Modeling Biology on a Quantum Computer: Deciphering the Mechanism of ATP Hydrolysis Using Quantum Hardware

The ability to model biochemical reaction dynamics on quantum hardware would open the door to the virtually exact description of enzymatic catalysis, accelerating the discovery of novel therapeutics. However, noisy hardware, the costs of computing gradients, and the number of qubits and gates required to simulate large systems present major challenges to realizing the potential of dynamical simulations using quantum hardware. In this talk, I will discuss our recent efforts to model ATP hydrolysis, a paradigmatic and clinically-important biochemical reaction, using quantum hardware. Key to our modeling is employing transfer learning to learn approximate force fields based on abundant data and then correcting those force fields using data from quantum hardware. Using this technique and new embedding and downfolding methods, I will show how we can gain novel mechanistic insights into a variety of hydrolysis-related reactions and how these techniques can be adapted to other problems in biochemistry. Throughout this talk, I will underscore the opportunities and challenges associated with using quantum hardware and how these can be addressed via the fruitful marriage of quantum computation and machine learning.

Brenda Rubenstein

Director of Data Science, Vernon K. Krieble Professor of Chemistry, Professor of Physics, Brown University on September 12, 2025 at 10:15 AM in EB2 1231
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Dr. Brenda Rubenstein is currently the Krieble Professor of Chemistry at Brown University. She was named to Popular Science magazine’s 2021 Brilliant 10 list of the top early career scientists and C&EN’s 2019 Talented 12 list of early career chemists, and has received a number of research and teaching honors including the Camille Dreyfus Teacher Scholar Award, a Cottrell Teacher Scholar Award, and a Sloan Research Fellowship. While the focus of her work is on developing new electronic structure methods, she is also deeply engaged in rethinking computing architectures and computational biophysics. Prior to arriving at Brown, she was a Lawrence Distinguished Postdoctoral Fellow at Lawrence Livermore National Laboratory. She received her Sc.B.s in Chemical Physics and Applied Mathematics at Brown University, her M.Phil. in Computational Chemistry while a Churchill Scholar at the University of Cambridge, and her Ph.D. in Chemical Physics at Columbia University.

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