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How to solve difficult chemical engineering problems with quantum computing

Leading quantum chemist and a manager in the IBM Quantum Computational Science group, Gavin Jones sits down to discuss how quantum computers could be used to solve some of the most difficult questions in chemistry.

Gavin Jones on quantum computing solutions to hard chemical engineering problems

7 Mar 2023

Alex Lukin

IBM and JSR chart a new future for the global semiconductor industry, with quantum computing solutions to hard chemical engineering problems. Read the case study.

Gavin Jones is a leading quantum chemist and manager in the IBM Quantum Computational Science group, as well as an IBM Quantum Technical Ambassador. In his research, Jones explores chemistry with quantum computers, such as the formation of functional advanced materials, catalysis, molecular properties, and polymer degradation.

Jones brought this expertise to a recent case study with JSR, one of the leading global manufacturers of photoresists — crucial chemical solutions for microchip production worldwide. These solutions are expensive and time-consuming to develop, and the chemistry involved is too complex for even the most powerful classical supercomputers to accurately simulate. Research suggests that quantum computers could help remove barriers of complexity and streamline this process.

Together with JSR, Jones and his team have already shown that quantum computers can simulate small molecules that mimic parts of a photoresist. We met to discuss his own background, the significance of the JSR work, and what it is that makes chemistry “quantum.”

Gavin Jones is a leading quantum chemist and manager in the IBM Quantum Computational Science group, as well as an IBM Quantum Technical Ambassador.

Dr. Gavin Jones, a leading quantum chemist and manager in the IBM Quantum Computational Science group, and IBM Quantum Technical Ambassador.

I've heard that “chemistry is quantum.” What does that mean — and why is that relevant to quantum computing?

It means that, at an atomic level, molecules behave according to quantum mechanical principles. Some examples of this are that the particles that make up these molecules (for example, electrons) sometimes act like waves spread out in space. But if the object interacts in specific ways with other particles, it loses its wave-like properties and starts acting like a discrete point — like a particle.

You can also use an equation like the Schrödinger equation — which governs the wave function for a quantum mechanical system — to predict the behavior of a chemical system. For example, you can predict energies of molecules and their properties. You can also do useful tasks such as predicting the likely outcomes of chemical reactions.

You just co-authored a paper with JSR Corporation that “simulated a molecule with similar behaviors to a PAG.” What are PAGs, and what do we gain from efforts to simulate them accurately?

PAGs are photo acid generators. These are important molecules in industrial applications such as photolithography or photopolymerization. In photolithography, for example, researchers use light to pattern thin films of polymers over some type of substrate, such as a silicon wafer, to protect selected areas of it during subsequent etching, deposition, or implantation steps. The photoresist either breaks down or hardens where it is exposed to light. The patterned film is then created by removing the softer parts of the coating with appropriate solvents. We require this process for creating microchips.

IBM researchers are racing to create more sustainable PAGs, turning to AI to help create them, faster, paving the way to the era of Accelerated Discovery. Read more.

What is special or new about what you and your collaborators have accomplished with this new paper? Where do you hope to go next?

In my opinion, this is the most advanced chemical simulation done on a quantum computer to date. It’s one of the largest systems that has ever been simulated, and the type of simulation that we’ve done is more complex than anything that has ever been done before. As a proof of concept, this was one of the most ambitious things that has come out in recent chemistry literature.

But we still need to scale up. So far, quantum computing research for chemistry has focused on toy problems of maybe three or four atoms. The problems that we are going to be looking at in the very near future will require technologies that will scale to much larger systems. So that’s the focus of the next adventure. We are trying to develop techniques that will let us increase the size of the simulations so we can explore more industrially relevant simulations of battery materials, OLEDs, crystalline materials, biomolecules and so on.

When did you first become interested in chemistry, and what is it about the field that captured your interest? What about quantum computing?

I loved math growing up, and I’ve had an interest in chemistry ever since high school, when I realized that chemistry requires logical reasoning similar to the logic someone would employ in mathematics. I hated to just memorize stuff, and was really attracted to the fact that there is a logic underpinning chemistry. I thought it was cool that someone could employ such logic to figure out how actual reactions work that make interesting and useful materials in the real world. From then on, from high school to post-doc work and now at IBM, that’s been my main interest — especially the theoretical side.

Today, there are actual chemists in the building who rely on some of the things that we predict to actually go into the lab and try stuff, who then come out and say “oh, based on your predictions, this is what we’ve made.” I find that absolutely amazing and it makes for an exciting work environment.

As for quantum computing, I only started learning about it maybe eight years ago. I heard about it from a senior manager at the time who mentioned its potential for quantum chemistry. As a computational chemist I have always wished for a technique that could provide more accurate predictions than predictions I can obtain on a classical computer. So when I heard that quantum computers could potentially help us to achieve this goal, I became intensely interested and wanted to jump on the bandwagon. I eventually did and I haven’t looked back.

Today, I manage a team of researchers that I'm very happy to support as we work to bring quantum computing to life. I’m hopeful that together with IBM Quantum, my team can use quantum to retrieve more accurate answers and produce more accurate simulations for chemistry.

If you could go back in time and talk to yourself as a young undergraduate chemistry student, what advice would you give him?

I think when you’re an undergraduate and even in high school you naturally have some anxiety for your future. But I think that if you have certain tools, or a certain mindset, it gets you very far. I’d tell myself (or anyone in that position): Don’t stop being curious and don’t stop seeking out new opportunities.

As I mentioned previously, I had no idea about quantum computing until fairly recently. After I heard about it, I wanted to find out as much as possible about the capabilities. I was excited that I had the chance to learn something new and figure out how I could contribute to the field. No matter what your interests are, you should be able to say “I’m curious. I want to learn more. I want to know as much as possible.” If you’re interested — then go after it.

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