Eighty Years of Boolean Thinking Is the Real Barrier to Quantum Adoption

The hardware is not the hard part for enterprises. Recasting business problems in quantum terms means unlearning assumptions about problem solving that nobody realizes they are carrying.

By Sebastian Hassinger, author of The New Quantum Era and former quantum lead at AWS and IBM | This piece is adapted from his live Deep Engineering session, Quantum Computing Beyond the Hype. Edited by Saqib Jan

It is smart for enterprises to start investing in the skills you need for understanding quantum information, and in exploring potential algorithms now, even though the computers that could actually run anything useful do not exist yet. That sounds premature until you look at what the work actually involves, because it takes a great deal of effort to recast a business problem into quantum terms.

We have been thinking about these problems in classical terms, in Boolean logic terms, since the middle of the last century. That is seventy or eighty years now, and it will be a hundred before too long. So we are carrying a very deeply ingrained set of preconceptions about problem solving. We look at our world through the lens of how our laptop could fix the problem in front of us. We write code on those machines, and our minds run along the lines of decomposing the problem, working out what the dynamics are, and figuring out how to represent them efficiently. All of that rests on an invisible reliance on the assumption that at the ground level you are doing Boolean algebra to solve the problem.

I do not think many people are fully aware of how much of their problem solving thinking is rooted in the way classical computing works. And it is so different in quantum computing that the gap becomes the real obstacle. People talk about developing quantum intuition, which means building the habit of looking at the world through the lens of linear algebra and through the dynamics and capabilities of quantum information. That takes a lot of work, and it is not the kind of work you can compress once the hardware arrives.

The smartest enterprise approaches to quantum computing I have seen understand this. They hire a small number of strong people and run research alongside quantum hardware companies and academic researchers at the top of the field. JPMorgan does this well. You can imagine an organization that size has a very large set of challenges, algorithms, and tasks it has to carry out to operate as a financial entity, and the team there looks at theoretical problems that have some mapping to an aspect of those business processes, then does exploratory research against them. They publish open science papers, so everybody benefits from the work they put in.

What they are really doing is building the muscle. When quantum technologies mature to sufficient scale, JPMorgan will know how to use them, because the people there will already have years of thinking in the right terms behind them. That head start cannot be bought later. The hardware will become available to everyone at roughly the same moment, and the differentiator will be whether your organization has anyone who can look at a business problem and recognize the high dimensional structure inside it.

Two things follow from this if you are deciding where to put effort right now. Pick one or two people who are genuinely curious and give them real time to build quantum intuition, rather than sending the whole team to an introductory session that changes nothing. And point them at a problem you actually have, something with heavily interconnected variables, so the learning attaches to your business instead of staying abstract.

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Read the full issue

This piece comes from a longer conversation on how to read quantum progress honestly. The complete interview and the rest of this week’s Deep Engineering issue are available here.