Quantum Annealing Enhanced Markov-Chain Monte Carlo

TL;DR

This paper introduces a quantum annealing-enhanced MCMC method that improves sampling efficiency and convergence in complex systems, demonstrated on the Sherrington-Kirkpatrick model.

Contribution

It presents a novel integration of quantum annealing into MCMC, significantly enhancing exploration and convergence for complex energy landscapes.

Findings

Larger spectral gaps observed with QAEMCMC

Faster convergence of energy observables

Reduced total variation distance to target distribution

Abstract

In this study, we propose quantum annealing-enhanced Markov Chain Monte Carlo (QAEMCMC), where QA is integrated into the MCMC subroutine. QA efficiently explores low-energy configurations and overcomes local minima, enabling the generation of proposal states with a high acceptance probability. We benchmark QAEMCMC for the Sherrington-Kirkpatrick model and demonstrate its superior performance over the classical MCMC method. Our results reveal larger spectral gaps, faster convergence of energy observables, and reduced total variation distance between the empirical and target distributions. QAEMCMC accelerates MCMC and provides an efficient method for complex systems, paving the way for scalable quantum-assisted sampling strategies.