When Lawrence Gasman was looking for a PhD topic back in the 1970s, computing labs were already abuzz with smart people proposing clever studies in artificial intelligence. “But the problem was we had nothing to run them on,” he says. “The processors needed just didn’t exist.”
It took half a century for computing power to catch up with AI’s potential. Today, thanks to hi-powered chips such as GPUs from California-based Nvidia, generative artificial intelligence, or gen AI, is revolutionizing the way we work, study, and consume entertainment, empowering people to create bespoke articles, images, videos, and music in the blink of an eye. The technology has spawned a bevy of competing consumer apps offering enhanced voice recognition, graphic design, and even coding.
Now AI stands poised to get another boost from a radical new form of computing: quantum. “Quantum could potentially do some really remarkable things with AI,” says Gasman, founder of Inside Quantum Technology.
Rather than relying on traditional computing’s binary “bits”—switches denoted as 1s and 0s—quantum use multivariant “qubits” that exist in some percentage of both states simultaneously, akin to a coin spinning in midair. The result is exponentially boosted computing power as well as an enhanced ability to intuitively mimic natural processes that rarely conform to a binary form.
Whereas gen AI’s consumer-targeted applications have made its impact more widespread and immediate, quantum is more geared towards industry, meaning several recent milestones have slipped under the radar. However, they stand to potentially turbocharge the AI revolution.
“Generative AI is one of the best things that has happened to quantum computing,” says Raj Hazra, CEO of Colorado-based quantum start-up Quantinuum. “And quantum computing is one of the best things to happen to the advance of generative AI. They are two perfect partners.”
Ultimately, AI relies on the ability to crunch huge stacks of information, which is where quantum excels. In December, IBM unveiled its latest processor, dubbed Heron, which boasts 133 qubits, the firm’s best ever error reduction and the ability to be linked together within its first modular quantum computer, System Two. In addition, IBM unveiled another chip, Condor, which has 1,121 superconducting qubits arranged in a honeycomb pattern. They’re advances that mean “now we’re entering what I like to call ‘quantum utility,’ where quantum is getting used as a tool,” Jay Gambetta, vice-president of IBM Quantum, tells TIME.
Since qubits are incredibly delicate subatomic particles, they don’t always behave in the same way, meaning quantum relies both on increasing the overall number of qubits to “check” their calculations as well as boosting the fidelity of each individual. Different technologies used to create a quantum effect prioritize different sides of this equation, making direct comparisons very tricky and enhancing the arcane nature of the technology.
IBM uses superconducting qubits, which require cooling to almost absolute zero to mitigate thermal noise, preserve quantum coherence, and minimize environmental interactions. However, Quantinuum uses alternative “trapped-ion” technology that holds ions (charged atoms) in a vacuum using magnetic fields. This technology doesn’t require cooling, though is thought to be more difficult to scale. However, Quantanium in April claimed it had achieved 99.9% fidelity of its qubits.
“The trapped ion approach is miles ahead of everybody else,” says Hazra. Gambetta, in turn, argues the superconducting quantum has advantages for scaling, speed of quantum interactions, and leveraging existing semiconductor and microwave technology to make advances quicker.
For impartial observers, the jury is still out since the raft of competing, non-linear metrics render it impossible to tell who’s actually ahead in this race. “They are very different approaches, both are showing promise,” says Scott Likens, global AI and innovation technology lead for the PwC business consultancy. “We still don’t see a clear winner, but it’s exciting.”
Where Gambetta and Hazra agree is that quantum has the potential to mesh with AI to produce truly awesome hybrid results. “I would love to see quantum for AI and AI for quantum,” says Gambetta. “The synergies between them, and the advancement in general in technology, makes a lot of sense.”
Hazra concurs, saying “generative AI needs the power of quantum computing to make fundamental advances.” For Hazra, the Fourth Industrial Revolution will be led by generative AI but underpinned by the power of quantum computing. “The workload of AI and the computing infrastructure of quantum computing are both necessary.”
It’s a view shared across the Pacific in China, where investments in quantum are estimated at around $25 billion, dwarfing the rest of the world. China’s top quantum expert, Prof. Pan Jianwei, has developed a Jiuzhang quantum computer that he claims can perform certain kinds of AI-related calculations some 180 million times faster than the world’s top supercomputer.
In a paper published in the peer-reviewed Physical Review Letters journal last May, Jiuzhang processed over 2,000 samples of two common AI-related algorithms—Monte Carlo and simulated annealing—which would take the world’s fastest classical supercomputer five years, in under a second. In October, Pan unveiled Jiuzhang 3.0, which he claims was 10 quadrillion times faster in solving certain problems than a classical supercomputer.
Jiuzhang utilizes yet a third form of quantum technology—light or photons—and Pan is widely lauded as China’s king of quantum. A physics professor at the University of Science and Technology of China, Pan in 2016 launched Micius, the world’s first quantum communication satellite, which beamed entangled photons between earth a year later for the world’s first quantum-secured video call.
Micius is considered quantum’s “Sputnik” moment, prompting American policymakers to funnel hundreds of millions of dollars into quantum information science via the National Quantum Initiative. Bills such as the Innovation and Competition Act of 2021 have provided $1.5 billion for communications research, including quantum technology. The Biden Administration’s proposed 2024 budget includes $25 billion for “emerging technologies” including AI and quantum. Ultimately, quantum’s awesome computing power will soon render all existing cryptography obsolete, presenting a security migraine for governments and corporations everywhere.
Quantum’s potential to turbocharge AI also applies to the simmering technology competition between the world’s superpowers. In 2021, the U.S. Commerce Department added eight Chinese quantum computing organizations to its Entity List, claiming they “support the military modernization of the People’s Liberation Army” and adopt American technologies to develop “counter-stealth and counter-submarine applications, and the ability to break encryption.”
These restrictions dovetail with a raft of measures targeting China’s AI ambitions, including last year blocking Nvida from selling AI chips to Chinese firms. The question is whether competition between the world’s top two economies stymies overall progress on AI and quantum—or pushes each nation to accelerate these technologies. The answer could have far-reaching consequences.
“AI can accelerate quantum computing, and quantum computing can accelerate AI,” Google CEO Sundar Pichai told the MIT Technology Review in 2019. “And collectively, I think it’s what we would need to, down the line, solve some of the most intractable problems we face, like climate change.”
Still, both the U.S. and China must overcome the same hurdle: talent. While only a few universities around the world offer quantum physics or mechanics, dedicated courses on quantum computing are even rarer, let alone expertise on the various specialties within. “Typically, the most valuable and scarcest resource becomes the basis of your competitive advantage,” says Hazra. “And right now in quantum it’s people with that knowledge.”
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