High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware
High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware
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DOI：
10.1103/physrevapplied.17.044046
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
J. Nelson;Marc Vuffray;A. Lokhov;T. Albash;Carleton Coffrin
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文献类型：
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作者：
J. Nelson;Marc Vuffray;A. Lokhov;T. Albash;Carleton Coffrin
量子退火（QA）最初旨在加速具有自然编码的伊辛模型的组合优化任务的解决方案。然而，最近在QA硬件平台上的实验表明，在对应于弱相互作用的操作机制中，QA硬件在硬件特定的有效温度下表现得像一个有噪声的吉布斯采样器。这项工作建立在这些见解的基础上，并确定了一类小型硬件本地伊辛模型，这些模型对噪声效应具有鲁棒性，并提出了一种在QA硬件上执行这些模型的程序，以最大限度地提高吉布斯采样性能。实验结果表明，该协议的结果在高质量的吉布斯样本从硬件特定的有效温度。此外，我们表明，这个有效的温度可以通过调节退火时间和能量尺度来调节。在这项工作中提出的程序提供了一种方法，使用QA硬件伊辛模型采样提供潜在的新的机会，在机器学习和物理模拟的应用。
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.