Materials Science Gets Real Value From Quantum Before Anything Else

Small molecule chemistry follows close behind. Optimization, cryptography, and machine learning all need thousands of logical qubits, so those sit much further out.

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

People list the same five domains whenever they ask where quantum computing will actually pay off, so chemistry, materials, optimization, cryptography, and machine learning. Of those, materials science is closest to real value, with small molecule chemistry a close second, and small molecule chemistry is sometimes treated as a type of materials science anyway. Optimization, cryptography, and machine learning all need thousands of logical qubits, so those are considerably further off.

The reason materials leads is that materials science is condensed matter physics. It is about how atoms pack together into a lattice, into a crystalline structure, and how the material behaves as a result of that packing. All of that comes down to physics calculations, which is precisely the kind of work a quantum computer should do well. There is already interesting research going on around battery technology, using quantum information and quantum computing to help with designing and inventing new battery chemistries.

What sits behind that work is more ambitious. The aspiration is that if we can simulate materials precisely enough at sufficient scale, we can start creating designer materials. Maybe something much lighter for the same strength, or much stronger for the same weight. Maybe photosynthesis built into the material itself, so you coat your car in something that generates electricity without any cells on the outside. The imagination gets stimulated by the idea of engineering the attributes of a new material at atomic scale, and that is one of the genuinely underestimated parts of this whole story.

Chemistry follows for the same underlying reason, because chemistry is quantum mechanical at its core. It is the way atoms interact with one another, and that interaction is a quantum mechanical phenomenon happening at scale, which is what makes it so difficult to simulate precisely. People talk about small molecule chemistry as the next frontier beyond materials, and there is a natural leap from small molecule chemistry to pharmaceuticals, which tend to be larger and more complex molecules. You need a bigger machine to simulate them.

Provided we can build one large enough, you can easily imagine drug discovery and research happening inside a very high fidelity simulation, with all the acceleration we are used to getting from computer simulation. The pipeline could compress quite a bit, because you might screen a whole set of drug candidates in simulation before you ever formulate anything, and by the time you are making the drug in the real world you already know how it will interact with the human system.

The word doing the work in all of this is fidelity. Classical simulation of chemistry is approximate by necessity. Methods like DMRG throw away a great deal of information about the reaction because there is simply too much to calculate, so we have devised ways to focus algorithmically on the small slice of the problem we hope matters most to the answer. The promise of quantum computing is not throwing away as much, and getting a simulation that captures more of the real dynamics of the system accurately.

So if you are working out where to point attention, watch the physical sciences rather than your own industry for the first real result. And treat any near term claim about quantum optimization, quantum machine learning, or breaking encryption with the logical qubit count in mind, because those applications are waiting on hardware that does not exist yet.

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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.