In statistical mechanics, computing the partition function is generally difficult. An approximation method using a variational autoregressive network (VAN) has been proposed recently. This approach offers the advantage of directly calculating the generation probabilities while obtaining a significantly large number of samples. The present study introduces a novel approximation method that employs samples derived from quantum annealing machines in conjunction with VAN, which are empirically assumed to adhere to the Gibbs-Boltzmann distribution. When applied to the finite-size Sherrington-Kirkpatrick model, the proposed method demonstrates enhanced accuracy compared to the traditional VAN approach and other approximate methods, such as the widely utilized naive mean field.

翻译：在统计力学中，计算配分函数通常较为困难。近年来，有研究提出了一种基于变分自回归网络（VAN）的近似方法。该方法具有直接计算生成概率并可获取大量样本的优势。本研究提出了一种新型近似方法，将量子退火机产生的样本与VAN相结合——这些样本经验上被认为服从吉布斯-玻尔兹曼分布。当应用于有限尺寸的Sherrington-Kirkpatrick模型时，与传统VAN方法及其他近似方法（如广泛使用的朴素平均场法）相比，该方法展现出更高的精度。