Lawrence Gasman, President, LDG Tech Advisors.
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Entire libraries can be filled with articles about quantum computing and AI working together—“quantum AI." The pieces often seem to be exercised in handwaving, with little said about what quantum AI can achieve in business and how quantum can be profitably implemented. Yet quantum AI is already proving useful in applications where ultra-fast processing of large, complex datasets is required and applications are steadily arriving.
My journey in quantum AI began back when I studied quantum physics and was considering doing a Ph.D. in AI. These days, I am finishing up writing an AI book on AI while running quantum technology conferences with strong coverage of quantum AI. AI did not take off because computers were too slow to run it for many applications—quantum computers can make that happen.
Accelerating Time To Market
There is increasing use of quantum computing in the booming AI industry itself. Quantum computers build better AI products faster.
Goldman Sachs says as AI penetrates commerce and industry, it could raise global GDP by 7% over a 10-year period. For this to happen, the AI industry requires a productivity boost. Quantum AI may be just the tool to provide this. Near-term benefits include speeding up the training of large language models (LLMs) like ChatGPT. LLMs perform many tasks but need training on massive data sets.
Using classical computers, this task can take weeks, but timeframes are reduced to hours using quantum AI. Both time to market and energy consumption can then be reduced. While the AI industry may be the first sector to benefit from quantum AI, one can see how the need to speed up the delivery of other products, with proven demand but long times to market, could lead to the use of quantum AI.
Simulation Of New Products
New drugs are the lifeblood of the pharma industry, yet pharma is under threat from patent expiration, which will weigh heavily in the 2020s. Although generics will appear, a slew of top drugs with patents expire before the next decade.
Quantum AI may be most valuable in the pharma industry for simulations of potential blockbuster drugs. Some drug and biotech companies (e.g., Roche, Amgen and Boehringer Ingelheim) already use quantum computing in drug discovery, although AI is often not involved. Conversely, the use of AI in drug discovery need not include quantum computing.
A recent announcement from ZapataAI, Insilico Medicine, Foxconn and the University of Toronto points the way. These companies have explored using hybrid quantum-classical generative adversarial networks (GAN)—an AI technology—for small molecule discovery. These quantum-AI-generated molecules are superior to those generated by purely classical GANs. They are more soluble and synthesizable than those produced with classical GANs.
The use of quantum AI may also find its way into searching and analyzing massive collections of health records to provide valuable insights for drug designers. Recent research by IBM suggests that practical drug design is already within the capabilities of today’s quantum computers. Quantum-AI thinking can be extended to similar areas, such as designing advanced materials for new kinds of batteries and sustainable industrial coatings.
Quantum AI Optimization For Financial Services
Optimization was one of the first functions for which quantum computing was used. This currently seldom involves AI. But the potential is huge. An example is portfolio optimization in banking and financial services, where AI is utilized to replicate high-performing portfolios discovered by the AI algorithm. The claim is that resulting portfolios are much lower risk than those constructed by conventional approaches. Data from this process becomes quite complex. AI plus quantum computing makes the most profitable portfolios as a result.
Quantum AI’s optimization potential goes further. Consider traffic management in the autonomous vehicle era. Quantum AI is appropriate—it enhances safety and is also practical since cars are forever spewing data that can be utilized in optimization. Other applications in which quantum-AI-driven optimization will appear include telecommunications, logistics and complex designing such as city planning.
Making Quantum AI Happen
Keeping up with technology is essential for business success. In the financial sector, banks and others are aware they need to be innovative in technology deployment; other financial institutions can easily copy them. Online banking was critical to the financial sector's competitiveness in the 1990s, and quantum AI-enabled services may be in the coming decade.
Most firms lack the financial resources and trained workforce to support on-premises quantum computers. But note that access to quantum computing over the cloud is available and is relatively inexpensive. And the cost of quantum computing will decline as lower-cost alternatives to superconducting machines become available—IonQ’s trapped-ion technology—springs to mind.
The first step for leaders to implement quantum AI is to conduct an internal brainstorming session about the possible benefits of quantum AI and plan a possible timeframe and roadmap. Without their own internal quantum team, leaders should then talk to a specialized quantum software provider about turning these thoughts into a deployment plan. The next step is running a pilot program—probably for a year or so—to see if the hoped-for benefits manifest themselves.
It is early days for quantum AI, and for many organizations, quantum AI right now might be overkill. In any case, quantum AI is not without its limitations. Quantum-AI-designed drugs still need to go through extensive trials. Quantum-optimized portfolios need to be tested before investors will trust them.
Conclusion
Despite the extra risks and costs, industries acknowledge that quantum AI can provide the right data faster. The quantum software company Zapata has recently become ZapataAI. Google spinout SandboxAQ says that it leverages the effects of both AI and quantum. Also, major quantum industry conferences, such as the Inside Quantum Technology events, are devoting panels and talks to Quantum AI.
The natural fit between quantum computing and AI is already yielding results, and I believe it will yield more. We stand on the verge of an applications revolution—a revolution where quantum computers provide the brawn while AI provides the brain.
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