The prospects of Monte Carlo antibody loop modelling on a fault-tolerant quantum computer
arXiv:2105.09690 · doi:10.3389/fddsv.2022.908870
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.
arXiv comment29 pages, 7 Figures, 5 Tables
References in corpus (29)
- A variational eigenvalue solver on a quantum processor
- Surface codes: Towards practical large-scale quantum computation
- A Quantum Approximate Optimization Algorithm
- Topological quantum memory
- Machine Learning Force Fields
- Quantum Computation and Decision Trees
- Quantum Computing with Very Noisy Devices
- Quantum Networks for Elementary Arithmetic Operations
- A simple formula for the average gate fidelity of a quantum dynamical operation
- Quantum Computation by Adiabatic Evolution
- Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity
- Quantum codes on a lattice with boundary
- A new quantum ripple-carry addition circuit
- Halving the cost of quantum addition
- The prospects of quantum computing in computational molecular biology
- Addition on a Quantum Computer
- Resource-Efficient Quantum Algorithm for Protein Folding
- Focus beyond quadratic speedups for error-corrected quantum advantage
- Efficient magic state factories with a catalyzed |CCZ> to 2|T> transformation
- On the construction of model Hamiltonians for adiabatic quantum computation and its application to finding low energy conformations of lattice protein models
- Bounding quantum gate error rate based on reported average fidelity
- Compilation of Fault-Tolerant Quantum Heuristics for Combinatorial Optimization
- QFold: Quantum Walks and Deep Learning to Solve Protein Folding
- Efficient Quantum Walk Circuits for Metropolis-Hastings Algorithm
- A quantum alternating operator ansatz with hard and soft constraints for lattice protein folding
- Construction of Energy Functions for Lattice Heteropolymer Models: A Case Study in Constraint Satisfaction Programming and Adiabatic Quantum Optimization
- Quantum circuits design for evaluating transcendental functions based on a function-value binary expansion method
- Forecasting timelines of quantum computing
- Coarse-grained lattice protein folding on a quantum annealer