Will pharma discovery be quantum computing’s first killer app?

19 May 2021

A 1923 statistic machine by Fredrik Rosing Bull (L), the BulllSequana X1000 supercomputer (C) and the Atos Quantum Learning Machine (R), at the Atos headquarters in Bezons, near Paris. (Photo by ERIC PIERMONT / AFP)

Pharmaceutical research has always been a growth area, driven by pressing demand for cures and treatments that makes it an extremely lucrative business. With such high stakes, drug companies are always looking for ways to improve efficiencies while reducing costs (who isn’t?), and it appears the emerging field of quantum computing might be helpful in that regard.

In January, the world’s largest private drug producer, Boehringer Ingelheim, revealed that it would work with Google to harness the power of quantum computing in its pharmaceutical research and development (R&D). Shortly thereafter, the world’s largest pharma firm Roche announced it was collaborating with Cambridge Quantum Computing to design quantum algorithms for early-stage drug discovery and development.

According to a report by McKinsey, quantum computers have four fundamental capabilities that differentiate them from today’s classical computers: quantum simulation, in which quantum computers model complex molecules; optimization (that is, solving multivariable problems with unprecedented speed); quantum artificial intelligence (AI), utilizes better algorithms that could transform machine learning across industries as diverse as pharma and automotive; and prime factorization, which could revolutionize encryption.

Using quantum simulation and algorithms to improve the complex and time-consuming simulation models required to test a litany of molecules and chemical reactions, is what makes quantum computing’s application interesting in the drug manufacturing sector. “Google’s view is that chemistry is the near-term application for quantum computing, and I buy that as well,” says Chad Edwards, the head of strategy and product at Cambridge Quantum Computing in England.

Cambridge Quantum Computing does not build or operate quantum computers itself, but instead develops software to match such advanced hardware. “We sit at the interface of big corporations like Roche, that want to use quantum computing but don’t know how to fit it into their organizations, and the IBMs, Honeywells, Microsofts, and Googles of the world, that are not sure how quantum computing might be deployed inside different organizations,” Edwards explains. “We’ve engaged with five of the top 10 pharma companies.”

The main algorithm used in quantum chemistry research is the variational quantum eigensolver, which is “the workhorse everyone in the industry uses now for quantum chemistry,” according to Edwards. But the new algorithm that Cambridge Quantum Computing uses is a more resource-efficient one known as imaginary time evolution, which Edwards says reduces the likelihood of the algorithm pursuing promising solutions that turn out to not be the desired result.

When it comes to what benefits quantum computing might bring to pharmaceutical research, “A lot of people jump to the conclusion that it will be faster than conventional methods,” notes Edwards. “In some cases, it will be, but what we’re really aiming for is a level of accuracy not possible using classical machines.”

This higher threshold for accuracy, invaluable in drug studies, is a big reason why the big pharma companies are no longer hoping to just make better earnings from quantum research. Instead Edwards claims the major pharma firms have banded together in a cabal called QuPharm to work together pre-competitively to advance quantum computing uses for drug production. QuPharm in turn is collaborating with the Quantum Economic Development Consortium (QED-C), which is devoted to helping develop commercial applications for quantum science and engineering.

Besides drug development, quantum research is also seeing more innovation in life sciences R&D and in bioinformatics, grappling with the likes of gene sequencing and gene annotation. “We’re seeing longer-term research that’s dedicating significant resources to quantum computing, typically two to three years in length,” Edwards confirms. “These are much more advanced than proof-of-concept studies. They’ve tested the waters, and now they’re committing to the technology for the long term, which is what’s required for this technology.”