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，配分函数的计算通常较为困难。近年来，有研究者提出了一种基于变分自回归网络的近似方法。该方法能够直接计算生成概率，同时获得数量可观的样本。本研究提出了一种新颖的近似方法，该方法将量子退火机生成的样本（经验上认为这些样本遵循吉布斯-玻尔兹曼分布）与变分自回归网络相结合。当应用于有限尺寸的舍灵顿-柯克帕特里克模型时，与传统变分自回归网络方法及其他近似方法（如广泛使用的朴素平均场）相比，所提方法展现出更高的精度。