论文标题
通过生成深度学习，使用量子退火器加速平衡自旋玻璃模拟
Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning
论文作者
论文摘要
绝热量子计算机（例如由D-Wave Systems Inc.商业化的量子退火器）通常用于解决组合优化问题。在本文中，我们展示了如何利用它们来加速马尔可夫链蒙特卡洛模拟在低但有限的温度下计算挑战性旋转玻璃模型的模拟。这是通过培训生成性神经网络对D-Wave量子退火器生成的数据的培训，然后使用它们来为大都市杂货店算法生成智能建议。特别是，我们通过结合单个自旋和神经建议以及D波和经典的蒙特卡洛训练数据来探索混合方案。混合算法的表现优于单个旋转式大都市杂货算法。就相关时间而言，它具有平行回火的竞争力，其平衡时间较短的显着好处。
Adiabatic quantum computers, such as the quantum annealers commercialized by D-Wave Systems Inc., are routinely used to tackle combinatorial optimization problems. In this article, we show how to exploit them to accelerate equilibrium Markov chain Monte Carlo simulations of computationally challenging spin-glass models at low but finite temperatures. This is achieved by training generative neural networks on data produced by a D-Wave quantum annealer, and then using them to generate smart proposals for the Metropolis-Hastings algorithm. In particular, we explore hybrid schemes by combining single spin-flip and neural proposals, as well as D-Wave and classical Monte Carlo training data. The hybrid algorithm outperforms the single spin-flip Metropolis-Hastings algorithm. It is competitive with parallel tempering in terms of correlation times, with the significant benefit of a much shorter equilibration time.
