Quantum Computing for Biological Systems

Motivated by our extensive work in Computational Biology and Quantum Computing, our group is deeply invested in understanding how today’s quantum computers can be used to model and simulate biologically relevant systems. We are particularly interested in exploring how quantum algorithms can uncover new insights into molecular dynamics and biomolecular interactions that are beyond the reach of conventional high-performance computing.

In our effort to tackle real-world biological problems using quantum technologies, we are developing large-scale data pipelines that integrate classical simulations, machine learning, and novel quantum algorithms to accelerate discovery. These pipelines aim to bridge the gap between quantum hardware capabilities and biologically meaningful observables, enabling hybrid quantum–classical workflows that push the boundaries of what can be learned from complex molecular systems.

Our work in this area has led us to advance the phases of two major ongoing competitions: Wellcome Leap’s Quantum for Bio and the NIH Quantum Computing Challenge, both of which seek to catalyze the use of quantum computing in the life sciences.