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How Quantum Computing Trends Will Impact Pharma in 2024: Q&A With Erik Huestis, Partner at Foley Hoag


Huestis discusses the ways that quantum simulation and quantum systems can be used in areas such as drug discovery and the creation and protection of intellectual property in the life sciences industry.

Erik Huestis

Erik Huestis
Foley Hoag

Erik Huestis, a partner at the law firm Foley Hoag, serves as co-chair of the firm’s technology industry group. He recently discussed how quantum computing may be used in the coming year by the life sciences and biotech industries.

Pharmaceutical Executive: What are the benefits of using quantum computing over classical computing for the pharma industry?

Erik Huestis: The exciting thing about quantum computing, as compared to classical computing, is that it is inherently easier to simulate quantum systems using quantum systems. Particularly in the near term, quantum simulation is what’s most exciting to me. The application in biotech is that it’s extremely computationally prohibitive to compute attributes of molecules, of complex compounds in a classical regime, but quantum simulation and universal quantum computing allows us to compute those properties of pharmaceutically interesting molecules on a reasonable time scale with reasonable precision. That feeds directly into the discovery pipeline.

PE: How can innovators in biotech create and protect quantum intellectual property?

Huestis: In biotech in particular, the focus is going to be on the algorithm side, as opposed to the hardware side. The average biotech isn’t developing new lasers or chips, but they are in a unique situation to create new algorithms, both purely-quantum and quantum-classical hybrid.

On the purely quantum side, optimized algorithms for predicting the kinds of properties that a pharmaceutical company cares about is an interesting avenue. More broadly, there are some really interesting avenues combining quantum computing into hybrid solutions. For example, bringing to bear these quantum algorithms that have an advantage of classical algorithms as part of an end-to-end artificial intelligence driven development pipeline.

It's very tempting to conflate quantum computing and AI, but they’re very different. There is a whole suite of problems, however, that quantum computing is really good at solving that work nicely into broader AI systems. When I think of a biotech and protection opportunities, my mind goes to what kind of system architectures combining quantum computing and drug discovery and screening processes are being solved.

PE: Will 2024 be the year that quantum computers break-through, similar to how AI performed in 2023?

Huestis: It’s important to be a little more fine grained about that analysis. In AI, 2023 was the year people became aware of generative. That technology isn’t new, however, and has been simmering for quite some time. AI has been deployed in a variety of other fields for quite a while.

Quantum simulation could be really important in the next year. Are we going to achieve a error-resistant, gate-based quantum computer next year? No. That’s further down the road. There are very interesting near-term applications in quantum simulation or analog quantum computation that I do think will have a significant impact in 2024, even while we’re waiting for continued advancements in error-correction, gate-based digital quantum computers.

PE: Can you discuss digital twins in quantum computing?

Huestis: Digital twin is a catch all phrase regarding the simulation of a patient or a process. It meshes really nicely with the idea of quantum simulation. It’s the idea that in the absence of being able to perform direct measurements of a slew of compounds, we can do some quantum simulation that illuminates the molecular properties of compounds of interest very rapidly.

In that respect, I suppose that quantum simulation is kind of a form of digital twin. More generally, I don’t think quantum computing is that suited to the kinds of broader applications that come to mind, such as looking at patient data, hospital systems, or those sorts of things in a broad way. It turns out that classical computing and conventional AI models work really well for that kind of thing.

But for anything that has a quantum element, digital twinning speaks to the strength of quantum computation as a platform.

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