The prospects of Monte Carlo antibody loop modelling on a fault-tolerant quantum computer

TL;DR

This paper explores the potential of using fault-tolerant quantum computers to model antibody structures via Monte Carlo methods, analyzing resource requirements and future prospects for pharmaceutical applications.

Contribution

It introduces a specific quantum encoding scheme for antibody modeling and provides a detailed analysis of the resource needs for industrial-scale problems.

Findings

Quantum advantage is theoretically possible with continued technological improvements.

Resource requirements are currently high but may become feasible in the future.

The approach could significantly impact drug development processes.

Abstract

Quantum computing for the biological sciences is an area of rapidly growing interest, but specific industrial applications remain elusive. Quantum Markov chain Monte Carlo has been proposed as a method for accelerating a broad class of computational problems, including problems of pharmaceutical interest. Here we investigate the prospects of quantum advantage via this approach, by applying it to the problem of modelling antibody structure, a crucial task in drug development. To minimize the resources required while maintaining pharmaceutical-level accuracy, we propose a specific encoding of molecular dihedral angles into registers of qubits and a method for implementing, in quantum superposition, a Markov chain Monte Carlo update step based on a classical all-atom force field. We give the first detailed analysis of the resources required to solve a problem of industrial size and relevance and find that, though the time and space requirements of using a quantum computer in this way are considerable, continued technological improvements could bring the required resources within reach in the future.