High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware
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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 novel 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 and that the QA annealing time can be used to adjust the effective temperature of the output distribution. The procedure proposed in this work provides a new approach to using QA hardware for Ising model sampling presenting potential new opportunities for applications in machine learning and physics simulation.
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