A quantum alternating operator ansatz with hard and soft constraints for lattice protein folding

Abstract

Gate-based universal quantum computers form a rapidly evolving field of quantum computing hardware technology. In previous work, we presented a quantum algorithm for lattice protein folding on a cubic lattice, tailored for quantum annealers. In this paper, we introduce a novel approach for solving the lattice protein folding problem on universal gate-based quantum computing architectures. Lattice protein models are coarse-grained representations of proteins that have been used extensively over the past thirty years to examine the principles of protein folding and design.These models can be used to explore a vast number of possible protein conformations and to infer structural properties of more complex atomistic protein structures. We formulate the problem as a quantum alternating operator ansatz, a member of the wider class of variational quantum/classical hybrid algorithms. To increase the probability of sampling the ground state, we propose splitting the optimization problem into hard and soft constraints. This enables us to use a previously under-utilised component of the variational algorithm to constrain the search to the subspace of solutions that satisfy the hard constraints.

All three authors have contributed equally to this work, 12 pages, 8 figures

References in corpus (9)

• Quantum Computing in the NISQ era and beyond
• A variational eigenvalue solver on a quantum processor
• Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
• Barren plateaus in quantum neural network training landscapes
• From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
• Quantum computing with nearest neighbor interactions and error rates over 1%
• Compiling quantum circuits to realistic hardware architectures using temporal planners
• On the construction of model Hamiltonians for adiabatic quantum computation and its application to finding low energy conformations of lattice protein models
• A quantum algorithm to train neural networks using low-depth circuits

Cited by in corpus (18)

• Noisy intermediate-scale quantum (NISQ) algorithms
• Quantum computational chemistry
• From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
• $XY$-mixers: analytical and numerical results for QAOA
• The state of quantum computing applications in health and medicine
• Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information
• Biology and medicine in the landscape of quantum advantages
• NISQ Computers: A Path to Quantum Supremacy
• Learning quantum data with the quantum Earth Mover's distance
• Constrained Quantum Optimization for Extractive Summarization on a Trapped-ion Quantum Computer
• QFold: Quantum Walks and Deep Learning to Solve Protein Folding
• Quantum Embedding Search for Quantum Machine Learning
• The prospects of Monte Carlo antibody loop modelling on a fault-tolerant quantum computer
• Coarse grained intermolecular interactions on quantum processors
• Graph Cut Segmentation Methods Revisited with a Quantum Algorithm
• RinQ: Towards predicting central sites in proteins on current quantum computers
• Lower Bounds on Circuit Depth of the Quantum Approximate Optimization Algorithm
• Quantum constraint learning for quantum approximate optimization algorithm