and benchmark tools, and application use cases, quantum communications and cryptography (including QKD, PQC and QPU interconnect technologies), quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an update to the 2023, 2022, and 2021 editions published respectively in October 2023, 2022 and 2021. An update log is provided at the end of the book. 

Comments: | 1,554 pages, 1,064 illustrations, >5,600 references, >750 quantum technologies vendors mentioned, Letter format, version 7.1 - November 6th, 2024  
---|---  
Subjects: |  Quantum Physics (quant-ph)  
Cite as: | [arXiv:2111.15352](https://arxiv.org/abs/2111.15352) [quant-ph]  
  | (or  [arXiv:2111.15352v5](https://arxiv.org/abs/2111.15352v5) [quant-ph] for this version)   
  |  <https://doi.org/10.48550/arXiv.2111.15352> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Olivier Ezratty [[view email](/show-email/558c161d/2111.15352)]   
**[[v1]](/abs/2111.15352v1)** Wed, 24 Nov 2021 19:59:08 UTC (75,264 KB)  
**[[v2]](/abs/2111.15352v2)** Wed, 1 Dec 2021 22:55:49 UTC (75,156 KB)  
**[[v3]](/abs/2111.15352v3)** Thu, 27 Oct 2022 20:25:08 UTC (89,529 KB)  
**[[v4]](/abs/2111.15352v4)** Sun, 12 Nov 2023 13:55:39 UTC (105,577 KB)  
**[v5]** Wed, 6 Nov 2024 19:19:41 UTC (115,892 KB)  


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# Breakthroughs in Quantum Computing

## Article #2 of Engineering the Quantum Future Series: Recent breakthroughs in quantum computing made substantial strides, driving closer to practical applications that could revolutionize industries by solving complex computational problems more efficiently.


[Ravi Rao](/profile/ravi.rao)

19 Aug, 2024. 13 min read

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This is the second article in a six-part series featuring articles on "[Engineering the Quantum Future](https://www.wevolver.com/article/engineering-the-quantum-future/)". The series explains the revolutionary advancements in quantum computing and their implications for various industries. Each article discusses a specific aspect of this transformative technology, from the fundamental concepts of quantum computing to the practical applications and challenges. This series is sponsored by Mouser Electronics. Through the sponsorship, Mouser Electronics promotes innovation and the exchange of knowledge, aiming to harness the revolutionary capabilities of quantum computing for a smarter and safer technological future.

## Introduction

2023 was a landmark year for quantum computing, with innovative breakthroughs promising to reshape our technological landscape and revolutionize how we solve complex problems and process information. From IBM's groundbreaking quantum chips to pioneering experiments in manipulating spin waves, the simulation of molecular structures, and quantum communication, the landscape of quantum computing is undergoing a transformation that will redefine our technological future.[1] 

With quantum computing transitioning from theoretical exploration to practical implementation, the time to delve into its developments is now. Though the journey is not without hurdles, we see a clear trend in collaborative efforts by scientists, engineers, and industry leaders, paving the way for a future where quantum computing plays a central role in our lives, from pharmaceuticals to telecommunications.

## IBM's Quantum Computing Milestone

From the broad landscape of quantum computing breakthroughs in the past year, IBM's recent advancements stand out as a testament to the rapid evolution and growing practicality of quantum technologies. IBM has ushered in a new era of quantum utility, marked by significant milestones and innovations that redefine the capabilities and applications of quantum computing.

IBM's recent unveiling of the Condor processor represented a monumental leap in quantum computing with its 1,121 superconducting qubits, shattering the 1,000-qubit barrier and showcasing IBM's cross-resonance gate technology by pushing the limits of scale, yield, and design in quantum chip manufacturing.[2] The Condor processor, with its 50% increase in qubit density and over a mile of high-density cryogenic flex IO wiring within a single dilution refrigerator, is a model for future hardware design and offers performance comparable to IBM's previous 433-qubit Osprey processor while solving critical scalability challenges.![ibm-quantum-lab](https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIyNDM1NjQwNDczLWltYWdlNC5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19)IBM Quantum scientist in IBM quantum lab. Image source: [IBM](https://newsroom.ibm.com/media-quantum-innovation#gallery_gallery_0:22083)

In parallel, IBM introduced the Quantum Heron processor on the IBM torino quantum system, featuring 133 fixed-frequency qubits with tunable couplers.[3] This development yields three to five times better device performance over the previous flagship 127-qubit Eagle processors, virtually eliminating crosstalk. The Heron processor embodies four years of research and development, laying the foundation for IBM's hardware roadmap and signaling a significant step forward in quantum processor technology.

Moreover, IBM's Quantum System Two is designed for scalable quantum computation and combines cryogenic infrastructure with advanced control electronics and classical runtime servers. It is set to be the bedrock for scalable quantum computation over the next decade, featuring three IBM Quantum Heron processors and embodying a modular architecture that supports parallel circuit executions for quantum-centric supercomputing.

With tools like circuit knitting enhancing the reach of quantum computation and new algorithms emerging for multiple quantum circuits, IBM's approach points towards a heterogeneous computing architecture, essential for realizing quantum-centric supercomputing and paving the way for utility-scale quantum applications.

## Spin Waves: A New Frontier

Amidst the corporate achievements and technological advancements led by giants like IBM, the academic world is pushing the capabilities of quantum technology forward. Universities and research institutions are making significant strides in quantum computing, contributing to the field's momentum with groundbreaking experiments and discoveries. One such example is the pioneering work on spin waves at the Delft University of Technology.[4]

In a groundbreaking experiment at the university, quantum physicists successfully controlled and manipulated spin waves on a chip using superconductors. 

Spin waves, which are information-transmitting waves found in magnetic materials, have long been considered a potential cornerstone for energy-efficient technology, including quantum computing. This new development opens new possibilities for using these waves to connect a quantum computer’s components or create energy-efficient information technology.

The experiment involved a novel approach where a spin wave generates a magnetic field, prompting a superconductor to produce a supercurrent. The supercurrent acts as a mirror, reflecting the magnetic field back to the spin wave and allowing for more precise control over the wave's movement.

The research lays the groundwork for designing devices that combine spin waves and superconductors. These devices could produce minimal heat and sound waves, leading to the development of components like frequency filters or resonators found in cell phone circuits. More importantly, this technology could facilitate connections between qubits in quantum computers.

## Molecular Structures and Quantum Simulation

Another exciting academic-led development in quantum computing is its application in simulating molecular structures at the atomic scale. This capability is expected to revolutionize various chemistry-based domains, including the development of batteries, pharmaceuticals, and fertilizers. By accurately simulating molecular interactions, quantum computers can help design more efficient and effective products.

More recently, a team of physicists from Princeton University successfully entangled individual molecules, creating quantum states where the molecules remain correlated regardless of distance.[5] Quantum entanglement (QE) is a phenomenon in quantum physics where pairs or groups of particles interact in such a way that the quantum state of each particle cannot be described independently of the state of the others, even when the particles are separated by a considerable distance. 

![laser-setup-cooling-controlling-entangling-molecules](https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIyNDM1NjY2MDI3LWltYWdlMS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19)Laser setup for cooling, controlling, and entangling individual molecules. Image source: National Science Foundation

QE is crucial for the advancement of quantum computing, and thus, the discovery holds significant implications for practical applications of quantum computers. Due to their complexity, achieving controllable QE with molecules has been a long-standing challenge. The Princeton team, using a sophisticated tweezer array system, managed to manipulate individual molecules into entangled states. The method demonstrated the viability of molecules as a platform for quantum science, particularly in quantum information processing and simulations of complex materials.

## Shor's Algorithm: A Quantum Leap in Number Factoring

Of course, the domain of quantum computing extends far beyond the microscopic, reaching into macroscopic applications that could revolutionize everything from cryptography and drug discovery to material science and beyond, fundamentally altering how we approach complex problems across disciplines. The leap from simulating the fundamental building blocks of nature to redefining the essence of cryptographic security illustrates the versatile power of quantum technology and leads us to advancements in Shor’s algorithm. 

In 1994, Peter Shor, an MIT mathematician, introduced a quantum algorithm that could significantly expedite the process of finding prime number factors, a key component of data encryption methods used for Internet communications. Shor’s algorithm, a quantum algorithm capable of factoring large numbers exponentially faster than classical algorithms, has continued to garner attention, and for decades, it has stood as a testament to the potential of quantum computers. The algorithm highlights the potential of quantum computing to tackle problems that are currently infeasible for classical computers, such as decrypting certain cryptographic systems.

Recent developments, however, have introduced a new quantum algorithm by Oded Regev of New York University, potentially surpassing Shor’s method in efficiency.

Regev's algorithm, detailed in a preprint on the arXiv server, proposes a more efficient approach to factoring large numbers, potentially requiring fewer quantum gates. This advancement could enable smaller quantum computers to break encryption keys or allow larger machines to decode them more quickly.[6] The algorithm represents the first substantial improvement on Shor’s algorithm in 30 years, reducing the number of gates needed from n2 to n1.5 for an n-bit integer.

The new algorithm has garnered interest from the quantum computing community, with experts like Vinod Vaikuntanathan from MIT and Kenneth Brown from Duke University acknowledging its potential impact.[7] However, Regev’s method also presents challenges, particularly in quantum memory requirements, which could increase the overall cost of the algorithm.

Of course, as quantum computing continues to evolve, there's a possibility that internet encryption might shift to quantum-resistant methods like lattice cryptography. Nonetheless, algorithms like those of Regev and Shor could still have applications, such as decrypting recorded internet traffic from the past. Perhaps most importantly, the novelty of Regev’s work is expected to inspire further innovations in quantum cryptography, a field ripe for significant breakthroughs.

## Beyond Processor Benchmarks: The Shift in Focus

Another recent pivotal development in the quantum computing field was the shift from emphasizing processor benchmarks to focusing on practical implementations. This transition is vital for transforming quantum computing from a theoretical concept into an applied technology with real-world applications. An updated analysis for 2023 projects indicates that industries like automotive, chemicals, financial services, and life sciences could see an economic impact of up to $1.3 trillion by 2035, thanks to quantum computing.[8]

To harness the full potential of quantum computers, it's crucial to focus on extensive application research beyond developing isolated quantum algorithms. Such research involves identifying applications that can benefit from quantum solutions and integrating these technologies into existing computational workflows. This task is challenging, requiring collaboration across disciplines, including quantum information scientists, domain experts, and business leaders, while navigating the constraints of current quantum computers and preparing for future applications with uncertain specifications and timelines.

The process involves developing the right capabilities and knowledge, which could take several years due to the steep and interdisciplinary learning curve. 

Critical technologies still need to be developed to integrate quantum computing into existing workflows, and application research will continue to guide the quantum industry. 

## Quantum Modularization

In architecture, too, quantum broke through some earlier barriers, with quantum modularization emerging as a transformative trend in developing large-scale quantum computers. 

Quantum modularization marks a significant advancement in quantum computing, introducing scalable and flexible architectures that allow for cost-effective upgrades and enhanced system accessibility. This concept—inspired by the modular revolution in traditional computing—brings resilience, customization, and security to the forefront of quantum technology. Businesses can now tailor their quantum systems to specific requirements, optimizing for various applications without requiring complete system overhauls. Modular designs also support interoperability among different quantum technologies, such as ion traps and superconducting quantum computers, enabling the selection of the most appropriate technology for specific tasks.

Crucially, modularity addresses the inherent fragility of quantum systems by allowing for targeted repairs and upgrades, thereby reducing downtime and maintenance costs. It also plays a crucial role in enhancing cybersecurity, offering strategies like private cloud access and secure module ring fencing to protect against data breaches. The shift towards modular quantum computing streamlines research and development efforts by focusing on individual components and paves the way for quantum networking, where interconnected quantum processing units work in concert to achieve unprecedented computing power. As the quantum computing industry evolves, the practical implementation of modular systems is set to redefine the landscape, making quantum technology more accessible and adaptable for businesses worldwide.

## Advances in Quantum Communication

From landscapes to seascapes, quantum technology is also making waves on the communication front. Recent advancements in quantum communication have significantly enhanced the techniques for secure data transmission using quantum principles. A notable achievement in this field is the demonstration of quantum communication over a subsea optical fiber cable. This development, led by euNetworks Fiber UK Ltd., involved a successful quantum link between the UK and Ireland over the 224 km Rockabill subsea network.[9] This marks a significant milestone in quantum communication, pushing the boundaries of secure data transmission over long distances and under challenging conditions.

### Teleportation of Quantum Information

Another groundbreaking advancement is the teleportation of quantum information over distances exceeding 1,200km, facilitated by the Micius satellite.[10] This development demonstrates the potential for quantum communication to operate over vast distances, far exceeding the capabilities of traditional communication methods.

### Quantum Bits Transfer Between Microchips

From long distances to very tiny ones, researchers from the University of Sussex and Universal Quantum have made strides in transferring quantum bits (qubits) between quantum computer microchips.[11] This development is crucial for the practical implementation of quantum computing, enabling the integration of multiple quantum systems and enhancing the scalability of quantum networks.

A key aspect of quantum communication is its inherent security against eavesdropping. The principle that particles of light (photons) can transmit data in a highly fragile state along optical cables ensures that any interference, such as attempts to manipulate or steal data, causes the particles to collapse. This feature is particularly important for securely transmitting sensitive information, like bank details, making quantum communication a robust solution for protecting data in transit.

### Collaborative Breakthroughs in Quantum Networking

Good communication is paramount, quantum or otherwise, and 2023 also saw some excellent academic and corporate networking pushing quantum technology forward. Amazon Web Services (AWS), in collaboration with Harvard University, achieved a breakthrough in quantum networking that could enhance the speed and efficiency of classical telecom networks.[12] Researchers developed a new method for packaging optical fibers that addresses the long-standing issue of data degradation over distances. The method involves connecting tapered ends of optical fibers and devices, such as quantum repeaters, allowing for a gradual and stable light transfer. The interface's resistance to minor displacements, like those caused by traffic noise, makes it suitable for real-world applications.

The AWS innovation is effective at cryogenic temperatures and can also be integrated with modulators used in high-speed telecommunication networks. This compatibility signifies a step towards more efficient interfaces between quantum and classical hardware, paving the way for the widespread adoption of quantum computers and networks.

### Enhanced Error Correction Techniques

Wherever there are breakthroughs, though, there are also errors. A major focus in quantum computing has been the development of enhanced error correction techniques. Given the sensitive nature of quantum computers, which makes them prone to errors, robust error correction methods are vital for their reliability and practicality. Researchers from the RIKEN Center for Quantum Computing have made significant strides in this area by employing machine learning (ML) for error correction.[13] They have developed an autonomous correction system that, despite being approximate, can efficiently determine the best ways to correct errors. This system leverages ML to search for error correction schemes that minimize device overhead while maintaining effective error correction performance.

The researchers focused on an autonomous approach to quantum error correction, where a specially designed artificial environment eliminates the need for frequent error-detecting measurements. This approach is particularly relevant for bosonic qubit encodings, which are used in some of the most promising quantum computing machines based on superconducting circuits. Bosonic qubit encodings represent a complex optimization task in the vast search space of quantum computing. The team addressed this challenge using reinforcement learning, an advanced ML method where an agent learns and optimizes its action policy by exploring an environment.

Furthermore, the research group discovered that a surprisingly simple, approximate qubit encoding could significantly reduce device complexity compared to other proposed encodings. The simpler encoding outperformed its competitors in error correction capabilities. This finding is crucial, as it suggests that less complex quantum computing machines can be highly effective in error correction, making quantum computing more accessible and practical.

In a similar fashion, researchers at Google Quantum AI have achieved a significant breakthrough in quantum error correction.[14] They demonstrated that computational error rates could be reduced by increasing the number of qubits used for quantum error correction. 

    

## Amphenol RF SMP Connectors for High-Frequency Precision

![amphenol-rf-smp-connector](https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIyNDM1NzM0MDI0LWltYWdlMy5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19)Amphenol RF SMP Connectors, designed for quick installation and precise alignment, support high-frequency applications up to 40GHz

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## Conclusion

2023 was a transformative year for quantum computing, marked by remarkable achievements and promising trends that extended beyond academia to practical, real-world applications. These developments, led by collaborations between industry giants and leading universities, pave the way for quantum technologies that promise to revolutionize fields like cryptography, medicine, and energy. 

The progress made in quantum error correction and network efficiency highlights a shift towards operational quantum systems, edging closer to achieving quantum supremacy. This year's achievements highlight the growing capabilities of quantum computers and hint at their future potential to solve some of humanity's most challenging problems.

_This article was initially published in "[**METHODS: Engineering the Quantum Future**](https://resources.mouser.com/methods/engineering-the-quantum-future)," an e-magazine by Mouser Electronics. It has been substantially edited by the Wevolver team and [**Ravi Y Rao**](https://www.wevolver.com/profile/ravi.rao). It's the second article in the Engineering the Quantum Future Series. Upcoming articles will introduce readers to more trends and technologies transforming quantum computing._

* * *

_The[**introductory article**](https://www.wevolver.com/article/engineering-the-quantum-future/)** ** explores the current state of quantum computing, highlighting its challenges and potential_

 _The[**first article**](https://www.wevolver.com/article/from-bits-to-qubits-an-introduction-to-quantum-computing/)** ** dives into the foundational concepts of quantum computing, covering its theoretical basis, technological evolution, and potential applications_

 _The[**second article**](https://www.wevolver.com/article/breakthroughs-in-quantum-computing/)** ** takes a look at some transformative breakthroughs in quantum computing_

 _The[**third article**](https://www.wevolver.com/article/quantum-computing-and-energy-optimization-a-future-beyond-moores-law/)** ** examines the role of quantum computing in enhancing energy efficiency_

 _The[**fourth article**](https://www.wevolver.com/article/the-quantum-web-interconnecting-quantum-processors-for-larger-scale-machines/)** ** explains quantum interconnectivity, detailing how linking quantum processors can amplify computing power_

 _The[**fifth article**](https://www.wevolver.com/article/the-quantum-threat-to-cybersecurity-and-the-quest-for-quantum-proof-encryption/)** ** addresses how the advancements in quantum capabilities pose significant challenges to traditional encryption methods and cybersecurity_

 _The[**sixth article**](https://www.wevolver.com/article/technical-and-ethical-issues-in-quantum-computing-the-quantum-challenge/)** ** discusses the technical and ethical challenges of quantum computing as the technology progresses_

* * *

## References

[1] IBM Quantum. IBM Quantum Computer Demonstrates Next Step Towards Moving Beyond Classical Supercomputing. IBM Newsroom. Available from: <https://newsroom.ibm.com/2023-06-14-IBM-Quantum-Computer-Demonstrates-Next-Step-Towards-Moving-Beyond-Classical-Supercomputing>. Accessed June 26, 2024.

[2] Exclusive: A Closer Look at IBM’s Heron and Condor Quantum Processors. All about Circuits. Available from: <https://www.allaboutcircuits.com/news/closer-look-at-ibms-heron-and-condor-quantum-processors/>

[3] The hardware and software for the era of quantum utility is here. Jay Gambetta. IBM. Available from:  <https://www.ibm.com/quantum/blog/quantum-roadmap-2033>

[4] Delft University of Technology. (2023, October 26). Controlling waves in magnets with superconductors for the first time. ScienceDaily. Retrieved June 30, 2024 from [www.sciencedaily.com/releases/2023/10/231026171429.htm](http://www.sciencedaily.com/releases/2023/10/231026171429.htm)

[5] Garlinghouse T. Physicists ‘entangle’ individual molecules for the first time, bringing about a new platform for quantum science. Office of the Dean for Research, Princeton University. 2023 Dec 7. Available from: <https://www.princeton.edu/news/2023/12/07/physicists-entangle-individual-molecules-first-time-bringing-about-new-platform>

[6] Regev O. A more efficient quantum algorithm for integer factorization. [Internet]. 2023 Aug [cited 2024 Jun 26]. Available from: <https://arxiv.org/abs/2308.06572>

[7] Ragavan S, Vaikuntanathan V. Space-efficient and noise-robust quantum factoring. arXiv. 2023 Oct 2 [cited 2024 Jun 26]. Available from: <https://arxiv.org/abs/2310.00899>

[8] Quantum Technology Monitor. McKinsey & Company; 2023 Apr.

[9] University of York. First time test of UK / Ireland quantum communications with underwater cable. 2023 Oct 3. Available from: <https://www.york.ac.uk/news-and-events/news/2023/research/quantum-communications-underwater-cable/>

[10] Juan Yin et al. ,Satellite-based entanglement distribution over 1200 kilometers.Science356,1140-1144(2017).DOI:10.1126/science.aan3211

[11] EurekAlert! UK Scientists make major breakthrough in developing practical quantum computers that can solve big challenges of our time. [Internet]. Washington: American Association for the Advancement of Science (AAAS). 2023 Feb 8. Available from: <https://www.eurekalert.org/news-releases/979109>

[12] AWS and Harvard University researchers reported a breakthrough in quantum networking that improves data transmission over optical fibers. Tech Monitor. 2023 Oct 16. Available from: <https://techmonitor.ai/technology/infrastructure/aws-claims-quantum-networking-breakthrough>

[13] RIKEN. Machine learning contributes to better quantum error correction. [Internet]. Wako (Japan): RIKEN Center for Quantum Computing; 2023 Sep 8. Available from:  <https://www.riken.jp/en/news_pubs/research_news/pr/2023/20230908_2/index.html>

[14] Nature Editorial. Google’s quantum computer hits key milestone by reducing errors [Internet]. Nature; 2023 Feb 22. Available from: <https://www.nature.com/articles/d41586-023-00536-w>

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url: http://arxiv.org/abs/2501.17825v1
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# Quantum Physics

**arXiv:2501.17825v1** (quant-ph) 

[Submitted on 29 Jan 2025]

# Title:Optimizing Superconducting Qubit Performance: A Theoretical Framework for Design, Analysis, and Calibration

Authors:[Sirshi S Ram](https://arxiv.org/search/quant-ph?searchtype=author&query=Ram,+S+S), [Muralikrishna Molli](https://arxiv.org/search/quant-ph?searchtype=author&query=Molli,+M), [Vamshi Mohan Katukuri](https://arxiv.org/search/quant-ph?searchtype=author&query=Katukuri,+V+M), [Bharadwaj Chowdary Mummaneni](https://arxiv.org/search/quant-ph?searchtype=author&query=Mummaneni,+B+C)

View a PDF of the paper titled Optimizing Superconducting Qubit Performance: A Theoretical Framework for Design, Analysis, and Calibration, by Sirshi S Ram and 3 other authors

[View PDF](/pdf/2501.17825v1)

> Abstract:Designing a qubit architecture is one of the most critical challenges in achieving scalable and fault-tolerant quantum computing as the performance of a quantum computer is heavily dependent on the coherence times, connectivity and low noise environments. Superconducting qubits have emerged as a frontrunner among many competing technologies, primarily because of their speed of operations, relatively well-developed and offer a promising path toward scalability. Here, we address the challenges of optimizing superconducting qubit hardware through the development of a comprehensive theoretical framework that spans the entire process - from design to the calibration of hardware through quantum gate execution. We develop this framework in four key steps: circuit design, electromagnetic analysis, spectral analysis, and pulse sequencing with calibration. We first refine the qubit's core components - such as capacitance, Josephson junctions, and resonators - to set the foundation for strong performance. The electromagnetic analysis, using the Lumped Oscillator model, allows us to map out the capacitance matrix, ensuring that we minimize spectral dispersion while maximizing coherence times. Following this, we conduct spectral analysis to fine-tune the qubit's frequency spectrum and coherence properties, ensuring that the qubit parameters are optimized. Finally, we focus on pulse sequencing, including pulse-width optimization, DRAG optimization, and randomized benchmarking, to achieve high gate fidelity. We applied this framework to both Transmon and Fluxonium qubits, obtaining results that closely match those found in experimental studies. This work provides a detailed and practical approach to the design, optimization, and calibration of superconducting qubits, contributing to the broader effort to develop scalable quantum computing technologies. 

Comments: | 17 pages, 18 fugures, 6 tables  
---|---  
Subjects: |  Quantum Physics (quant-ph)  
Cite as: | [arXiv:2501.17825](https://arxiv.org/abs/2501.17825) [quant-ph]  
  | (or  [arXiv:2501.17825v1](https://arxiv.org/abs/2501.17825v1) [quant-ph] for this version)   
  |  <https://doi.org/10.48550/arXiv.2501.17825> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Bharadwaj Chowdary Mummaneni Chowdary [[view email](/show-email/9d571c12/2501.17825)]   
**[v1]** Wed, 29 Jan 2025 18:17:16 UTC (4,263 KB)  


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![Quantum Computing](https://microtime.com/wp-content/uploads/2024/09/blogs-quantum.jpg)

# Quantum Computing in 2024: Breakthroughs, Challenges, and What Lies Ahead

September 5, 2024|[Microtime Computers](https://microtime.com/author/marketingformsps/ "Posts by Microtime Computers")|

Quantum computing (QC), once a theoretical concept confined to academic research, has rapidly evolved into one of the most exciting and promising fields in technology. As we move through 2024, QC is inching closer to practical applications that could revolutionize industries from cryptography to drug discovery. However, alongside these breakthroughs come significant challenges that must be addressed before quantum computing can reach its full potential. Let's explore the latest developments in quantum computing, the obstacles that remain, and what the future may hold for this transformative technology.

## Breakthroughs in Quantum Computing in 2024

2024 has been a year of significant progress in the field of QC, with several key breakthroughs that are bringing us closer to realizing its potential:

**1\. Increased Qubit Stability and Error Correction**  
One of the most critical challenges in quantum computing has always been maintaining the stability of qubits—the basic units of quantum information. In 2024, researchers have made notable advancements in error correction techniques, which are essential for stabilizing qubits and reducing the errors that occur during quantum computations. Improved error correction codes and the development of more stable qubits, such as topological qubits, have pushed the boundaries of what is possible, bringing us closer to achieving reliable quantum computing.

**2\. Quantum Supremacy Milestones**  
Quantum supremacy—the point at which a quantum computer can solve a problem that classical computers cannot—has been a hot topic in recent years. In 2024, several QC firms and research institutions have announced new milestones in this area. While there is still debate over what constitutes true quantum supremacy, the latest demonstrations have shown quantum computers tackling increasingly complex problems, outpacing their classical counterparts in specific tasks such as complex simulations and optimization problems.

**3\. Advancements in Quantum Algorithms**  
The development of quantum algorithms has seen significant progress in 2024. New algorithms designed to take advantage of quantum computing’s unique capabilities are being developed, offering the potential to solve problems in fields such as cryptography, materials science, and machine learning more efficiently than ever before. For instance, advances in quantum algorithms for factoring large numbers have implications for breaking traditional encryption methods, a development that could reshape the field of [cybersecurity](https://microtime.com/cybersecurity-services/).

**4\. Commercial Quantum[Cloud Services](https://microtime.com/cloud-computing/)**  
Quantum computing is gradually becoming more accessible, thanks to the expansion of quantum [cloud services](https://microtime.com/cloud-computing/) offered by tech giants such as IBM, Google, and Amazon. In 2024, these platforms have introduced more powerful quantum processors, allowing businesses and researchers to experiment with QC without needing to build and maintain their own quantum hardware. These services are making it easier for organizations to explore quantum computing applications in a real-world context, accelerating innovation across industries.

## Challenges Facing Quantum Computing

Despite these exciting breakthroughs, significant challenges remain before quantum computing can be fully realized and integrated into everyday use:

**1\. Scalability Issues**  
While qubit stability has improved, scaling quantum computers to the level necessary for solving large, complex problems remains a daunting challenge. Building quantum computers with millions of qubits that can operate reliably in tandem is still beyond our current capabilities. As researchers work to overcome these scalability issues, the goal of creating large-scale quantum computers remains on the horizon.

**2\. Quantum Error Correction**  
Although error correction has improved, it is not yet at the level required for fully fault-tolerant QC. Quantum systems are inherently susceptible to noise and decoherence, which can introduce errors into computations. Developing more efficient and effective error correction methods is essential for the future of quantum computing, and remains an active area of research in 2024.

**3\. Hardware Limitations**  
The physical construction of quantum computers presents significant engineering challenges. Quantum processors need to operate at extremely low temperatures, close to absolute zero, and are highly sensitive to environmental disturbances. Maintaining the delicate balance required for quantum operations is difficult, and building robust, reliable quantum hardware that can function outside of highly controlled laboratory environments is still a major hurdle.

**4\. Security Concerns**  
The potential of QC to break current cryptographic systems is both a promise and a threat. As quantum computers become more powerful, they could render many of the encryption methods that secure today’s digital communications obsolete. This has led to a race to develop quantum-resistant cryptography, but widespread adoption is still years away. The looming threat of quantum-enabled cyberattacks is a significant concern for governments and industries worldwide.

**5\. High Costs and Accessibility**  
Quantum computing technology remains expensive, and the expertise required to work with quantum systems is still relatively rare. This limits access to QC for many businesses and researchers, particularly smaller organizations without the resources to invest in cutting-edge technology. While quantum [cloud services](https://microtime.com/cloud-computing/) are helping to bridge this gap, the costs and complexity of quantum computing remain barriers to widespread adoption.

## What Lies Ahead for QC

As we look to the future, the road ahead for quantum computing is both challenging and full of potential. Here are some key developments to watch for in the coming years:

**1\. Quantum-Classical Hybrid Systems**  
One of the most promising directions for QC is the development of quantum-classical hybrid systems. These systems combine the strengths of classical computing with the unique capabilities of quantum processors, allowing for more efficient problem-solving. In the near term, we are likely to see more hybrid approaches that leverage quantum computing for specific tasks while relying on classical computing for others.

**2\. Quantum Networking and the Quantum Internet**  
Quantum networking, which involves connecting quantum computers over long distances, is an emerging field with the potential to revolutionize communication and data sharing. The concept of a quantum internet, where quantum information is transmitted securely over vast distances, is still in its early stages but could become a reality within the next decade. This would open up new possibilities for secure communication, distributed quantum computing, and more.

**3\. Wider Industry Adoption**  
As quantum computing technology matures and becomes more accessible, we can expect to see broader adoption across various industries. Sectors such as finance, healthcare, energy, and logistics are likely to be early adopters, using QC to optimize processes, develop new materials, and solve complex logistical challenges. The potential applications are vast, and as businesses begin to see tangible benefits from QC, its adoption will accelerate.

**4\. Ongoing Research and Development**  
The future of QC will be shaped by ongoing research and development efforts. Governments, academic institutions, and private companies are investing heavily in quantum research, with the goal of overcoming current challenges and unlocking the full potential of this technology. As breakthroughs continue to occur, we will move closer to the realization of quantum computing’s promise.

As we look ahead, the continued advancement of quantum computing will depend on the collaboration between researchers, engineers, and businesses. The journey to fully functional QC is still in its early stages, but the progress made in 2024 demonstrates that the quantum revolution is well underway. By staying informed and engaged with the [developments](https://www.localitcompanies.com/boston-ma/) in this field, businesses and individuals alike can prepare for the transformative impact that quantum computing is set to have on the world.

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url: https://www.mdpi.com/2624-960X/6/4/39
scrape_date: 2025-03-11T15:03:12.638814
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title:
Quantum Computing: Navigating the Future of Computation, Challenges, and Technological Breakthroughs

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#  Quantum Computing: Navigating the Future of Computation, Challenges, and Technological Breakthroughs 

by 

Qurban A. Memon

![](/bundles/mdpisciprofileslink/img/unknown-user.png)Qurban A. Memon

[ SciProfiles ](https://sciprofiles.com/profile/1095917?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[ Scilit ](https://scilit.com/scholars?q=Qurban%20A.%20Memon)[ Preprints.org ](https://www.preprints.org/search?search1=Qurban%20A.%20Memon&field1=authors)[ Google Scholar ](https://scholar.google.com/scholar?q=Qurban%20A.%20Memon)

Qurban A. Memon received his bachelor's degree from Mehran University of engineering and and his [...] 

Qurban A. Memon received his bachelor’s degree from Mehran University of engineering and technology, and earned his M.S. from the University of Gainesville, Florida, and his Ph.D. from the University of Central Florida, Orlando. He is currently an associate professor in the Electrical Engineering Department of the College of Engineering at UAE University, Al-Ain, UAE. He has conducted research in intelligent systems, networks, and e-health. He is a principal (lead) author of over hundred publications in international journal and conferences. 

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1[ __](/cdn-cgi/l/email-protection#200f434e440d4347490f4c0f454d41494c0d50524f54454354494f4e03101010141711101311131110114615461143111411431145114613111014111011141014154611101112154611101114),

Mahmoud Al Ahmad

![](/bundles/mdpisciprofileslink/img/unknown-user.png)Mahmoud Al Ahmad

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Mahmoud Al Ahmad (Senior Member, IEEE) received the B.Sc. degree in electrical engineering Birzeit, [...] 

Mahmoud Al Ahmad (Senior Member, IEEE) received the B.Sc. degree in electrical engineering from Birzeit University, Ramallah, Birzeit, in 1999, and the M.Sc. and Ph.D. degrees in microwave engineering from Technische Universität München, Munich, Germany, in 2002 and 2006, respectively. He is currently a Full Professor with the Department of Electrical and Communication Engineering, United Arab Emirates University, Al Ain, United Arab Emirates. He has over ten years of electronic materials and device fabrication research experience in academia, national laboratories, and industry. He has managed several research projects and teams with annual budgets of up to U.S. $1 million. He has also conducted research in energy harvesting technologies and frequency agile circuits with Siemens AG/CNRS and the King Abdullah University of Science and Technology (KAUST) and has published more than 20 journal articles and 35 conference papers in this domain with more under review. He is a principle (lead) author of around 55 published papers in journals and international conferences and has over 40 presentations at international conferences (several of which has been invited). His research interests include the design and fabrication of self-powered, low-powered nano-based electronic devices and systems and applied electromagnetic for biomedical applications. 

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1,*[__](/cdn-cgi/l/email-protection#3f105c515b125c58561053105a525e5653124f4d504b5a5c4b5650511c0f0f0b0c095b0f5c0f0e0f5c0f0a0f0f0f5c0f060d5b0e070f5c0f070e070b0c0f5c0f5a0b0c0f5c0f07)[![](https://pub.mdpi-res.com/img/design/orcid.png?0465bc3812adeb52?1741356594)](https://orcid.org/0000-0003-1243-5207)and

Michael Pecht

![](/profiles/505365/thumb/Michael_Gerard_Pecht.png)Michael Pecht

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Michael Pecht (Life Fellow, IEEE) received the first M.S. degree in electrical engineering and and [...] 

Michael Pecht (Life Fellow, IEEE) received the first M.S. degree in electrical engineering and the second M.S. and Ph.D. degrees in engineering mechanics from the University of Wisconsin, Madison, USA. He is the Founder and the Director of the Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, which is funded by over 150 of the world’s leading electronics companies at more than US$6M/year. He is currently a Distinguished Professor in mechanical engineering and a professor in applied mathematics with the University of Maryland. He is a world-renowned expert in strategic planning, supply chain management, design, testing, IP, and risk assessment of electronic products and systems. He has written more than 40 books on product reliability, development, and supply chain management. He has also written over 900 technical articles and has ten patents. He is a Professional Engineer, an IEEE Fellow, an ASME Fellow, an SAE Fellow, and an IMAPS Fellow. In 2010, he received the IEEE Exceptional Technical Achievement Award for his prognostics and systems health management innovations. He is serving as the Editor-in-Chief for Circuit World. He served as a Chief Editor for Microelectronics Reliability, for 16 years, and IEEE Transactions on Reliability, for nine years. 

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2[ __](/cdn-cgi/l/email-protection#16397578723b75717f397a39737b777f7a3b666479627375627f7978352626272321262725272e262225262623277227222373272327222623)[![](https://pub.mdpi-res.com/img/design/orcid.png?0465bc3812adeb52?1741356594)](https://orcid.org/0000-0003-1126-8662)

1

Electrical and Communication Engineering Department, UAE University, Al Ain 15551, United Arab Emirates

2

Center for Advanced Life Cycle Engineering, University of Maryland at College Park, College Park, MD 20742, USA

*

Author to whom correspondence should be addressed. 

_Quantum Rep._ **2024** , _6_(4), 627-663; <https://doi.org/10.3390/quantum6040039>

Submission received: 2 November 2024 / Revised: 12 November 2024 / Accepted: 13 November 2024 / Published: 16 November 2024

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[ ](https://pub.mdpi-res.com/quantumrep/quantumrep-06-00039/article_deploy/html/images/quantumrep-06-00039-g001.png?1731750185 "
                        <strong>Figure 1</strong><br/>
                                                    <p>Time plot of current advances in classical computing power.</p>
                                                ") [ ](https://pub.mdpi-res.com/quantumrep/quantumrep-06-00039/article_deploy/html/images/quantumrep-06-00039-g002.png?1731750186 "
                        <strong>Figure 2</strong><br/>
                                                    <p>Classical computing bit vs. qubit \(can exist in both states\).</p>
                                                ") [ ](https://pub.mdpi-res.com/quantumrep/quantumrep-06-00039/article_deploy/html/images/quantumrep-06-00039-g003.png?1731750187 "
                        <strong>Figure 3</strong><br/>
                                                    <p>Various qubits and their pros and cons.</p>
                                                ") [ ](https://pub.mdpi-res.com/quantumrep/quantumrep-06-00039/article_deploy/html/images/quantumrep-06-00039-g004.png?1731750189 "
                        <strong>Figure 4</strong><br/>
                                                    <p>A quantum processing unit suspended beneath refrigeration setup, maintaining the processor at ultracold temperatures necessary for its operation source: <a href="https://www.research.ibm.com/ibm-q/network/" target="_blank">https://www.research.ibm.com/ibm-q/network/</a> \(accessed on 1 April 2024\).</p>
                                                ") [ ](https://pub.mdpi-res.com/quantumrep/quantumrep-06-00039/article_deploy/html/images/quantumrep-06-00039-g005.png?1731750189 "
                        <strong>Figure 5</strong><br/>
                                                    <p>Challenges faced by quantum computing.</p>
                                                ") [ ](https://pub.mdpi-res.com/quantumrep/quantumrep-06-00039/article_deploy/html/images/quantumrep-06-00039-g006.png?1731750189 "
                        <strong>Figure 6</strong><br/>
                                                    <p>Illustration of quantum decoherence.</p>
                                                ") [ ](https://pub.mdpi-res.com/quantumrep/quantumrep-06-00039/article_deploy/html/images/quantumrep-06-00039-g007.png?1731750190 "
                        <strong>Figure 7</strong><br/>
                                                    <p>Surface code for error correction.</p>
                                                ") [ ](https://pub.mdpi-res.com/quantumrep/quantumrep-06-00039/article_deploy/html/images/quantumrep-06-00039-g008.png?1731750190 "
                        <strong>Figure 8</strong><br/>
                                                    <p>Algorithm qubit timeline by the end of the current decade.</p>
                                                ") [ ](https://pub.mdpi-res.com/quantumrep/quantumrep-06-00039/article_deploy/html/images/quantumrep-06-00039-g009.png?1731750192 "
                        <strong>Figure 9</strong><br/>
                                                    <p>Quantum computer attack on public key cryptography.</p>
                                                ") [ ](https://pub.mdpi-res.com/quantumrep/quantumrep-06-00039/article_deploy/html/images/quantumrep-06-00039-g010.png?1731750192 "
                        <strong>Figure 10</strong><br/>
                                                    <p>Environmental considerations in quantum computing.</p>
                                                ") [ ](https://pub.mdpi-res.com/quantumrep/quantumrep-06-00039/article_deploy/html/images/quantumrep-06-00039-g011.png?1731750193 "
                        <strong>Figure 11</strong><br/>
                                                    <p>Quantum computing’s impact on the future.</p>
                                                ") [ ](https://pub.mdpi-res.com/quantumrep/quantumrep-06-00039/article_deploy/html/images/quantumrep-06-00039-g012.png?1731750193 "
                        <strong>Figure 12</strong><br/>
                                                    <p>QC business application development timescale.</p>
                                                ") [ ](https://pub.mdpi-res.com/quantumrep/quantumrep-06-00039/article_deploy/html/images/quantumrep-06-00039-g013.png?1731750194 "
                        <strong>Figure 13</strong><br/>
                                                    <p>Vendor prototype roadmaps. Legend: Vendor \(physical qubits\).</p>
                                                ") [ ](https://pub.mdpi-res.com/quantumrep/quantumrep-06-00039/article_deploy/html/images/quantumrep-06-00039-g014.png?1731750195 "
                        <strong>Figure 14</strong><br/>
                                                    <p>Quantum initiative funding by country \(2014–2030\).</p>
                                                ") [ ](https://pub.mdpi-res.com/quantumrep/quantumrep-06-00039/article_deploy/html/images/quantumrep-06-00039-g015.png?1731750196 "
                        <strong>Figure 15</strong><br/>
                                                    <p>Quantum computing market growth projections.</p>
                                                ")

[Versions Notes](/2624-960X/6/4/39/notes)

## Abstract

**:**

Quantum computing stands at the precipice of technological revolution, promising unprecedented computational capabilities to tackle some of humanity's most complex problems. The field is highly collaborative and recent developments such as superconducting qubits with increased scaling, reduced error rates, and improved cryogenic infrastructure, trapped-ion qubits with high-fidelity gates and reduced control hardware complexity, and photonic qubits with exploring room-temperature quantum computing are some of the key developments pushing the field closer to demonstrating real-world applications. However, the path to realizing this promise is fraught with significant obstacles across several key platforms, including sensitivity to errors, decoherence, scalability, and the need for new materials and technologies. Through an exploration of various quantum systems, this paper highlights both the potential and the challenges of quantum computing and discusses the essential role of middleware, quantum hardware development, and the strategic investments required to propel the field forward. With a focus on overcoming technical hurdles through innovation and interdisciplinary research, this review underscores the transformative impact quantum computing could have across diverse sectors.

Keywords: 

[computation](/search?q=computation); [noise](/search?q=noise); [quantum](/search?q=quantum); [quantum computers](/search?q=quantum+computers); [reliability](/search?q=reliability)




##  1\. Introduction

Quantum computing, an emergent field at the forefront of technology, is predicated on the principles of quantum physics for data processing [1]. This paradigm significantly diverges from classical computing, which operates on binary bits representing states of 0 or 1 [2]. Quantum computing harnesses qubits, which, through quantum superposition, can exist in |0> and |1> states simultaneously [3]. Another fundamental aspect, quantum entanglement, augments the capabilities of quantum computing, enabling it to potentially outperform classical computers in tasks such as optimization and simulation [4]. Central to this topic are three primary elements: qubits, quantum gates, and quantum circuits. The field also explores methods to measure outcomes from quantum circuits by using either local simulations with random sampling or employing remote quantum devices [5].

Quantum computers, whose function is based on the fundamentals of quantum mechanics affecting atomic and subatomic particles, promise capabilities surpassing those of classical computers [6]. This advanced potential is particularly relevant in the fields of drug discovery [7], cryptography [8], finance [9], and logistics [10].

Market growth and investment in quantum computing are significant [11]. The industry, valued at USD 10.13 billion in 2022, is projected to reach approximately USD 125 billion by 2030. This growth is fueled by the demand for high-performance computing and the technology’s applicability in industries such as petroleum, financial services, and aviation. Hybrid quantum computing, which combines quantum and classical computing, has become increasingly crucial for addressing complex problems that surpass the capacity of classical computing. This approach is essential for enabling non-quantum users to access quantum functionalities, particularly through cloud services.

Early quantum computer models encountered more problems than anticipated, resulting in disillusionment. Despite aiming to develop quantum computers with potentially millions of qubits, initial experiments with just a few qubits revealed unexpected noise issues. Nevertheless, the field has seen rapid advancements, transitioned from theoretical concepts to practical applications, and has overcome major technical obstacles. Today, substantial improvements in computational power and precision are evident, marking progress towards achieving operational, large-scale quantum computing and setting the stage for future innovations in this promising field.

Improving the reliability of quantum computing is essential, with the main challenge being environmental noise that increases error rates in qubits. Current efforts involve the development of fault-tolerant quantum processors and error-correcting algorithms, although full quantum error correction remains a goal [12]. This ability requires quantum processors with tens of thousands of qubits—a target that has yet to be reached.

####  1.1. Basics of Quantum Computing

Classical computers, which are deeply ingrained in the principles of classical physics, have been integral to the evolution of computational technology [13]. They function by using binary bits confined to 0 or 1 states, based on voltage or charge. The processing in these computers utilizes logic gates such as AND, OR, and NOT operating under Boolean algebra [14]; the solutions they provide are deterministic and limited by the design of such algorithms. Hardware-wise, classical computers employ macroscopic technologies (e.g., CMOS), which are renowned for their speed and scalability [15]. Quantum Dot Cellular Automata (QCA) is a non-transistor computing model that encodes binary information using the positions of electrons in quantum dots or mixed-valence molecules [16]. Unlike conventional transistors, QCA does not rely on electrical current for its operation. A standard QCA cell consists of four quantum dots, with two electrons occupying opposite corners to represent binary states (|0> and |1>) [17]. Information is transmitted across the system via interactions between neighboring cells, governed by Coulombic forces. QCA uses a clocking mechanism with four phases—relaxation, switching, hold, and release [18]—that synchronize state transitions. By arranging QCA cells in specific patterns, binary logic gates like AND, OR, and NOT can be constructed based on electron interactions [19]. Additionally, QCA circuits can be formed using mixed-valence molecules, such as diferoenyl acetylene (DFA), which are arranged in ordered arrays on a substrate and interact through Coulomb forces, controlled by a perpendicular electric field. Since the 1960s, semiconductor transistors have steadily decreased in size, fueling significant advancements in computing power. The timeline trend of this progression [20] is shown in Figure 1, which displays an exponential rise in computing power with forecasts up to 2025. However, contemporary computing power is reaching the limit of this progression due to transistors becoming so minuscule that problematic quantum effects (e.g., tunneling) are inevitable, which compromise semiconductor predictability.

In stark contrast, quantum computers represent a significant leap forward by employing the principles of quantum mechanics [21]. These systems are characterized by high-speed, parallel processing, storing information in quantum bits, or qubits [22]. Related to storing information, the authors [23] argue that a quantum bit can be more advantageous than a classical bit and that quantum memory is anticipated to surpass classical memory in any context. Thus, the implications of quantum advantage are multifaceted.

Quantum computers enhance the management and processing of large data volumes by utilizing quantum states. This efficiency stems from superposition, allowing quantum computers to manage multiple data states simultaneously. Additionally, entangled qubits can store and transmit complex data correlations that surpass the capabilities of classical systems.

Quantum information storage facilitates the execution of quantum algorithms, making large-scale simulations, such as simulating molecular interactions and optimizing large systems, much more efficient. Furthermore, it significantly enhances the speed of searching and optimizing unsorted databases, achieving quadratic improvements over classical methods. However, reliable quantum storage and processing demand a fault-tolerant design, which is intricate and requires substantial advancements in quantum hardware.

Quantum information storage profoundly impacts cryptographic methods. It aids in breaking classical encryption, such as factoring large integers with Shor’s algorithm, undermining the security of many traditional encryption systems. Additionally, it enables new cryptographic techniques like quantum key distribution (QKD), which are theoretically impervious to any computational attack, leading to the development of highly secure communication protocols based on quantum mechanics. These advanced capabilities raise privacy concerns: the ability to break current cryptographic systems poses significant risks, necessitating new frameworks and regulations to safeguard individual and organizational data.

For illustration purposes, classical bits and qubits are shown in Figure 2. Figure 2 demonstrates that qubits are unique in that they can represent the |0> or |1> states, or even exist in both states simultaneously due to quantum superposition, leading to an exponentially larger array of possible states [24]. Once another qubit is added, the number of possible combinations increases.

The outputs from quantum computers are probabilistic, as they are derived from computations on superposed states [25]. Quantum computing employs quantum logic gates for parallel processing, with operations rooted in linear algebra and represented by unitary matrices [26]. The solutions provided by quantum computers are multiple and probabilistic, as they leverage properties such as superposition and entanglement. Furthermore, the distinction between classical and quantum computers highlights a paradigm shift in computational capabilities. Classical computers have laid the foundation for modern computing with deterministic solutions and stable processing within a defined framework [27]. In contrast, quantum computers usher in a new era of possibilities, as they challenge conventional computing models with their capacity for processing complex, probabilistic solutions and conducting calculations at unparalleled speeds [28]. This contrast underscores a future where quantum computing could potentially revolutionize the landscape of data processing and problem-solving. However, it is crucial to note that quantum systems do not adhere to classical mechanics as laid out by Newtonian law. Instead, their behavior and interactions are dictated by the Schrödinger equation, which defines the fundamental nature of these advanced computing systems [29].

####  1.2. Overview of the Current State of Technology

Since the enactment of the National Quantum Initiative Act in 2018, the quantum computing field has seen significant advancements [30]. A notable achievement is demonstrating quantum advantage, where quantum processors perform tasks faster than classical supercomputers [31]. For example, a Google-led team used a 54-qubit processor to complete a task that would take a classical supercomputer around 10,000 years in approximately 200 s [32]. Another team used a 70-qubit processor for a more complex task, which would have taken the Frontier supercomputer approximately 47 years [33]. The focus is also on practical quantum advantage, aiming to solve real-world problems beyond the reach of classical supercomputers. This task requires large-scale quantum computers with many qubits. IBM announced a 433-qubit processor in 2022 [34] with a subsequent launch of a 1000-qubit processor in 2024, while Google targets a 1000-qubit processor by 2025 and a one-million-qubit, error-corrected quantum computer within the next decade [35]. As quantum computing evolves, addressing technological and geopolitical diversity is crucial. This is highlighted by initiatives such as Deutsche Telekom’s collaboration with IQM for cloud-based quantum computing access, emphasizing the global and multifaceted nature of quantum computing advancement [36].

The article [37] explores how companies are leveraging quantum hardware to address combinatorics problems across four industry sectors: cybersecurity, finance and banking, materials and pharmaceuticals, and manufacturing. Moreover, advancements in quantum machine learning provide promising alternatives to classical techniques, potentially enhancing the early stages of biochemical research in drug discovery [7]. A recent study [38] delves into the progress made in different forms of quantum computing, encompassing ion traps, superconducting setups, nuclear magnetic resonance, spintronics, and semiconductor spin-based methods. This research outlines the diverse physical implementations and potential uses of quantum computation across various fields.

Quantum gates perform operations on qubits based on quantum logic, forming the foundation of gate-based quantum computers like IBM’s superconducting qubits and Google’s Sycamore processor. Several competing technologies aim to develop scalable quantum computers, including superconducting, spin, topological, neutral, and photonic qubits. Superconducting qubits operate at low temperatures to avoid decoherence, using Josephson junctions [39] for potential large-scale computing, though challenges remain with cooling and control systems. Trapped ions, manipulated by lasers, encode qubits, but scaling beyond two qubits is difficult due to slow ion movement [40].

Photonic qubits rely on photons to represent qubits and operate probabilistically [41], with challenges like photon loss and maintaining fidelity. Neutral qubits use neutral atoms excited to the Rydberg state for two-qubit gates, but face difficulties with scaling and error rates [42]. Topological qubits, based on anyons, use topologically protected states but struggle with large-scale implementation. Spin qubits, using electron spins in quantum dots, face challenges with fidelity and control as they scale. Although quantum computing holds great promise, unique challenges must be overcome before it becomes widely adopted. Significant advances in hardware, scalability, and robustness are expected in the coming years.

In the sphere of quantum algorithms and applications, the integration of quantum and classical computing to create hybrid algorithms is an exciting development [43]. These hybrid algorithms are being applied in various fields such as machine learning, simulation tools in optics and materials science, and drug discovery in the pharmaceutical industry [44]. There has also been a noticeable increase in private equity investments in quantum computing, reflecting confidence in its long-term potential and the development of various solution patterns by different companies. In the military sector, quantum computing is poised to revolutionize operations, with applications in secure communication, logistics efficiency, enhanced encryption, and security, solving complex optimization problems, and advancing materials science [45].

In summary, quantum computing is transitioning to a stage of practical applications and tangible impacts across various sectors, attracting significant interest from both private and public entities [46]. Similar to the early days of 5G technology, quantum computing is in a developmental stage marked by high expectations and technical challenges. Despite these obstacles, progress is notable, with companies striving to create versatile, error-correcting quantum machines. The quantum Internet, another frontier in this field, is nearing realization sooner than anticipated [47]. The applications of quantum computing are broad and promise to revolutionize sectors ranging from air travel and logistics to precision medicine, enhancing everything from route optimization to disease treatment and prevention. Central to these advancements are high-quality qubits, the foundational elements of quantum computing. Current challenges include qubit instability and a propensity for errors, necessitating specialized software and calibration. Efforts are underway by some companies to improve qubit quality, as they aim for more efficient quantum computing with fewer qubits [48].

##  2\. Quantum Computing Hardware

Due to the nature of quantum states, the hardware components of quantum systems face challenges, necessitating operation under environmental constraints. These components are built with cutting-edge materials with high purity and stability to preserve coherence. The longevity of these materials is crucial, as minor imperfections can greatly impact quantum computer performance over time.

Quantum systems are highly susceptible to environmental factors such as electromagnetic fields. Therefore, effective shielding is crucial to safeguard the hardware from these factors but ensuring this protection throughout the hardware’s lifespan is a challenge. Current shielding techniques want to expel magnetic fields, and electromagnetic radiation up to 5G (or even above, by using sophisticated approaches). Moreover, quantum hardware is susceptible to errors stemming from decoherence, requiring robust QEC techniques. These error-handling methods are linked to the hardware’s lifespan, as they counteract the effects of the operational environment.

Quantum computers can be broadly categorized [49] into four types based on their approach to reaching the solution:

  * Gate-based quantum computing utilizes discrete gate operations to compute a logical outcome for a quantum algorithm

  * Analog quantum computing [50] represents the physical state using continuous variables and continuous transformations. For example, fermionic atoms can be confined within a lattice to mimic electron behavior.

  * Measurement-based quantum computing [51] creates a large, entangled state within a photonic lattice. The extraction of photons from this lattice acts as a gate, enabling the execution of quantum algorithms.

  * Quantum annealers [52] are specialized for solving specific optimization, and function by seeking the system’s lowest energy state, making them particularly effective for optimization tasks.




The paper [53] explores the array of technologies supporting quantum computers. It delves into quantum programming languages, hardware platforms, and software development kits, all of which contribute to revolutionary research, experimentation, and the exploration of the extensive capabilities of quantum computing platforms. Quantum gates manipulate qubits through operations based on quantum logic. Gate-based quantum computers use quantum gates to perform operations on qubits. Examples include IBM’s superconducting qubits and Google’s Sycamore processor. The main technologies that are competing to build a scalable universal quantum computer are discussed below, with a comparative illustration shown in Figure 3.

Superconducting qubits: Superconducting qubits, typically cooled to extremely low temperatures to avoid decoherence, are promising for quantum detection, hybrid experiments, and large-scale quantum computing. Superconducting circuits encode qubits using the energy levels of Cooper pairs, which are pairs of electrons bound at low temperatures, located on opposite sides of a Josephson junction [39]. Higher-frequency qubits could also operate at elevated temperatures, offering scalability due to the greater cooling power available [54]. Research has demonstrated qubits operating between 11 and 24 GHz using niobium Josephson junctions [55], which have fewer quasi-particles compared to aluminum junctions, enabling better performance at higher temperatures—however, calibration, control electronics, and cooling present significant implementation challenges.

Ion Trap qubits: Trapped ions (e.g., calcium or ytterbium ions) manipulated using lasers serve as qubits, with their internal states representing quantum information. In trapped-ion technology, qubits are encoded in two energy levels of an ion. Two-qubit gates leverage the interaction between electrons and phonons, which are quanta of vibrational energy, to couple the ion’s excited electron state with the vibrational modes of the ion chain. The primary challenge for trapped-ion technology is increasing the number of qubits, as achieving entanglement among more than two qubits has proven difficult [40]. Moreover, qubit maneuverability is hindered by the slow physical movement of ions compared to changes in electronic states [55].

Photonic qubits: The system uses properties of photons, such as polarization, to represent qubit states. Quantum information is encoded and manipulated using optical components like beam splitters, waveguides, and detectors. In photonic networks, individual qubits are represented by single photons. A cluster state, which is a highly entangled state comprising multiple qubits, is first prepared [56]. Then, gate operations are performed by measuring the photons. Unlike other qubit technologies, two-qubit gates in photonic networks operate probabilistically rather than deterministically [41]. The challenges include maintaining fidelity at scale, dealing with photon loss during computations, and ensuring a photon source that can consistently produce identical photons for computations to be valid.

Neutral qubits: In neutral-atom technology, qubits are typically encoded in two hyperfine levels of an atom’s ground state. Two-qubit gates are formed by exciting two atoms to the Rydberg state. The challenges include scaling to a million qubits, developing control electronics at scale, and managing higher error rates compared to other technologies [42].

Topological qubits: Based on anyons, quasi-particles that exist in two-dimensional systems and store information in the braiding of their worldlines. Topological quantum computers aim to exploit topologically protected states that are resistant to local perturbations. Creating and manipulating large-scale arrays of topological qubits in a controlled manner remains a significant challenge.

Spin qubits: In spin qubit systems, qubits are represented by the spin of an electron within a semiconductor quantum dot. Two-qubit gates are established between entangled electrons on a silicon chip, with qubit control achieved through microwave electronics. However, spin qubits face many of the same challenges as superconducting circuits, especially regarding fidelity at scale and control electronics. Similar to superconducting circuits, error rates in spin qubits increase with size.

As quantum systems expand in scale with additional qubits, their operational complexity grows, impacting the hardware’s reliability. Moreover, integrating classical computing systems with quantum computers necessitates robust interfaces among them. Regular maintenance is essential to ensure the proper functioning of all components, and given the technological pace, hardware adaptability is crucial, either through replacements or upgrades. In essence, quantum computer hardware must endure extremely controlled environments, with reliability challenges being of paramount importance. Current efforts in materials science and engineering, along with QEC, aim to enhance the durability of quantum computing hardware.

Although the promise of quantum computing has the potential for positive societal impact, building the required hardware presents one of humanity’s greatest technological challenges [57]. The study [58] offers insight into the noisy intermediate-scale quantum (NISQ) era, explores applications across diverse industries (e.g., optimization, cryptography, machine learning, and material science), and examines relevant quantum hardware, quantum gates, and basic quantum circuits. In another survey paper [59], the authors present rapid developments in quantum technologies that have accelerated the development of quantum machines. The recent breakthroughs in quantum technologies and computation are also scrutinized from the standpoint of design automation. The authors in [60] have introduced parallel allocation of quantum chips (QCs) as a novel compilation protocol to optimize quantum multiprogramming performance on NISQ processors and minimize unwanted interference. The experimental findings revealed a trade-off between execution time and success rate in multiprogramming.

Heat leakage from the system on a chip (SoC) to the qubits can interfere with their state, necessitating the relocation of the SoC into the cryogenic environment. In [61], the authors investigate the feasibility of using an off-the-shelf SoC for this task. Measurements were conducted on advanced 5 nm FinFETs at temperatures of 10 K and 300 K, with calibration of a transistor compact model and generation of standard cell libraries for each temperature. A RISC-V SoC was synthesized, its physical layout completed at 300 K, and its performance evaluated at 10 K. The results indicated that the SoC at 10 K was feasible, but its processing speed fell short of handling more than a few thousand qubits. The authors in [62] introduce a cutting-edge hardware platform meticulously crafted to oversee spin-based quantum systems. This platform integrates a two-channel arbitrary waveform generator boasting a 1-GSa/s sampling rate and 16-bit amplitude resolution, along with an eight-channel sequence generator, a two-channel analog-to-digital converter, and a two-channel high-speed time-to-digital converter—all seamlessly integrated onto a printed circuit board. These components were implemented using a field-programmable gate array (FPGA) with specialized data calculation modules seamlessly incorporated into the FPGA logic. To facilitate testing and deployment, the team utilized a pulsed electron spin resonance.

A trusted execution environment for quantum computers is discussed in [63], wherein user circuits are obscured by incorporating decoy control pulses during circuit transpilation. Before reaching the qubits, these decoy pulses undergo attenuation by trusted hardware located within the superconducting quantum computer’s fridge. This initial research shows the feasibility of safeguarding against threats from malicious cloud providers using low-cost technology.

Study [64] introduces the Atos Q-score, a benchmark that is scalable to quantum advantage processor (superconducting and trapped-ion) sizes and beyond, as well as hardware-agnostic and application-centric. The Q-score quantifies the maximum number of qubits effectively utilized for solving the MaxCut combinatorial optimization problem via the quantum approximate optimization algorithm; its behavior is showcased through simulations of quantum processors, including both ideal and noisy conditions. The study [65] also introduces an open-source implementation of the Q-score, simplifying its computation for any cloud-accessible quantum computer. Moreover, a suite of open-source performance benchmarks is presented in the same study to evaluate the efficacy of quantum computing hardware in executing quantum applications.

The existing quantum technologies for quantum computing can be categorized into four generations based on their implementation [66,67]. The first generation includes quantum computers realized through ion traps, operating at a physical speed of kHz and a logical speed of Hz, with a size ranging from millimeters to centimeters [67,68,69]. The second generation technologies are implemented by the superconducting quantum circuits [70,71,72], distributed diamonds [73,74,75], and linear optical [67] technologies. These systems achieve physical speeds in the MHz range and logical speeds in the kHz domain, with sizes ranging from micrometers to millimeters.

The third generation quantum computers utilize quantum dots [76,77], monolithic diamonds [78], or donor [67,79,80] technologies. These systems operate at physical layer speeds in the GHz range and logical speeds in the MHz range, with sizes ranging from nanometers to micrometers. The fourth-generation quantum computers involve topological quantum computing technology [67,81] and are still evolving. Each generation represents advancements in speed, logical processing capability, and miniaturization, indicating a progression toward more powerful and compact quantum computing systems. An illustrative view of such an experimental quantum computing setup at IBM [82] utilizing superconductors is depicted in Figure 4.

##  3\. Technical Challenges and Reliability Issues

Despite significant advancements, quantum computing is not yet a technology that is widely accessible or practical for everyday use. Quantum computers are not readily available for general consumer use due to their complexity, high cost, and the need for specialized operating conditions, such as extremely low temperatures for superconducting qubits [83], which makes them impractical for personal or typical business use. Moreover, cloud-based quantum computing services are being offered by major tech companies such as IBM, Google, and Microsoft [84]. These platforms enable researchers, developers, and businesses to experiment with quantum algorithms and applications. However, their primary use is still directed towards research and development rather than mainstream applications.

The focus of quantum computing at present is on specialized applications where its unique abilities (e.g., handling complex calculations and large datasets) offer significant advantages [85]. Fields such as cryptography, drug discovery, and materials science, as well as complex optimization problems, are some areas where quantum computing is being explored. Figure 5 illustrates major technical challenges currently being faced by quantum computing industry and academia [86]. It includes error correction, coherence, and the creation of a large number of stable qubits. Addressing these challenges is crucial for making quantum computing more accessible and practical for broader end-user applications. Currently, universities and research institutions are the main end users of quantum computing technology; they use it for advancing scientific research and educating the next generation of quantum scientists and engineers. Some industries with high computational needs (e.g., finance and pharmaceuticals) are beginning to explore the use of quantum computing [87]. However, these applications are in their early stages and not yet mainstream. In summary, although quantum computing continues to progress rapidly and holds significant promise, it largely remains a tool for specialized research and development. Widespread and practical use for everyday end users remains a goal for the future, with the technology yet to overcome several key challenges to reach this stage. Below, we briefly discuss each technical challenge. The respective details are discussed in later sections.

####  3.1. Quantum Algorithms

One approach involves iteratively adjusting the probability amplitudes in a qubit vector until the target value is close to 1 and the other values are near 0. This technique is employed in Grover’s algorithm, which searches through an unordered list of n elements in n iterations, thereby locating the desired key with high probability [88]. Another method is to structure the computation such that any of the random results measured from a qubit vector are acceptable. This strategy is utilized in Shor’s algorithm for prime factorization of large numbers and is based on the quantum Fourier transform [89]. Here, qubits are manipulated to encode values with a specific period r. The Fourier transform converts the series to have a period k/r, where k is the number of qubits. Since this period is fractional, many values have r as their denominator. The result is measured, and continued fraction expansion is employed to determine r. If an integer is measured instead of a fraction, the calculation can be repeated. Given that prime factorization underpins most modern cryptographic security systems, Shor’s algorithm has garnered significant attention. Both of these algorithms highlight the distinct approaches in quantum computing for solving specific problems efficiently.

Numerous strategies for quantum optimization have emerged. Quantum annealing, modeled after simulated annealing, is one such method designed for global optimization problems [90]. It executes a series of quantum fluctuations to gradually transform a simple energy landscape into a more intricate one. Its efficiency is primarily attributed to quantum tunneling, significantly mitigating the risk of getting stuck in local minima. Additionally, quantum walks have also been integrated with other quantum optimization algorithms, such as in Grover search [88], to expedite the search process. Additionally, several important quantum algorithms have recently been developed that leverage core principles of quantum mechanics, such as superposition, unitary transformations, and interference. Linear combinations of unitaries enable the efficient execution of complex tasks, such as Hamiltonian simulation and solving linear systems, by expressing non-unitary operations as sums of simpler unitary ones [91]. Quantum walk [92] generalizes classical random walks, utilizing superposition and interference for faster exploration, which benefits search and optimization tasks. Quantum phase estimation employs the quantum Fourier transform to estimate eigenvalues, a key step in factoring and simulations [93]. Amplitude amplification, a generalization of Grover’s algorithm, enhances success probabilities in search and optimization [94].

An innovative model [95], the duality computer, utilizes quantum interference to potentially outperform both classical and quantum computers. It could enable efficient solutions to complex problems, such as NP-complete problems, and locate specific items in unsorted databases with a single query. Further development of the duality quantum algorithm is detailed in [96], where the linear combination of unitaries technique is applied to non-unitary dynamics on a single qubit, providing explicit decompositions of required unitaries and simulating arbitrary time-dependent single-qubit non-unitary operators. In short, the algorithms share foundational techniques, illustrating how quantum mechanics underpins computational advancements.

####  3.2. Quantum Supremacy and Verification

Google’s quantum supremacy experiment [97] represents a significant milestone, where quantum computers outperformed classical supercomputers in completing a specific task—random circuit sampling. It is anticipated that the computational prowess will persistently burgeon at a double-exponential pace equivalent to Moore’s law [98,99], doubling computational magnitude every few years. In the Noisy Intermediate-Scale Quantum (NISQ) era, quantum computers handle tasks that exceed the capabilities of the most powerful classical systems, thereby achieving quantum supremacy. The most extensive circuits with directly verifiable fidelity currently encompass 53 qubits and employ a simplified gate configuration. Conducting random circuit sampling on these circuits at 0.8% fidelity requires one million cores for 130 s, indicating a million-fold acceleration of the quantum processor compared to a single core [97]. Quantum processors utilizing superconducting qubits can now undertake computations within a Hilbert space of dimension 253, surpassing the capabilities of today’s swiftest classical supercomputers.

The study [100] showcases simulations of hard random quantum circuits, which were recently utilized as benchmarks for the first quantum supremacy demonstration, achieving an average performance of 281 Pflop/s (true single precision) on Summit, the fastest supercomputer globally. The results point to a substantial energy efficiency advantage of NISQ devices compared to classical supercomputers. The authors [101] investigate the constraints on quantum advantage for circuits involving a larger number of qubits and gates than those implemented in current experiments. They suggest that reducing error rates in components rapidly expands the regime of quantum advantage, underscoring the importance of minimizing errors. In a related study, the authors [102], using a next-generation Sunway supercomputer, took 8.5 days to verify three million exact amplitudes for experimentally produced bitstrings, achieving an XEB fidelity of 0.191% (compared to an expected value of 0.224%). The review study [103] offers a foundational overview of quantum computing and evaluates three key quantum supremacy experiments.

Quantum supremacy has been achieved through a series of breakthroughs, signaling the onset of the quantum era. During this time, quantum hardware has undergone significant integration and architectural improvements, a notable evolution from its early development stages. In a review study [104], the authors thoroughly examine the quantum supremacy experiments conducted to date and showcase various pioneering proof-of-concept studies in applied quantum computing. The review provides a detailed overview of the current state of quantum research and its potential influence across a wide range of scientific, industrial, and technological fields. These proof-of-concept examples highlight the immense promise of quantum computing and processors. The journey to fully realize the potential of quantum computing is ongoing, and its ultimate impact is still on the horizon. As we move from the NISQ era to the fault-tolerant phase, we are on the cusp of a remarkable future, where what seems impossible today may soon become both possible and routine.

####  3.3. Qubit Stability

Quantum computers rely on stable qubits to perform complex calculations, but qubits are highly sensitive to external disturbances. Coherence stability and its impact on multi-qubit device performance are also important since superconducting qubits display large and correlated temporal fluctuations [105]. Some of the most problematic issues that limit the implementation of applications on Noisy Intermediate Scale Quantum (NISQ) machines are the adverse impacts of both incoherent and coherent errors. Through a detailed set of measurements, the authors [106] identify inter-day and intra-day qubit calibration drift and the impacts of quantum circuit placement on groups of superconducting qubits in different physical locations on the processor. In another study, the authors [107] highlight the potential of stable organic radicals as high-temperature spin qubits and explore their applications in quantum information science. The authors examine factors that influence their electron spin relaxation and decoherence times and analyze the primary challenges associated with stable organic radical qubits to provide tentative insights into future research directions. Various technologies, such as superconducting qubits and trapped ions, are being explored to extend coherence times. For instance, IBM’s Quantum Hummingbird superconducting processor utilizes a hexagonal arrangement of qubits, allowing a maximum of 65 superconducting qubits with longer coherence times for extended computations. Quantum computer evolution has seen implementations with ion traps [108], second-generation quantum computers with distributed diamonds [109], quantum circuits [70], linear optical [110], third-generation quantum computers with monolithic diamonds [78], quantum dots [111], fourth-generation quantum computers implemented with topological quantum computing technology [81], etc.

####  3.4. Error Handling

The error-related challenges are due to two main factors. Firstly, errors in quantum computation errors differ significantly from those in classical computing. While classical binary bits can only experience a bit flip, qubits are much more complex. They are described by continuous probability amplitudes, making their errors continuous as well. This means that small changes in the superposition of a qubit can be indistinguishable from the intended computation. Additionally, qubits can experience phase flip errors because their amplitude signs can be both positive and negative. Secondly, quantum states must be corrected without directly measuring them, as measurement would collapse the superpositions we need to maintain.

The cost of error correction includes the extra resources required for computation and to periodically perform error correction, which can be performed without decoding the data. Each of these steps is referred to as a fault-tolerant operation. For instance, implementing a fault-tolerant single-qubit operation using the Steane code necessitates approximately 153 physical gates [112].

####  3.5. Quantum Decoherence and Its Implications

Quantum decoherence is a critical aspect of quantum mechanics with significant effects on quantum computing and related disciplines. It entails the degradation of a system’s superposition of states due to interactions with its environment, erasing crucial quantum features. Although quantum systems, including qubits, can inhabit multiple states (|0> and |1>) at the same time, exposure to external influences causes them to transition into classical states, which results in the loss of encoded quantum information. This process is known as decoherence, which forces the system out of superposition and into a singular classical state [113]. This process can be best understood as illustrated in Figure 5. The blue dot in Figure 6 shows a point where a qubit interacts with its environment, leading to entanglement and disturbance [114]. This disrupts the superposition and quantum state, causing irreversible loss of coherence over time. Consequently, the qubit becomes unusable for quantum algorithms.

Quantum decoherence poses significant challenges for quantum computing, as it disrupts the superposition and entanglement of qubits, which are crucial for efficient computation. This process leads to computational errors and hampers the scalability of quantum machines; it also creates obstacles for QEC techniques, which must operate faster than decoherence. Decoherence also diminishes quantum entanglement by reducing the computational power and efficiency of algorithms. The rate of decoherence is crucial, with faster rates posing greater challenges for maintaining coherence during computations. Designing quantum systems often prioritizes isolation from environmental factors by employing techniques such as electromagnetic shielding and ultra-low temperatures. Additionally, the material choice and system architecture play key roles in reducing decoherence effects, as different quantum technologies exhibit varying susceptibility to it. Understanding decoherence provides insights into the transition from the quantum to the classical realms, which is crucial for advancing both quantum technologies and fundamental physics research.

Quantum decoherence is essential to the dynamical description of the quantum-to-classical transition. The authors [115] provide an overview of many experiments that have examined decoherence processes, and survey strategies for preventing and minimizing it. The possible relevance of decoherence in answering fundamental issues is also discussed. A survey article [116] analyzes the entire spectrum of practical qubit devices and alternates between elucidating discussions on the problem of quantum decoherence and its implications for the possible creation of these devices. The authors also examine qubit devices such as quantum key receivers, multiple quantum gates, interaction-free detectors, games, Bell-state analyzers, quantum dense coders, qubit entanglers, entanglement swappers, quantum teleporters and repeaters, quantum robots, and multiple quantum computers.

The article [117] introduces quantum decoherence to non-specialists and explains its associated ideas; it aims to illustrate the potential implications of quantum decoherence for more general philosophical discussions, regardless of specific quantum mechanical interpretations. For example, decoherence demonstrates that any approach that breaks nature down into tiers or components cannot be fundamentally grounded; rather, it stems from a classical understanding of the universe, which is maintained by the decoherence process.

In [118], the authors present a rigorous derivation of decoherence within a fully functional quantum spacetime model, which is conveyed at the Planck scale by non-commutativity. In particular, a generalized quantum system time evolution is obtained where mixed states can develop from pure states. When spacetime non-commutativity causes the action of the time translation generator to become distorted, it induces a time evolution for the density operator resembling that of Lindblad. The maximum permitted mass for elementary quantum systems is demonstrated to be the Planck mass by using the decoherence period governing the trajectory of a free particle. The authors of the article elucidate this matter by showcasing how the fundamental attributes of spacetime at the Planck scale govern quantum decoherence, which leads to the conversion of a pure quantum state into a mixed state.

The article [119] provides a survey of decoherence models and discusses their approximation into quantum Pauli channel models, which can then be implemented on classical computers. The authors also discuss how Monte Carlo simulations on a classical computer can yield the performance of the quantum error correction code for the approximated channel.

One essential task in creating useful quantum technology is the parameter identification of quantum systems. The research reported in [120] investigates the time-varying decoherence for open quantum systems, which can be expressed as an optimization problem given the measurement data of local observables. The authors take the expansion coefficients as optimization variables and expand the unknown decoherence rates into the Fourier series. Subsequently, a minimax issue is developed and solved using a sequential linear programming method. The efficiency of the algorithm is demonstrated through a numerical analysis of a two-qubit quantum system with a time-varying decoherence.

The authors in [121] study several definitions of decoherence and their quantification. Comparisons are made between decoherence occurring in quantum systems featuring an infinite number of eigenstates and spin systems. In the former scenario, the key difference between entanglement with the reservoir occurring at some initial moment and assuming “entanglement at all times” is pointed out. The best computational methods in both domains are discussed.

In summary, quantum decoherence is a major hurdle in the advancement and practical implementation of quantum technologies. Overcoming the challenges posed by decoherence requires a fine balance between isolating the quantum system from environmental interferences and allowing necessary interactions for control and measurement. Addressing these challenges remains a primary focus in current quantum physics and engineering research, which aims to unlock the full potential of quantum computing and other quantum-based technologies.

##  4\. Noise and Error Correction in Quantum Systems

Quantum noise, differing from classical noise due to its intricate nature, is a major factor that affects quantum computing; it arises from interactions within the system and its surroundings, resulting in errors and decoherence, where qubits lose their quantum state. Multiple sources (e.g., thermal fluctuations and electromagnetic interference) contribute to this noise, and impact qubit phase relationships and state probabilities [122]. Quantum noise presents a significant hurdle in the development of fault-tolerant and scalable quantum computers. Decoherence can result from even minute amounts of this noise, which threatens the superposition and entanglement that are essential to quantum systems. This challenge restricts the practical use of complex quantum algorithms and makes it more difficult to remedy errors.

The article [123] explores the influence of physical qubit architecture properties on algorithm execution. Important factors include qubit connectivity and stability, native gate instructions, gate fidelities, and state preparation, and affect both the quantum execution time and the total wall clock time for users. Research into quantum defects within two-dimensional van der Waals (2D vdW) materials [124] is making significant strides. Notable advancements encompass the establishment of quantum guidelines aimed at comprehending and regulating spin defects in solid-state materials, the practical demonstration of quantum defects within 2D vdW materials, and the development of methods for generating and adjusting defects, thereby enabling meticulous control over their characteristics and behaviors.

The research [125] examines the microstructure and performance of devices made from two superconducting thin films, showing that tantalum oxide exhibits lower losses compared to niobium oxide. This is due to the formation of simpler oxides and a more crystalline oxide bonding structure. These results offer valuable insights for enhancing superconducting qubits through the optimization of surface oxide properties. In another study [126], the authors propose a transmon qubit fabrication method that significantly improves T1 relaxation times by encapsulating the surface of niobium to prevent the formation of its lossy surface oxide. The results from the comparative investigation show that niobium oxides negatively impact the coherence times of superconducting qubits. The surface-encapsulated niobium qubits exhibited T1 relaxation times that were 2–5 times longer than those of qubits with native niobium oxides.

Current research efforts are directed toward improving qubit isolation, optimizing control strategies, and generating reliable QEC codes to tackle these problems. Some notable algorithms, like the quantum approximate optimization algorithm (QAOA) [127], are being adapted to improve robustness to tackle defects in superconducting quantum circuits. Handling quantum noise requires advancements not only in algorithms but also in quantum hardware design. The design of quantum systems is susceptible to external factors, resulting in bit flips and phase flips, which are amplified by qubit entanglement. These errors can rapidly propagate, underscoring the need for sophisticated QEC techniques.

For quantum computing to be viable in practical applications, it must be fault-tolerant (i.e., capable of functioning correctly despite component failures or errors). This involves integrating error correction in a way that allows ongoing computations despite the presence of errors. The effectiveness of these QEC strategies is closely linked to the error threshold, which is the maximum error rate at which error correction remains effective. Current research is focused on reducing the physical error rates of qubits and gates to remain below this threshold.

Quantum bit errors are complicated because qubits have a phase—in addition to having a zero or one value—that can fluctuate. At each system level, a method for handling these two types of errors must be devised. Such methods include enhancing the ability to control the computational gear itself and incorporating hardware redundancy so that a correct calculation result may still be obtained even when one or more qubits malfunction. Error handling is a complex process that requires foresight and correction at each phase of system design. Error suppression, error mitigation, and error correction are the three main elements of error management.

Error suppression: Error suppression methods involve anticipating and avoiding adverse effects by introducing adaptations based on an understanding of such effects. The goal of error suppression is to ensure that the processor produces the intended outcome by adjusting control signals to handle errors at the hardware level. Some of these methods (e.g., spin echoes [128], a series of sequences that can assist in refocusing a qubit and enable it to sustain its state for an extended period) have already been incorporated into quantum computers. Spin echo belongs to a class of methods called dynamic decoupling. Derivative Removal by Adiabatic Gate (DRAG) incorporates a component into the usual pulse form to prevent qubits from entering states other than the |0> and |1> states used for calculations. The authors [129] conducted experiments to evaluate how the performance of logical qubits scales with different code sizes and show that the superconducting qubit system performs well enough to counteract the extra errors that come with having more qubits. In another research, the authors [130] suggest employing error filtration as a practical means to suppress errors for gate-based quantum computation without resorting to full quantum error correction. This approach offers a flexible error suppression protocol where the resources required for error suppression are independent of the size of the quantum operation and do not require its logical encoding. In [131], the authors suggest an effective technique to achieve exponential suppression by generating several separate circuit outputs and using symmetries to counteract errors that could skew the expected value and argue that this method is well-suited for present and upcoming quantum devices. Numerous additional approaches, established over decades, are being investigated and implemented in hardware when appropriate.

Error mitigation: Error mitigation reduces or eliminates the impact of noise on expectation values by utilizing the outputs of ensembles of circuits. Considering fault tolerance, error mitigation makes quantum computing viable. The probabilistic error cancellation approach is currently under investigation, which uses samples from an ensemble of circuits to approximate a noise-inverting channel on average to nullify noise. The approach functions similarly to that of noise-cancelling headphones; however, it cancels noise more generally than shot-by-shot. Another approach, zero-noise extrapolation [132], lowers error by extrapolating the circuit’s measurement results at various noise strengths to determine the true value. Each of these approaches has a different overhead and degree of accuracy. The most potent of these methods, for example, has an exponential overhead, meaning that the run time grows exponentially with the problem size. A portfolio of approaches enables users to select the approach that suits their needs, considering their tolerance for overhead and desired level of accuracy. It is anticipated that error mitigation will remain useful for qubits in the hundreds range, despite the overhead. When the comparable circuit depth reaches several hundreds of qubits, a hybrid of mitigation and correction techniques is envisioned. One proposed novel technique for mitigating quantum noise [133] accurately computes the mean output of a quantum device affected by noise. The average behavior of the multi-qubit system is approximated as a modified Pauli channel, where the average output for circuits with varying depths is estimated using Clifford gates. The effectiveness of the technique used on four IBM Q 5-qubit quantum devices shows accuracy improvements reaching 88% and 69% compared to the uncontrolled and pure measurement error mitigation techniques, respectively.

The authors [134] present a quantum error mitigation scheme designed to address quantum computational errors resulting from environmental couplings during gate operations, known as decoherence. The authors argue that this scheme is versatile and can be applied to mitigate various types of quantum noise effects, making it useful for improving the performance of noisy quantum computing, especially for long-depth quantum algorithms. The authors [135] assess the effectiveness of quantum error mitigation techniques by testing them on different benchmark problems and quantum computers, and the findings show that, on average, QEM offers more advantages than not using it, even when considering the additional resources, it consumes. The article [136] presents a quantum error mitigation technique that employs machine learning to enhance the accuracy of quantum computations, and validates through two-, three-, five-, and seven-qubit quantum circuits. The authors claim that the performance of noisy intermediate-scale quantum algorithms without the need for extensive error characterization or full quantum. In [137], the authors argue that error mitigation is essential for achieving lower error levels. However, at higher error levels, QEM raises the variance of the stochastic gradient estimate, potentially necessitating more iterations of stochastic gradient descent when employing QEM.

The literature review [138] examines a range of quantum error mitigation techniques, evaluating their theoretical efficacy and detailing practical hardware demonstrations. It identifies shared characteristics and limitations of these approaches and explains how to choose the most suitable method based on the prevalent type of noise, including algorithmic errors. The review outlines current challenges in the field and discusses the potential for mitigation-based devices to achieve quantum advantage, which could significantly impact scientific research and business. While QEM methods are already crucial for applications, the field of QEM is still evolving and intricate, with much still to be investigated.

Error correction: QEC is complex due to the fundamental principles of quantum mechanics, such as the no-cloning theorem, which prohibits the copying of unknown quantum states. Therefore, QEC codes aim to identify and rectify errors without directly measuring quantum states, often by using redundant encoding with multiple physical qubits representing a single logical qubit. Surface codes are a promising QEC approach, wherein qubits are arranged in a two-dimensional pattern for efficient error detection and correction.

Fault-tolerant quantum computation is the process of adding redundancies to a system such that it can still produce accurate results for any program, even in the event that a few qubits encounter errors. QEC shares the same fundamental idea as that of classical computing, with the requirement that newly discovered error types are also considered. Furthermore, to prevent the state from collapsing, the system must be carefully measured. In QEC, the gates are built to handle a network of physical qubits as if they were nearly error-free logical qubits by encoding single qubit values across multiple physical qubits. To identify and fix errors, a particular set of procedures is carried out, which is collectively referred to as the error correction code.

For every logical qubit, the leading code, now in use, requires many physical qubits O(d2), where d is the distance that corresponds to the errors that may be fixed. The distance d must be set sufficiently large for error correction to meet the quantum device rate for the codes to correct enough errors to attain fault tolerance. However, the number of qubits required for error correction using the surface code is impractical due to the noisy nature of current quantum devices, which have error rates close to 1 × 10−3. To continue reducing the error rates, researchers must both lower device physical error rates and find novel codes that require fewer physical qubits.

The authors [139] corrected associated phase errors occurring within an array of rubidium data qubits using a variety of cesium spectator qubits. By combining feedforward operations, data processing, and in-sequence readout, these errors were suppressed during the quantum circuit’s operation. In another research [140], the authors encoded a lone logical qubit employing the [1,3,7] color code on a 10-qubit trapped-ion quantum computer utilizing quantum charge-coupled device technology. An encoding circuit was used to set the logical qubit’s initial state to the eigenstates of three mutually unbiased bases. The average measurement error and logical state preparation error were contrasted with the average physical SPAM error of qubits. Various syndrome measurements were conducted on the encoded qubit using a decoder in real time to detect any required modifications that needed to be made physically as gates or as a software update. Iterative Bayesian unfolding [141] is a promising method for readout fault correction in universal gate-based quantum computers. It is shown that this method will prevent pathologies from common matrix inversion and least squares approaches. A true quantum processor that can execute a full quantum process tomography of a single qubit was used to realize echo experiments [142]. The obtained results from gate-based quantum computers revealed that coherence errors had a major impact on quantum computing, which was addressed by lengthening the quantum circuit length with the addition of random single qubit unitaries.

Researchers globally continue to develop various error correction strategies and qubit arrangements to identify those with the most promising potential for the future, and they have made notable progress. Recently, they have found a method to overcome the quadratic overhead O(d2), revealing a code that grows linearly with robustness (i.e., increasing the number of qubits increases the robustness). Figure 7 illustrates a d − 5 surface code that corrects bit and phase flips [143]. In this example, the measure qubits, on the (d2 − 1) layout, and shown on the white background represent data qubits. The qubits represented by smaller circles with a light grey background are called stabilizers. Each internal stabilizer acts on four adjacent data qubits, while the boundary stabilizer acts on three data qubits.

Although error correction, error mitigation, and error suppression may sound similar, each calls for a different set of skills and concerns and ensures that quantum computers provide actual benefits. Advancements in any of these domains move quantum computing closer to becoming a reality. Tech industrial giants are currently researching the critical challenge of noise in quantum computing.

In conclusion, addressing the challenges of quantum noise and effective error correction is central to advancing quantum computing. The field continues to evolve, with research focusing on achieving practical and efficient error correction as a key milestone towards scalable and functional quantum computing. Overcoming these challenges is essential, but it requires balancing the benefits of error correction against the complexities and resource demands introduced by additional qubits and technological advancements.

##  5\. Scalability Challenges

Scalability challenges in quantum computers arise from the complex nature of quantum systems, as well as the need for/maintaining coherence and effective error handling. Increasing the number of qubits increases the challenge of preserving their states, as external factors can create decoherence and reduce computational power. Balancing qubit isolation from the environment is a major challenge in quantum computing scalability.

As the number of qubits in quantum systems grows, error rates increase, which must be mitigated by advanced QEC techniques. However, this adds to system complexity, as multiple physical qubits are needed for each logical qubit [143]. Developing fault-tolerant quantum systems that are capable of functioning correctly despite failures or errors is essential for scalability but remains a challenging task. However, the race for scalable quantum computing, also known as qubit count, is already on. Various companies are charting out their plans for scalable quantum computers. One such example is shown in Figure 8, which shows an increase in the number of algorithm qubits each year from 2021 to 2024 and projections until 2028, using 16 1-error correction coding during 2025–2026 and using 32 1-error correction coding during 2027–2028 [144].

The constraints in quantum computing present significant challenges. Ensuring high-quality qubits in large quantities is difficult due to variations in their quality. It is technically challenging to connect several qubits while maintaining their quantum states and enabling qubit–qubit interactions for quantum gates. Additionally, many models require extremely low temperatures, leading to increasingly complex and costly cooling infrastructure as systems scale up. Scaling up quantum computers necessitates additional infrastructure, such as clean room and vibration-free environments. Significant energy is required to maintain operational conditions, especially for cooling large-scale quantum computers. Precisely controlling numerous qubits demands scalable systems, with complexity increasing alongside qubit count. Moreover, higher qubit counts elevate the risk of interference, leading to errors.

Superconducting traveling-wave parametric amplifiers have emerged as highly promising devices for near-quantum-limited broadband microwave signal amplification, making them essential for high-efficiency microwave readout in quantum systems. The authors [145] introduce a Josephson-junction-based traveling-wave parametric amplifier isolator, utilizing third-order non-linearity for amplification and second-order non-linearity for frequency-up conversion of backward-propagating modes, achieving reverse isolation. This development paves the way for quantum-limited broadband microwave amplification without bulky magnetic isolators and with reduced back-action. In another study [146], the authors propose a new phase detection technique using a flux-switchable superconducting circuit, the Josephson Digital Phase Detector (JDPD), which can differentiate between two-phase values of a coherent input signal. Upon excitation by an external flux, the JDPD relaxes into one of two stable states based on the input phase. The study further explores how this method could be applied to superconducting qubit readout in future quantum systems.

The development of algorithms can efficiently leverage the capabilities of large-scale quantum computers but poses significant challenges due to the fundamental disparities between classical and quantum computing. Therefore, algorithms must be tailored for quantum hardware while considering its distinct features and constraints.

####  5.1. Scaling Quantum Systems

The ongoing quest for greater computational power continues to drive efforts to scale up quantum computers. The effectiveness of quantum computers significantly depends on expanding their scale and overcoming the limitations of qubit connectivity. However, achieving large-scale quantum machines faces challenges such as limited coherence time and increased instruction bandwidth, which can impede further scaling efforts. Employing a modular approach is crucial to scale current quantum processors and achieve the large quantities of physical qubits necessary for fault tolerance [147]. In the short term, modularity in superconducting qubits involves implementing short-range interconnections that link neighboring chips [148,149]. In the medium term, long-range gates operating in the microwave frequency range might be conducted over conventional cables spanning longer distances [150]. This approach could establish a non-planar qubit connectivity suitable for efficient error correction [147]. A long-term solution involves entangling remote quantum processor units using an optical link, leveraging microwave-to-optical transduction [151], although this achievement remains unproven. Moreover, dynamic circuits expand the range of operations of a quantum computer by facilitating mid-circuit measurements and classical control of a gate within the qubits’ coherence time. They improve algorithmic quality [152] and qubit connectivity [153]. Error-mitigated dynamic circuits could also facilitate modularity by establishing real-time connections between quantum processor units.

Developing a scalable model for distributed quantum computation poses challenges due to the diverse spectrum of quantum systems ranging from small-scale to large-scale devices. Multichip, VQE, and circuit cutting technologies represent distinct facets of quantum computing, each tackling unique challenges and opportunities in the domain. These technologies frequently complement each other in the quest for more powerful and practical quantum systems. Below, each of these is briefly discussed.

####  5.2. Hardware Development Challenges

A range of technologies have been employed to create successful quantum computing prototypes. However, scaling quantum machines to thousands or even hundreds of thousands of qubits requires more scalable technologies. The optimal spacing between qubits is currently a subject of debate in the physics community, with a trade-off between noise immunity and manufacturing challenges. In the near term, ion trap technologies offer a potential path to scaling up to thousands of qubits. Ion traps are well-understood and backed by extensive experimental data detailing their properties.

The essential aspect of scaling ion traps is the capability to transfer ions between traps using magnetic fields and a series of electrodes. Essentially, both ion traps and silicon-embedded ions depend on the spatial arrangement of qubits and control lines to execute quantum algorithms. Currently, both approaches encounter challenges in fabricating and validating the accurately positioned qubits and control signals, as well as in the quantum data communication within each system. One of the most evident challenges in fabricating quantum computers is the tiny scale of the components and the precision needed for their placement in the system. Given that achieving reliable quantum operations is already challenging with perfectly spaced and aligned components, minimizing, and detecting any variations is essential. The first hurdle in manufacturing is accurately placing the phosphorus atoms. Another issue is the scale of the classical control system, where each control line in the quantum data path is approximately 10 nm wide. Additionally, maintaining the device’s temperature is crucial for the stability of qubits, requiring cooling to below one degree Kelvin. While cooling is relatively straightforward, it adversely impacts classical logic. Two major problems arise: conventional transistors cease functioning as electrons get trapped near their dopant atoms, preventing ionization, and the classical control lines start exhibiting behaviors like conductance quantization, etc.

Once qubits and their controls are manufactured, testing them poses significant challenges. Strict tolerances are necessary, and qubits that are improperly spaced or have misaligned control signals may need to be excluded from the system. Furthermore, issues arise with the spacing and alignment of the ions that constitute the qubits. Although quantum test programs can detect defects, they struggle to differentiate between errors in ion spacing, misalignment, and spacing errors of the control vias.

Quantum computers, which are still in their early stages of development, encounter various technical hurdles. It is noteworthy that not every problem is conducive to quantum computing, and uncertainties persist regarding the scalability of hardware and the feasibility of algorithms. The potential impact of quantum devices is significant, especially in fields such as cryptography, optimization, drug discovery, and materials science. Various approaches to building quantum computers are being explored—each based on different physical systems for creating and manipulating qubits—such as superconductors, ion traps, neutral atoms, photons, spins in semiconductors, and nitrogen-vacancy centers in diamond. Each method has its own set of advantages and challenges, with ongoing research to determine those that are the most suitable for practical quantum computing applications. Benchmarking the performance of quantum computers is a complex task due to their unique properties and architectural layers. Existing benchmarks focus on fundamental physical functionalities and overall characteristics but predicting application performance from these metrics is an intricate process.

####  5.3. Multichip Approach

Networked and Multichip Technology aims to build larger and scalable quantum systems by connecting multiple chips. The authors of the study [154] define a scalable and distributed gate-model quantum computation tailored for near-term quantum systems. The architecture is shown to optimize for computational problems in a distributed fashion. In [155], the authors introduce an innovative strategy aimed at tackling the considerable hurdle of expanding quantum computers. They argue for a holistic architectural viewpoint, underscoring the importance of a complete resolution spanning from hardware to connectivity. Their concept revolves around scalable quantum computing designs wherein dispersed quantum cores are linked through quantum-coherent qubit state transfer connections and managed via a unified wireless interconnect setup.

The researchers in [156] introduce a major advancement in quantum networking with the development of a scalable framework termed a multichip multidimensional quantum entanglement network. This is realized by fabricating integrated nanophotonic quantum node chips using conventional semiconductor methods. Their study highlights the viability of constructing expansive quantum entanglement networks based on chips, representing a notable progression towards practical large-scale quantum communication.

The study [157] proposes an innovative approach to generate multiple qubits through magnetic resonance imaging (MRI). This technique relies on the “gradient” concept and several radiofrequency coils—one shared by all qubits and others designated for individual qubits using small Q-coils. In a separate study, the authors [158] use a floating tunable coupler to enable interactions between superconducting qubits on different chips, creating a modular architecture. They introduce three different designs for multichip tunable couplers, utilizing vacuum-gap capacitors or superconducting indium-bump bonds to connect the coupler to a microwave line on a shared substrate, which then links to the qubit on the neighboring chip.

The paper [159] delves into the possibilities of Quantum Data Networking (QDN), aiming to link multiple smaller quantum computers (QCs) to match the computational capabilities of a single large quantum computer. However, this endeavor requires the transfer of quantum state information, encoded in qubits, among QCs scattered across different locations. The research also identifies core limitations set by quantum physics and communication technologies, examining how they influence the structure and routing protocols of QDN on both basic and advanced levels.

The challenge of scaling quantum computing to meet the demands of its most powerful applications are addressed in [160]. There are restrictions to integrating many qubits into one chip, despite the inherent potential. Although quantum communications present difficulties due to their complexity and susceptibility, multicore architectures are thought to be a promising solution. Thus, it suggests a thorough design strategy that incorporates the communications stack into the architecture of quantum computers. This strategy seeks to overcome these obstacles by integrating computing and communications at the center of the architecture. The objective is to offer design principles that will enable quantum computing to reach its maximum potential.

####  5.4. Circuit Cutting Approach

Circuit cutting technology enables running large quantum circuits on smaller quantum hardware by decomposing them into smaller pieces. The study [161] describes a low-power digital superconducting single flux quantum (SFQ) circuit-based scalable quantum computing infrastructure. This method makes use of SFQ pulses at base temperature, in contrast to current systems that rely on microwave control at room temperature. Using optical control theory, coherent SFQ pulse sequences are used to irradiate qubits to achieve qubit control. In [162], the authors delve into the partitioning of quantum circuits utilizing a hypergraphic representation with the goal of enhancing the efficiency of this process. To minimize communication qubits across partitions, a modified version of the Fiduccia–Mattheyses heuristic is then devised. Compared to existing algorithms, the method dramatically reduces partitioning costs by 10–50% through tuning and implementation. These results provide useful benchmarks for various methods and shed light on quantum progress in distributed contexts.

The study [163] presents Maximum-Likelihood Fragment Tomography (MLFT) as an improved method for circuit cutting, specifically for executing clustered quantum circuits on devices with a limited number of qubits. The results demonstrate that MLFT-enhanced circuit cutting provides a more accurate estimation of a clustered circuit’s output than running the entire circuit. This highlights the potential of circuit cutting to become a standard technique for executing clustered circuits on quantum hardware.

The paper [164] investigates a technique for breaking down primary quantum circuits into smaller segments and evaluates the accuracy of the outcomes derived from these segments. The authors present a new analytical approach that considers multiple aspects of quantum characteristics, such as error data from qubits and the configuration of quantum processors, to efficiently divide the quantum circuits.

The research [165] presents an innovative method to enhance the capability of quantum computations beyond what is restricted by the number of qubits on one device. It involves integrating measure-and-prepare channels randomly, allowing the representation of a complex circuit’s output state as separate across multiple devices. The technique shows notable speed enhancements supported by numerical tests, outperforming previous approaches.

The paper [166] presents a framework, which employs two primary methods to speed up quantum circuit simulation—circuit cutting and Clifford-based simulation. By isolating fragments of Clifford subcircuits within larger non-Clifford circuits, efficient Clifford simulation is made possible, leading to a significant reduction in runtime. After executing these fragments independently, techniques involving circuit cutting and recombination reconstruct the final output of the original circuit from the results of fragment execution. The results illustrate that Clifford-based circuit cutting accelerates the simulation of circuits, making it feasible to evaluate hundreds of qubits within acceptable timeframes.

In [167], a hybrid variational method called the quantum divide and conquer algorithm (QDCA) is presented, which is designed to efficiently solve large combinatorial optimization problems on distributed quantum structures. This is accomplished by integrating graph partitioning and quantum circuit cutting techniques. This method, which is a prime example of application–compiler co-design, is both suitable for near- and long-term distributed quantum systems and flexible enough to adjust to available classical or quantum processing resources. Simulations conducted on maximum independent set problem cases show that QDCA exhibits better performance than comparable conventional techniques.

The objectives outlined in [168] are to design quantum circuits that are efficient for execution on quantum computers and to limit the number of qubits and entanglement gates to help reduce computational time. Three approaches to dimensionality reduction and data management are explored and tested for quantum reinforcement learning in large-scale, real-world settings, with an emphasis on energy-efficient scenarios. The study also shows how quantum neural networks can be used in reinforcement learning applications and emphasizes how QML can be used to improve real-world settings such as energy efficiency scenarios. In [169], the authors present a comprehensive framework to imitate quantum computations using classical hardware acceleration. The framework includes structures such as the quantum Haar transform (QHT) and parts for the critical C2Q data encoding process. This framework is employed for exploring enhancements to the QHT and C2Q algorithms, leading to the development of highly efficient quantum circuits.

In conclusion, scalability in quantum computing involves not only increasing the number of qubits but also enhancing their quality, managing error rates, ensuring efficient interconnectivity, and developing suitable software and control mechanisms. Addressing these challenges is essential for realizing the practical applications of quantum computing and requires collaborative advancements in physics, engineering, materials science, and computer science.

####  5.5. Variational Quantum Eigensolver (VQE)

VQE uses a specific algorithmic approach to solve problems by finding ground-state energies. The research conducted in reference [170] describes a method for achieving scalable variational quantum eigensolver (VQE) calculations in topological quantum phases. This is achieved by building scalable parameterized quantum circuits (PQCs) tailored to individual problems. This PQC is used for numerical simulations of the trivial dimmer phase and the symmetry-protected topological Haldane phase in the non-exactly solvable alternating Heisenberg chain using the VQE technique. The article [171] presents a modular quantum compilation framework for distributed quantum computing (DQC) that accounts for network and device characteristics. This framework was evaluated using circuits such as the VQE and quantum Fourier transform (QFT) on various network topologies. The results show that TeleData operations can positively influence the number of Einstein-Podolsky-Rosen (EPR) pairs used, depending on the compiled circuit’s characteristics.

The article [172] introduces a novel algorithm designed to prepare the ground state of a gapped translationally invariant system on a quantum computer. The algorithm takes advantage of the exponential decay of correlations between distant regions in the ground state to develop scalable quantum circuits for state preparation. When applied to the Schwinger model, the algorithm demonstrates systematic improvability, with its accuracy improving exponentially as the circuit depth increases.

The article [173] investigates the effective use of the VQE for practical chemical modeling on near-term quantum processors. The authors detail the steps involved in writing and executing parallel code on a quantum computer to determine the bond dissociation curves for the diatomic hydride molecules TiH, LiH, NaH, and KH using VQE. By performing VQE in conjunction with Unitary Coupled Cluster Single Double (UCCSD) calculations on TiH, the study assesses the feasibility of modeling a molecule with d-orbitals on actual quantum hardware.

##  6\. Quantum Computing and Cybersecurity

Quantum computing has a substantial influence on cybersecurity, especially concerning cryptographic protocols. Its effect on cybersecurity is deep and far-reaching, presenting a considerable challenge to current data encryption methods. This challenge necessitates a shift in approaches to securing data [174]. The rise of quantum computing threatens existing encryption methods, especially public-key cryptography, which relies on complex math with which traditional computers struggle. Quantum computers, with their superior processing power, could quickly solve these problems, making current encryption techniques vulnerable [175]. Shor’s algorithm, introduced in 1994, threatens current encryption, as it can quickly factor large numbers on quantum computers, with potential use in decrypting data and intercepting communications. The current security mechanism highlighted in Figure 9 will likely be broken down as quantum computers significantly threaten public key encryption schemes. Therefore, it is suggested that post-quantum cryptography could effectively secure data against quantum computing threats [176].

The risk of retroactive decryption is a major concern, where adversaries collect encrypted data now to decrypt it in the future with quantum computing, posing a long-term threat to data privacy. This problem is particularly challenging for those who must protect data for decades, as it questions the reliability of current encryption methods. For example, the US fears that data stolen today could be decrypted by quantum computers in the next decade [177]. The arrival of quantum computers with millions of qubits could compromise data at all levels including the most existing public-key cryptosystems [178], as illustrated in Figure 9.

In response, there has been a push for quantum-resistant cryptography to secure information and communications technology (ICT) infrastructure. Post-quantum cryptography aims to create secure algorithms that can withstand attacks from both classical and quantum adversaries. The US National Institute of Standards and Technology is spearheading the standardization process of developments in these algorithms. To safeguard communications against quantum computer attacks, quantum key distribution (QKD) and post-quantum cryptography (PQC) have been integrated into network security protocols, such as Transport Layer Security. These cutting-edge cyber-security technologies pave the way for quantum-resistant cryptography by developing new algorithms and modernizing existing systems. Moreover, the exploration of hybrid systems that merge classical and post-quantum methods offers a robust defense against future quantum threats [179]. QKD exemplifies this idea by leveraging quantum mechanics to securely share encryption keys, with the unique ability to identify any attempts at intrusion owing to the characteristics of quantum states [180]. The exploration of PQC covers its fundamentals and development, focusing on the Kyber encryption algorithm as a leading quantum-resistant solution [181]; it emphasizes the urgency of progressing quickly to effective PQC in response to quantum computing advances, discussing the field’s achievements, challenges, and future directions. Hybrid systems are noted as interim protections, underlining the need for lasting, feasible PQC.

##  7\. Quantum Computing Benchmark Metrics

Benchmark metrics in quantum computing play a pivotal role in evaluating both the performance and capabilities of quantum hardware and algorithms. By providing standardized measures, they enable effective comparisons across different systems and algorithms, thereby fostering progress and innovation within the field. These metrics cover various aspects such as hardware performance, algorithmic efficiency, and practical utility, thus offering a comprehensive framework for evaluation by researchers, engineers, and developers. Key benchmark metrics are outlined briefly, with further details available in [182].

####  7.1. Physical Benchmarks

Gate Fidelity: Gate fidelity in quantum computing pertains to the precision of quantum operations, assessing their execution accuracy relative to their ideal forms. Typically, metrics such as average gate fidelity or diamond norm distance are employed for this evaluation. In [183], the authors accomplished an impressive 99.5% fidelity in deploying two-qubit entangling gates concurrently across up to 60 atoms, exceeding the requisite threshold for error correction via the surface-code technique. This approach entails the utilization of swift, single-pulse gates driven by optimal control, exploitation of atomic dark states to mitigate scattering, and advancements in Rydberg excitation and atom cooling methodologies.

Number of qubits: The quantum computer’s capacity to describe information increases with the addition of qubits. Consequently, the performance of quantum computers is intuitively linked to the number of qubits. Measuring qubits involves sending a microwave tone to resonators and analyzing the reflected signal.

Connectivity: This dictates how qubits can engage with one another, a crucial aspect for conducting quantum operations and executing quantum algorithms. It influences the execution of quantum algorithms, the establishment of quantum entanglement, error correction processes, and the comprehensive scalability, efficiency, and speed of quantum computing systems. When qubits are interconnected seamlessly, they can execute algorithms more effectively, solving problems in fewer steps and maximizing the utilization of the qubits’ limited coherence time. Hence, the enhanced quantum connectivity directly correlates with improved performance.

Relaxation time and Coherence time: The assessment of quantum computer quality can be conducted through the decoherence process, which is characterized by the relaxation time (T1) and coherence time (T2). T1 time elucidates the characteristic of a quantum state decay from a high-energy level state, while T2 time represents a transverse relaxation parameter, encompassing both energy relaxation and pure dephasing effects.

####  7.2. Aggregated Benchmarks

Quantum Volume: This is a comprehensive measure pioneered by IBM, that encompasses various aspects of a quantum computer’s performance, including gate error rates, crosstalk, and coherence times. This metric offers a singular value indicating the system’s capacity to address practical challenges, rendering it a prevalent standard for assessing quantum hardware. A quantum volume of 2n for a processor suggests its capability to accurately execute a square quantum circuit on a designated subset of n qubits using n layers of random two-qubit gates.

Algorithmic qubits: The quantity of algorithmic qubits (AQ) dictates the size of a quantum circuit that can be run on a quantum computer. AQ considers error correction and is closely tied to the qubit count. Essentially, the equation #AQ = N defines the dimension of the largest box, with a width of N and a depth of N², that can be accommodated in this spatial framework. This ensures that every circuit enclosed within this box satisfies the success criteria.

Circuit layer operations per second (CLOPS): The rate of operation is assessed through circuit layer operations per second (CLOPS). This metric considers the interplay between classical and quantum computing since real-world applications involve both forms of processing. CLOPS is defined as the quantity of Quantum Volume (QV) layers executed within a second.

####  7.3. Application-Based Benchmarks

QPack: QPack comprises a comprehensive platform benchmark suite encompassing three common combinational optimization problems: the traveling salesman problem, the dominating set, and the Max-Cut. The objective of QPack benchmark scores is to provide a comprehensive understanding of quantum performance, facilitating swift and straightforward comparisons between various quantum computers [184].

Q-Score: The Q-score evaluates the proficiency of executing a representative quantum application, assessing a system’s capability to tackle real-world challenges rather than solely its theoretical or physical performance. It gauges the practical performance of quantum processors in addressing optimization problems representative of the near-term quantum computing landscape. This score is derived from the maximum number of variables within such problems that a quantum technology can optimize.

Quantum LINPACK: Dong et al. [185] introduced a quantum LINPACK benchmark inspired by classical high-performance computing’s LINPACK benchmark. They devised a random circuit block-encoded matrix to address quantum problems, leveraging quantum algorithms instead of classical ones to solve linear equations. This approach harnesses superposition, quantum parallelism, and entanglement to potentially achieve quicker solutions to linear algebra problems compared to classical methods.

##  8\. Environmental and Sustainability Concerns

####  8.1. Resource Requirements

Environmental and sustainability concerns with quantum computing involve its significant use of resources and ecological footprint, including high energy consumption and material use, which pose unique environmental challenges. As digital silicon computers approach their speed limits, sparking a slowdown in progress, the shift towards quantum computing introduces new environmental challenges, particularly in terms of its substantial energy consumption, among others. The environmental considerations related to quantum computing [186] can be summarized as illustrated in Figure 10. These challenges include the extensive energy requirements for cooling systems to maintain superconducting qubits at near-absolute-zero temperatures, alongside the significant power needed for the operational demands of quantum processors. In response to these concerns, recent developments have focused on designing cryogenic systems, which are essential for the efficiency of solid-state quantum processors [187].

Cryogenic systems aim to significantly reduce both passive and active heat loads, incorporating a wired, thermally optimized dilution refrigerator to ensure rapid qubit control and readout. Such advancements are pivotal for enhancing thermal management within the burgeoning field of large-scale quantum computing, which relies heavily on superconducting circuits. These efforts not only address critical environmental concerns but also mark a significant advancement in the sustainable development of quantum computing technology. Ongoing research aims to balance energy efficiency with computational power in large-scale operations and has identified quantum computing as a potential solution for complex power system challenges. Despite current limitations in the NISQ era, optimism persists that future advancements will enable quantum computing to address these challenges effectively [188]. Quantum computer production relies on rare, specialized materials (e.g., exotic superconductors), posing environmental concerns due to the impact of their extraction, refinement, and processing [189]. Sustainable sourcing faces challenges due to scarcity, geopolitical issues, and environmental costs. However, the development of exponentially more powerful machines offers the potential for significant emissions reductions, making the goal of limiting global warming more achievable [190]. Additional worries stem from the fast-paced advancements in quantum computing, which can potentially cause rapid obsolescence and contribute to a surge in electronic waste [191]. Moreover, the unique characteristics of quantum computing parts could pose challenges in recycling and safely disposing of them, especially for materials that might be hazardous. Quantum computing demands facilities with cryogenic systems and interference-free environments, increasing its environmental impact through construction and maintenance. Researchers are currently exploring various cooling techniques, including advanced cryogenic systems, to promote environmental sustainability in quantum computing [192]. As such facilities increase, urban planning, resource allocation, and environmental assessments will become more critical. Quantum computing could transform battery development and environmental sustainability by simulating quantum-mechanical phenomena for improved battery efficiency and discovering sustainable materials. This technology may also reduce prototype testing and enhance energy density, which is crucial for the shift toward renewable energy. Additionally, it has the potential to optimize energy consumption, advance energy storage, and speed up climate research, as compared to classical computing [193]. The creation of global standards to evaluate and handle the opportunities and risks associated with quantum technology is essential, necessitating the development of regulatory frameworks and policies for its sustainable advancement. Such guidelines would offer a unified set of rules for all involved parties, to steer the technology in a way that benefits humanity [194]. To ensure the sustainable advancement of quantum computing, it is essential to balance technological progress with environmental considerations. Guidelines should prioritize innovation while minimizing ecological impacts. As quantum computing develops, ongoing assessment and mitigation of its environmental footprint are crucial to aligning with broader sustainability goals.

####  8.2. Energy Consumption and Ecological Impact

The ecological impact of quantum computing is complex, as it involves significant energy consumption often from non-renewable sources, thereby increasing its carbon footprint. Quantum computers’ main energy use stems from cooling chips to prevent temperature-induced qubit state changes, with operational energy needs varying by technology and architecture [195]. The supporting infrastructure (e.g., data processing units and environmental controls) also increases the overall energy consumption. Despite their higher energy demands for certain operations, quantum computers offer the potential for greater efficiency in specific tasks compared to classical computers; they can solve problems in significantly shorter times than classical supercomputers, potentially lowering the total energy usage for these computations. Despite facing challenges, the advancements in quantum computing showcase its potential as a significant innovation for the next generation, with broad implications for information systems research and beyond [196]. Furthermore, by contributing to materials science, climate modeling, and optimization challenges (e.g., energy grid management), quantum computing indirectly supports environmental conservation efforts. The merging of semiconductor physics and quantum technology, particularly through investigating Gallium Nitride (GaN) defects, presents a notable opportunity for innovation [197]. These defects, which were once considered mere imperfections, are now seen as key to advancing quantum computing and communication efficiently at room temperature, showcasing their potential to revolutionize quantum systems in practical settings. The transition of quantum computing from research to commercial applications brings its energy consumption and ecological impact to the forefront. Ensuring a balance between technological progress and environmental sustainability is essential, thus necessitating ongoing efforts to improve energy efficiency and mitigate ecological impacts.

##  9\. Future Prospects and Developments

####  9.1. Upcoming Technological Breakthroughs

Quantum computing stands on the brink of a technological revolution, as it is poised to unlock unprecedented computational capabilities and address some of the most intricate challenges facing humanity [198]. This burgeoning field promises not only to transform industries but also to revolutionize the way society solves complex problems, thus demanding a reevaluation of existing ethical and security paradigms. At the heart of this revolution is the quest for scalability, with efforts focusing on augmenting the number of qubits while ensuring their coherence and minimizing errors [199]. Innovations in qubit technology, QEC, and chip design are crucial milestones on the path to creating fault-tolerant quantum computers capable of tackling real-world applications. The integration of quantum and classical computing elements heralds the advent of hybrid systems, which are poised to enhance accessibility and versatility in computing applications [200]. These systems, alongside the development of quantum networks, are setting the stage for a future with secure quantum communication and the potential for a quantum internet, reshaping cybersecurity, and global data exchange. In the scientific realm, quantum simulation is anticipated to drive breakthroughs in materials science and pharmacology, opening doors to novel materials and drug discoveries. Moreover, quantum computing’s foray into machine learning and optimization problems presents new horizons in artificial intelligence, logistics, and finance. This impact of quantum computing in the top six application domains is illustrated in Figure 11.

The journey toward realizing the full promise of quantum computing is both exhilarating and formidable, stretching across decades and necessitating a confluence of expertise from physics, engineering, computer science, and beyond. This endeavor calls for a proactive stance on ethical, societal, and environmental issues to ensure that privacy, security, and sustainability are at the forefront of quantum computing development. Quantum computing not only ignites a global technological race but also offers a unique platform for international research collaboration and standard setting [201]. As we navigate this journey, an emphasis on interdisciplinary collaboration, ethical considerations, and sustainability will be key to unlocking the transformative potential of quantum computing, making it a cornerstone of future scientific, industrial, and societal advancements. This narrative paints a vivid picture of a future where the capabilities of quantum computing are fully harnessed, driving innovation and solving critical challenges across the globe. Developers and end users are increasingly working together to develop specific applications. It is reasonable to anticipate that some applications will be available sooner than others, considering the varying complexities involved. Figure 12 illustrates such a timescale (2024–2029 and beyond) for the development of different business applications [202].

####  9.2. The Roadmap for Quantum Computing Advancements

The advancement of quantum computing is charted through a roadmap of stages, each with significant technological milestones and escalating capabilities. This roadmap, although not uniform across various quantum computing technologies, generally transitions from basic research to practical application. The initial research and proof-of-concept stage involves the development of fundamental quantum mechanics theories and initial quantum algorithms, such as Shor’s and Grover’s algorithms. During this stage, the creation and manipulation of the first qubits demonstrate the basic principles of quantum computing. This phase also includes the development of small-scale quantum computers capable of simple calculations and proof-of-concept demonstrations of quantum supremacy. Progressing further, enhancing coherence time, and developing QEC become pivotal. Extending the time qubits maintain their quantum state before decoherence and refining error correction methods to address inherent quantum computation errors are crucial milestones.

Scaling up qubits is also a significant step, transitioning from systems with tens of qubits to hundreds and then thousands, while maintaining stability and coherence. This stage also focuses on developing technologies for efficient qubit interconnectivity and information transfer.

In the era of NISQ computers, quantum computers operate with fifty to a few hundred qubits [203]. These systems start to perform tasks beyond the reach of classical supercomputers, with explorations of early applications in fields such as materials science and chemistry. The development of fault-tolerant quantum computers involves implementing full QEC to allow continuous operation despite errors. This stage aims to create quantum computers that can dynamically handle errors and maintain coherent states for extended periods. Achieving broad quantum advantage in practical applications marks a pivotal point; this includes consistently outperforming classical computers in various applications and making quantum computers more accessible. Integration and expansion involve integrating quantum computers with classical systems for broader applications and developing quantum communication networks for secure data transfer, possibly leading to a quantum internet. This stage also anticipates widespread adoption of quantum computing across various sectors.

Long-term developments include building large-scale quantum computers for complex, general-purpose computing tasks and using quantum computers for intricate simulations in physics, chemistry, and biology, potentially leading to discoveries and technologies. This final stage envisions establishing a global quantum computing infrastructure, including quantum networks and widespread services. The roadmap for delivering new prototypes, as pledged by the quantum computing ecosystem, is expansive, with new devices anticipated by the respective key players between now and 2030 [202], as illustrated in Figure 13. The figure shows how different vendor technologies have progressed recently concerning physical qubits and how they are projected until 2030.

In conclusion, the quantum computing roadmap is ambitious and encompasses numerous technical and theoretical challenges. However, the potential rewards are transformative, as they promise to advance computational capabilities for real-world applications.

####  9.3. Future Outlook and Recommendations

Quantum computing, marked by its revolutionary approach and initial success with small-scale devices, faces the challenge of scaling up. Achieving a practical, large-scale quantum computer requires major technical breakthroughs, necessitating a strategic blend of current capabilities and long-term ambitions for gradual advancement [4]. Middleware is essential to bridge the gap between hardware manufacturers and software developers, which fosters collaboration. In quantum hardware development, superconducting circuits and trapped ions are key focuses. Superconducting circuits lead in scalability and speed, whereas trapped ions show promise but encounter technical challenges. Breakthroughs, including integrated ion traps and efficient cryogenic contacts, highlight the potential and future growth opportunities of quantum technologies [204].

Strategic investments and interdisciplinary research are crucial for realizing the nearly USD 700 billion value potential predicted by 2035 [205]. Strategic investment data by countries in billions [11], as shown in Figure 14, provides a healthy outlook toward goals set in prototype roadmaps. Achieving stability in quantum computing will unlock a multitude of opportunities and catalyze global technological advancement. Advancements could unlock and expedite progress in critical domains, potentially leading to swift commercialization. As a result, market growth forecasts vary significantly for quantum computing market growth projections [202], as displayed in Figure 15.

##  10\. Conclusions

The field of quantum computing presents significant challenges and opportunities for advancing computational capabilities beyond classical systems. The sensitivity of quantum computers to errors, noise, and decoherence poses substantial obstacles to the development and reliability of their hardware components. Technologies such as superconducting qubits, photonics, and trapped ions, although promising, face issues related to environmental sensitivity, scalability, and integration. Emerging technologies, including diamond color centers, topologically protected systems, and silicon quantum dots, offer potential solutions to these challenges but have yet to be commercialized. To address these issues, a variety of methods and metrics, including quantum process tomography, QEC, quantum benchmarking, and quantum certification, have been employed to test and enhance the reliability and performance of quantum computers. Furthermore, the construction of a quantum computer requires a sophisticated integration of various electronic components, such as qubit integrated circuits (ICs), quantum gate ICs, quantum memory ICs, quantum control unit ICs, and quantum measurement ICs. These components must be precisely designed and implemented to manipulate and measure quantum states effectively. Despite the hurdles, the ongoing research and development in quantum computing hold the promise of revolutionizing computing by harnessing the principles of quantum mechanics, potentially leading to breakthroughs in various fields, including cryptography, materials science, and complex system simulation.

## Author Contributions

All authors have contributed equally. All authors have read and agreed to the published version of the manuscript.

## Funding

This research received no external funding.

## Data Availability Statement

Not applicable.

## Conflicts of Interest

The authors declare no conflict of interest.

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**Figure 1.** Time plot of current advances in classical computing power. 

**Figure 1.** Time plot of current advances in classical computing power.

**Figure 2.** Classical computing bit vs. qubit (can exist in both states). 

**Figure 2.** Classical computing bit vs. qubit (can exist in both states).

**Figure 3.** Various qubits and their pros and cons. 

**Figure 3.** Various qubits and their pros and cons.

**Figure 4.** A quantum processing unit suspended beneath refrigeration setup, maintaining the processor at ultracold temperatures necessary for its operation source: <https://www.research.ibm.com/ibm-q/network/> (accessed on 1 April 2024). 

**Figure 4.** A quantum processing unit suspended beneath refrigeration setup, maintaining the processor at ultracold temperatures necessary for its operation source: <https://www.research.ibm.com/ibm-q/network/> (accessed on 1 April 2024).

**Figure 5.** Challenges faced by quantum computing. 

**Figure 5.** Challenges faced by quantum computing.

**Figure 6.** Illustration of quantum decoherence. 

**Figure 6.** Illustration of quantum decoherence.

**Figure 7.** Surface code for error correction. 

**Figure 7.** Surface code for error correction.

**Figure 8.** Algorithm qubit timeline by the end of the current decade. 

**Figure 8.** Algorithm qubit timeline by the end of the current decade.

**Figure 9.** Quantum computer attack on public key cryptography. 

**Figure 9.** Quantum computer attack on public key cryptography.

**Figure 10.** Environmental considerations in quantum computing. 

**Figure 10.** Environmental considerations in quantum computing.

**Figure 11.** Quantum computing’s impact on the future. 

**Figure 11.** Quantum computing’s impact on the future.

**Figure 12.** QC business application development timescale. 

**Figure 12.** QC business application development timescale.

**Figure 13.** Vendor prototype roadmaps. Legend: Vendor (physical qubits). 

**Figure 13.** Vendor prototype roadmaps. Legend: Vendor (physical qubits).

**Figure 14.** Quantum initiative funding by country (2014–2030). 

**Figure 14.** Quantum initiative funding by country (2014–2030).

**Figure 15.** Quantum computing market growth projections. 

**Figure 15.** Quantum computing market growth projections.

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Memon, Q.A.; Al Ahmad, M.; Pecht, M. Quantum Computing: Navigating the Future of Computation, Challenges, and Technological Breakthroughs. _Quantum Rep._ **2024** , _6_ , 627-663. https://doi.org/10.3390/quantum6040039 

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Memon QA, Al Ahmad M, Pecht M. Quantum Computing: Navigating the Future of Computation, Challenges, and Technological Breakthroughs. _Quantum Reports_. 2024; 6(4):627-663. https://doi.org/10.3390/quantum6040039 

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Memon, Qurban A., Mahmoud Al Ahmad, and Michael Pecht. 2024\. "Quantum Computing: Navigating the Future of Computation, Challenges, and Technological Breakthroughs" _Quantum Reports_ 6, no. 4: 627-663. https://doi.org/10.3390/quantum6040039 

**APA Style**  


Memon, Q. A., Al Ahmad, M., & Pecht, M. (2024). Quantum Computing: Navigating the Future of Computation, Challenges, and Technological Breakthroughs. _Quantum Reports_ , _6_(4), 627-663. https://doi.org/10.3390/quantum6040039 

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**MDPI and ACS Style**

Memon, Q.A.; Al Ahmad, M.; Pecht, M. Quantum Computing: Navigating the Future of Computation, Challenges, and Technological Breakthroughs. _Quantum Rep._ **2024** , _6_ , 627-663. https://doi.org/10.3390/quantum6040039 

**AMA Style**  


Memon QA, Al Ahmad M, Pecht M. Quantum Computing: Navigating the Future of Computation, Challenges, and Technological Breakthroughs. _Quantum Reports_. 2024; 6(4):627-663. https://doi.org/10.3390/quantum6040039 

**Chicago/Turabian Style**  


Memon, Qurban A., Mahmoud Al Ahmad, and Michael Pecht. 2024\. "Quantum Computing: Navigating the Future of Computation, Challenges, and Technological Breakthroughs" _Quantum Reports_ 6, no. 4: 627-663. https://doi.org/10.3390/quantum6040039 

**APA Style**  


Memon, Q. A., Al Ahmad, M., & Pecht, M. (2024). Quantum Computing: Navigating the Future of Computation, Challenges, and Technological Breakthroughs. _Quantum Reports_ , _6_(4), 627-663. https://doi.org/10.3390/quantum6040039 

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