Toward end-to-end quantum simulation for protein dynamics

TL;DR

This paper explores the development of end-to-end quantum algorithms for simulating protein dynamics, including state preparation, evolution, and measurement, aiming to leverage quantum speedups in computational biology.

Contribution

It introduces the first efficient procedures for preparing Gaussian pseudo-random states and loading protein connectivity matrices in quantum simulations of protein dynamics.

Findings

Algorithms for estimating energy and vibrational modes

Classical experiments validating quantum speedup potential

Demonstration of quantum simulation feasibility for protein folding

Abstract

Modeling and simulating the protein folding process overall remains a grand challenge in computational biology. We systematically investigate end-to-end quantum algorithms for simulating various protein dynamics with effects, such as mechanical forces or stochastic noises. A major focus is the read-in of system settings for simulation, for which we discuss (i) efficient quantum algorithms to prepare initial states--whether for ensemble or single-state simulations, in particular, the first efficient procedure for preparing Gaussian pseudo-random amplitude states, and (ii) the first efficient loading of the connectivity matrices of the protein structure. For the read-out stage, our algorithms estimate a range of classical observables, including energy, low-frequency vibrational modes, density of states, displacement correlations, and optimal control parameters. Between these stages, we simulate the dynamic evolution of the protein system, by using normal mode models--such as Gaussian network models (GNM) and all-atom normal mode models. In addition, we conduct classical numerical experiments focused on accurately estimating the density of states and applying optimal control to facilitate conformational changes. These experiments serve to validate our claims regarding potential quantum speedups. Overall, our study demonstrates that quantum simulation of protein dynamics represents a robust, end-to-end application for both early-stage and fully fault-tolerant quantum computing.