High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware
This work provides an incremental approach for using quantum annealing hardware in machine learning and physics simulations by improving Gibbs sampling quality.
The authors tackled the problem of using quantum annealing hardware as a noisy Gibbs sampler by identifying robust small Ising models and proposing an execution protocol to maximize sampling performance, resulting in high-quality Gibbs samples from a hardware-specific effective temperature that can be adjusted via annealing time and energy scale.
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.