High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware

TL;DR

This paper demonstrates how quantum annealing hardware can be used as a high-quality Gibbs sampler for specific Ising models, with adjustable effective temperature, opening new avenues for machine learning and physics applications.

Contribution

It identifies noise-robust Ising models suitable for Gibbs sampling on QA hardware and proposes a protocol to optimize sampling quality and temperature control.

Findings

High-quality Gibbs samples achieved on QA hardware.

Effective temperature can be tuned via annealing time and energy scale.

Proposed method enhances QA's utility in machine learning and physics simulations.

Abstract

Quantum Annealing (QA) was originally intended for accelerating the solution of combinatorial optimization tasks that have natural encodings as Ising models. However, recent experiments on QA hardware platforms have demonstrated that, in the operating regime corresponding to weak interactions, the QA hardware behaves like a noisy Gibbs sampler at a hardware-specific effective temperature. This work builds on those insights and identifies a class of small hardware-native Ising models that are robust to noise effects and proposes a procedure for executing these models on QA hardware to maximize Gibbs sampling performance. Experimental results indicate that the proposed protocol results in high-quality Gibbs samples from a hardware-specific effective temperature. Furthermore, we show that this effective temperature can be adjusted by modulating the annealing time and energy scale. The procedure proposed in this work provides an approach to using QA hardware for Ising model sampling presenting potential new opportunities for applications in machine learning and physics simulation.