Efficient Hamiltonian Simulation: A Utility Scale Perspective for Covalent Inhibitor Reactivity Prediction
We demonstrate how Haiqu’s optimized execution enables the dynamical simulation of the electronic structure of pharmaceutically-relevant molecules. Previously unavailable due to extremely deep quantum circuits being corrupted by the hardware noise, these results, in combination with classical machine learning, enable prediction of reactivity of the studied molecules. This property is a key metric in drug discovery research. Following a 15x reduction of the quantum circuit depth compared to baseline, we recovered the relevant signal from the quantum computer with high accuracy, while the bare execution produced uniform noise.
Date: 2024/12/20
Journal/conference link and reference: Accepted to Royal Society’s Phil. Trans. A., as part of a bigger paper.
