New infectious diseases emerge annually, threatening millions of people around the world. Meanwhile, existing pathogens evolve resistance to treatment. Medical science can struggle to keep up.
Researchers turn to high-performance computers to identify promising chemical compounds for medicine by studying their interactions with the human body. However, the medical industry faces challenges as it works to develop new drugs and fight disease. Developing new drugs amid these challenges requires more efficient research and development methods.
Molecular research is key to developing these new drugs. When researching molecules, scientists need to isolate them and model their underlying composition. They also must anticipate how they will interact with other compounds in the human body.
Quantum computers rely on a unique underlying hardware based on the principles of quantum mechanics. They can solve certain problems beyond the ability of classical computers alone. Quantum computers won’t solve every problem more efficiently than classical supercomputers, which are better at performing sequential logical tasks. So we are leveraging the power of both by creating quantum-centric supercomputers. Just like graphics processing units (GPUs) accelerate supercomputers today, QPUs (quantum processing units) could accelerate supercomputers tomorrow.
Researchers and industry leaders worldwide are developing quantum-centric supercomputing techniques for challenging problems. For example, startup QunaSys (opens in a new tab) offers a function (opens in a new tab) to users of IBM Quantum Platform, that implements a quantum-centric technique for chemistry called quantum-selected configuration interaction, or QSCI.
Partnering with RIKEN, IBM explored one method for simulating chemistry with quantum-centric supercomputers called sample-based quantum diagonalization (opens in a new tab), or SQD. It uses quantum to generate measurements corresponding to electronic configurations, then uses classical to process those configurations into an answer robust to quantum computing noise. This technique has the potential to outperform what classical approximation methods could do alone.
We don’t have to wait for fault-tolerant quantum systems to start exploring chemistry applications with quantum. Cleveland Clinic is extending the RIKEN work to run molecular simulations relevant for drug discovery. New work combines quantum and classical using SQD to obtain information on the energies of molecules in a way that is robust to the noise inherent to quantum computation.
Quantum-centric supercomputing can go beyond the limitations of either quantum or classical computing alone and has the potential to reduce computational load and the costs required for analyzing drug compounds and interactions. These advances will help accelerate the industry and usher in a new era of computing.
Check out the interactive case study I wrote at the link below for a detailed look at the foundational research and demos!
https://www.ibm.com/quantum/case-studies/modeling-realistic-chemistry
Alex Lukin 