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

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.

29 pages, 7 Figures, 5 Tables

References in corpus (6)

• Surface codes: Towards practical large-scale quantum computation
• A Quantum Approximate Optimization Algorithm
• A new quantum ripple-carry addition circuit
• The prospects of quantum computing in computational molecular biology
• Focus beyond quadratic speedups for error-corrected quantum advantage
• On the construction of model Hamiltonians for adiabatic quantum computation and its application to finding low energy conformations of lattice protein models

Cited by in corpus (3)

• The state of quantum computing applications in health and medicine
• Protecting Expressive Circuits with a Quantum Error Detection Code
• Quantum-Classical Auxiliary Field Quantum Monte Carlo with Matchgate Shadows on Trapped Ion Quantum Computers