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.

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.

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.

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