Health care and life sciences. The application of quantum computers to health care and life sciences R&D is poised to revolutionize the diagnosis and treatment of diseases. Such a revolution is likely to increase the pace and precision of disease identification and detection, drug and pharmaceutical discovery, genomic analysis, and the development of customized medicines and interventions.
For instance, the need to assess billions of potential drug reactions and side effects on so many individual human systems can stretch the process of bringing a pharmaceutical to market to a decade or more. In early-stage drug discovery and development, pharmaceutical scientists conduct computer-assisted drug discovery. Using tools such as molecular dynamics and density functional theory, they develop computer models and simulations that can predict the impact of millions of macromolecules on diseases and the human body. While such “in-silico” methods are currently neither accurate nor speedy, they’re typically safer at this stage than in-vitro methods such as clinical trials.
Quantum computing could change that. It’s expected to have an outsized impact on all aspects of the discovery phase. This includes generating scientifically valid evidence pinpointing a disease’s biological origins and ensuring that such a biological target can be safely manipulated to achieve therapeutic benefits. Quantum computers could also identify compounds with therapeutically useful, pharmacological, or biological action that could serve as a starting point to improve the strength and precision of the compound. All this could help reduce the time and cost of bringing life-saving drugs to market.
Another potential use case for quantum computers is precision medicine—the design of individualized interventions and treatments. Existing standardized therapies and treatment protocols don’t take into account individual genetic and biological factors, behaviors, socioeconomic considerations, or environmental factors that can boost or diminish treatment impact. For example, consider that 31% of patients can’t safely metabolize Tamoxifen, a common cancer treatment,7 or that there are more than 7,000 known rare diseases—affecting more than 30 million people in the United States alone—and that most of them are incurable or have very expensive treatments.8
By helping shed light on the correlations and dependencies of various contributing factors, quantum-enhanced machine learning could advance medical research. With it, researchers could predict the efficacy of drugs and treatments, design individual treatment plans that could improve patient outcomes, and even forecast the risk of future diseases to allow for earlier or preventative treatments.
Materials science and discovery. Materials designers use complex calculations to predict the mechanical, optical, and physical properties of ceramics, glasses, polymers, metal alloys, and composite materials. Quantum computers could help design engineers create better materials by giving them more precise control over molecular reactions.
For instance, to develop the most efficient solar cells, energy researchers seek to understand the interaction of sunlight with thousands of combinations of silicon- and polymer-based materials, organometallic compounds, and inorganic substances.Moreaccurate predictions of how materials interact with light could help create more efficient solar panels and LED devices.
Likewise, energy companies are working to test, develop, and scale carbon capture technologies that use less energy to strip carbon dioxide (CO2) from power plant emissions. Current carbon capture methods, which rely on water to absorb CO2, are costly, inefficient, and polluting, and scientists have recently developed absorbent solids that can be used for more effective carbon capture.9 Quantum computing could help build on these innovations, leading to the discovery of highly porous solid materials for capturing CO2.
Chemical process simulation and optimization. Quantum computing could help scientists develop and test new industrial processes that could help conserve natural resources, reduce emissions, lower costs, and speed production.
For example, large-scale production of ammonia-based fertilizers relies on the Haber-Bosch process, a 100-year-old, energy-intensive method of nitrogen fixation that consumes between 3% and 5% of the world’s annual natural gas production. Producing fertilizers using the Haber-Bosch process alone accounts for 1% to 2% of the world’s annual energy supply and is responsible for 2% to 3% of global CO2 emissions.10Similarly, the production of hydrogen for use in hydrogen fuel cells relies on industrial methods for splitting water molecules into hydrogen and oxygen. But these existing methods are energy-intensive, rely on expensive and rare metals, and create waste carbon, which limits the use of hydrogen as a clean, green source of energy.11
Quantum computers could streamline both processes by enabling scientists to discover new catalyst materials that would reduce energy requirements for ammonia production and water-splitting. This would result in technological breakthroughs for producing fertilizers and hydrogen fuel cells.
Given the current rate of advancement and the fact that only 1,000 or so qubits may be needed to simulate molecules, chemical reactions, and materials, quantum computers will be able to meaningfully tackle computational chemistry problems sooner rather than later. Organizations that use computational chemistry or traditional materials discovery to conduct experimental studies and predict the structure of molecules may have a good business case for taking a proactive approach to quantum computing. As next steps, consider the following:
- Understand industry impact. Learn about quantum computing’s potential repercussions in your industry. What complex problems could quantum help you solve? Be aware of important technology developments and pay attention to how others in your field are investing in and experimenting with quantum technologies.
- Prioritize computational chemistry use cases. Given that quantum computers may be useful for computational chemistry before they’re useful for other applications, start exploring use cases today. Prioritize those that can deliver high value and can be addressed by quantum computers.
- Investigate the quantum ecosystem. A growing number of technology vendors, startups, and independent and academic research labs are working to commercialize capabilities to simplify quantum-based computational chemistry use cases. Through research and collaboration with internal and external quantum experts, explore and understand how your organization could leverage the quantum ecosystem and its resources.
- Develop a strategy. Bring together existing talent with the appropriate skills and knowledge to develop a quantum strategy. Even if the strategy is to take no immediate action, determine a trigger event—for example, an announcement from a competitor or the achievement of a certain technological milestone—that will serve as a prompt for further quantum investments and exploration.
- Monitor technology and industry developments. Decide who on your team will lead the quantum charge when it’s time to engage, and make sure they stay up to date on quantum developments and research. They can help you refine your strategy as events warrant. Keep in mind your stated trigger event and don’t let it pass by without taking the appropriate action.
Like many other emerging technologies, quantum computing is expected to bring many advantages to early movers. By strategically beginning to explore quantum computing’s opportunities today, organizations that leverage quantum chemistry can get a head start on their competitors.