Realizing The Value Of Quantum Computing In Life Sciences

By Roger Frechette

Gartner predicts that by 2023, 20 percent of organizations will be budgeting for quantum computing projects (versus less than 1 percent today). In the life sciences sector, it holds great potential for transforming data-heavy processes, from accelerating drug discovery to simulating clinical trials, and is one of the key technological areas we predict will impact life sciences for the better in our recent report, 2030 — Life Sciences And Health Go Digital.

While traditional computers encode data in “bits” — measurements that are either one or zero — quantum computers use “qubits.” These can be both one and zero simultaneously, or somewhere in between, allowing a quantum computer to consider all possible combinations of a situation at once, rather than testing each individually until all are ruled out.

Today, many life sciences organizations are talking about quantum computing but don’t know what they can actually do with it — much like where the industry was, quite recently, when it came to AI. If quantum computing is really going to transform the industry, organizations must collaborate, share their ideas, and talk openly about their interests. Otherwise, as we experienced in the early days of AI adoption, we’ll see a lot of money spent on projects that don’t produce robust outcomes, make common errors, and aren’t interoperable with the efforts of colleagues and peers.

SUCCESS TO DATE

As with any technology-driven initiative in life sciences and healthcare, the quality of data is fundamental to its success. The more precompetitive data that can be shared across the whole industry, the better the outcome for R&D — particularly when applying quantum computing. To date, there have been some exciting examples of quantum computing in action that show how the broader industry can benefit, including:

• Enhancing drug discovery: Quantum computers can simulate how atoms and molecules behave under certain conditions far more accurately than traditional computers. This info can be used to help design next-generation pharmaceuticals.
• Simulating clinical trials: Many studies have shown how computational processes can simulate clinical trials, reducing the need for human participation. Quantum computing could take this further, simulating multiple variables such as electrolytes, hormones, and bodily fluids that traditional computing simply can’t replicate, making clinical trials shorter and introducing drugs into the market quicker.
• Advancing existing technologies: Powering technologies such as AI with quantum computing can massively increase the scope of feasible molecular comparisons for in silico therapeutics screening and can also reduce diagnostic imaging errors. The European Commission, for instance, is currently funding a project that uses diamond quantum dynamics to improve cardiac imaging.

THE ROAD AHEAD

Further advances are exactly what the industry needs if quantum computing is to reach its full potential. However, exploratory projects often are undertaken within individual organizations, which do not then share their knowledge or skills and lack the variety and depth of data needed. Private companies naturally have some reticence about sharing even precompetitive data, although we are certainly seeing a change in this attitude.

Ultimately, substantial hurdles can be cleared if organizations work together and are open about how they’re envisioning quantum computing applications — or indeed what challenges they face. For example, although many life sciences organizations employ data science experts, such as bioinformaticians and cheminformatics professionals, building quantum computers is a highly specialized task. Organizations should therefore collaborate to gain access to a greater variety and depth of data, and also work with companies that do employ specialists, providing greater access to skills, talent, and the platforms needed to conduct quantum computing- based experiments.

ROGER FRECHETTE has more than three decades’ experience working in life sciences. As a consultant for the Pistoia Alliance he works to lower barriers to innovation.