AI and quantum computing get grouped together constantly. They appear on the same conference agendas, in the same conversations about the future, and often in the same sentence. I understand why. Both technologies are advancing quickly, and both will change how we work. After spending most of my career in quantum physics, though, I think we’re missing one of the more interesting parts of the story. I’ve spent years working on problems that conventional computers weren’t well suited to solve. There is a particular frustration in knowing the question you want to ask but realizing that the tools available to you can’t realistically get you to an answer. At first, you work around the limitation. Eventually, you start choosing problems that fit the tools you have. Our tools determine how quickly we work but also influence which problems we decide are worth taking on. ANOTHER TOOL TO WORK WITH This pattern is much older than computing. Scientific progress has always depended partly on the instruments available to us. The microscope opened a world that researchers couldn’t see before. It changed the questions they could ask because suddenly there was something new to observe. Computing has its own boundaries. Classical computers are extraordinary machines, and they will remain essential. There are certain problems, however, where complexity grows so quickly that adding more conventional computing power eventually stops being a practical answer. I ran into that boundary repeatedly as a researcher. Quantum computing gives us another tool to work with. We are still early, but there are already areas, particularly complex optimization, where quantum approaches are being applied to problems that become increasingly difficult as the number of variables and constraints grows. That is what makes this moment so interesting to me. FASTER VERSUS FURTHER AI is already changing how people work. It can help researchers work through enormous amounts of information, identify patterns, and shorten the path from a question to a useful answer. Those capabilities are significant, and they’re improving quickly. I’ve sometimes described the distinction this way: AI can help us move faster. Quantum could help us go further. By further, I mean reaching problems that have been difficult to approach with the computing tools available to us. That’s what interests me. New tools can expand the range of work we decide is possible. I think about that because so much of today’s technology conversation is framed around what machines will do to human capability. The anxiety is understandable. People are watching technology change jobs and skills in real time. WHAT NEW TOOLS ALLOW We should also be talking about what new tools allow people to attempt. For me, that is the more compelling quantum story. Imagine a pharmaceutical researcher who can explore a problem that previously became too computationally complex. An engineer who can consider many more variables in a design. A logistics team able to approach a routing problem differently as the number of constraints grows. None of those outcomes is guaranteed because quantum technology exists. The technology has to become practical. It has to perform. It has to prove its value against the best alternatives already available. There is a long way to go. I believe the larger opportunity is worth paying attention to because I’ve spent a career experiencing the other side of it. Classical computing put a ceiling on certain kinds of problems. Over time, we adapted to that ceiling. We learned to work within it. And in some cases, we stopped asking certain questions. That may be the greatest promise of quantum computing. It gives us a reason to revisit some of those questions and to imagine new ones. The limits of our tools eventually become the limits of our ambition. Change the tools, and we may be surprised by what we decide is worth attempting. Not just faster, but further. Yuping Huang is chairman and CEO of Quantum Computing Inc.
Fast Company
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