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Is Quantum Artificial Intelligence Close? Understanding The Challenges of Quantum AI

Employee using AI computing simulation

A recent Forbes article got the quantum community’s dander up — and I’m not even sure what dander is.

The article — Quantum Artificial Intelligence Is Closer Than You Think — claims that quantum AI is imminent and it’s transformative power will soon be realized.

While it’s important to maintain enthusiasm and it’s completely understandable to be excited about the possibilities of quantum AI, timelines — short or long — are historically problematic to make about scientific progress, particularly progress on AI — and forget about predicting progress on quantum AI.

We’ll try to break down the argument about quantum AI’s imminent arrival with some real challenges that could temper the “closer than your think” prediction.

First, the pace of AI advancement, while impressive, is not solely contingent on processing power. AI also requires vast amounts of data for training, and the development of algorithms that can leverage quantum computing is still in its infancy. The notion that AI will be ‘supercharged’ by quantum computing presupposes that quantum computers will soon be capable of running these algorithms efficiently, which is currently not the case.

Further, quantum computers excel at solving particular types of problems, but they are not universally superior — nor are they expected to be — to classical computers for all tasks. Therefore, the transformative impact of quantum computing on AI may be more nuanced and specialized than the broad revolution implied.

Maybe generative AI has triggered some of this excitement. Indeed, generative AI has absolutely demonstrated remarkable capabilities, but its practical applications are still being explored and understood. The history of technology is littered with examples of innovations that promised to revolutionize the world but instead found a more modest place within it. This is not to understate the potential of quantum AI, but to acknowledge that its integration into the fabric of society and business often takes longer and is more complex than initial projections suggest.

As for quantum computing, while strides have been made, it remains a technology that is largely experimental and not ready for widespread practical application. Quantum computers are prone to errors and require conditions that are difficult to maintain, such as extremely low temperatures. They are also extraordinarily expensive and complex to operate, which will likely limit their accessibility and integration into mainstream business operations in the near term. In other words, to get to quantum AI, we just need quantum.

Let’s look beyond the technological hurdles. There are ethical, legal, and socio-economic considerations that also play a significant role in the adoption of new technologies. Quantum AI’s impact is as much about governance, trust, and accessibility as it is about technical capability.

Science is often caught between cynicism and hype, and this is certainly not meant to be a blanket statement against the prospects of quantum AI. The potential for quantum AI is there and scientists and entrepreneurs are busy bringing it into fruition. It’s also true that machine learning can benefit quantum computing right now. For example, scientists are using machine learning techniques to find new quantum algorithms and optimize quantum operations. Researchers are also using machine learning to improve error-correction for quantum computing.

Could there be a breakthrough to shorten this timeline? Most people didn’t see the breakthrough potential of large language models, so scientific leaps should not be ruled out.

However, while the potential of quantum computing to accelerate AI is indeed a fascinating prospect, it is essential to recognize the current state of quantum technologies. As of now, they are not poised to catalyze a new computing revolution within the next decade; rather, they represent a long-term aspirational goal. The research community is still grappling with fundamental questions about how to make quantum computers reliable, scalable, and useful for a broad range of applications.

We can hope quantum AI is closer than we think, but we should probably think it’s not as close as we hope.

The Future of Materials Discovery: Reducing R&D Costs significantly with GenMat’s AI and Machine Learning Tools

When: July 13, 2023 at 11:30am

What: GenMat Webinar

Jake Vikoren

Jake Vikoren

Company Speaker

Deep Prasad

Deep Prasad

Company Speaker

Araceli Venegas

Araceli Venegas

Company Speaker

Matt Swayne

With a several-decades long background in journalism and communications, Matt Swayne has worked as a science communicator for an R1 university for more than 12 years, specializing in translating high tech and deep tech for the general audience. He has served as a writer, editor and analyst at The Quantum Insider since its inception. In addition to his service as a science communicator, Matt also develops courses to improve the media and communications skills of scientists and has taught courses. [email protected]

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The Future of Materials Discovery: Reducing R&D Costs significantly with GenMat’s AI and Machine Learning Tools

When: July 13, 2023 at 11:30am

What: GenMat Webinar

Jake Vikoren

Jake Vikoren

Company Speaker

Deep Prasad

Deep Prasad

Company Speaker

Araceli Venegas

Araceli Venegas

Company Speaker

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