Works in quantum machine learning (QML) over the past few years indicate that QML algorithms can function just as well as their classical counterparts, and even outperform them in some cases. Among the corpus of recent work, many current QML models take advantage of variational quantum algorithm (VQA) circuits, given that their scale is typically small enough to be compatible with NISQ devices and the method of automatic differentiation for optimizing circuit parameters is familiar to machine learning (ML). While the results bear interesting promise for an era when quantum machines are more readily accessible, if one can achieve similar results through non-quantum methods then there may be a more near-term advantage available to practitioners. To this end, the nature of this work is to investigate the utilization of stochastic methods inspired by a variational quantum version of the long short-term memory (LSTM) model in an attempt to approach the reported successes in performance and rapid convergence. By analyzing the performance of classical, stochastic, and quantum methods, this work aims to elucidate if it is possible to achieve some of QML's major reported benefits on classical machines by incorporating aspects of its stochasticity.

翻译：近年来，量子机器学习领域的研究表明，量子机器学习算法能够达到与其经典对应方法相当的性能，在某些情况下甚至超越后者。在近期大量工作中，许多现有量子机器学习模型利用变分量子算法电路，原因在于其规模通常较小，足以兼容NISQ设备，并且机器学习领域熟悉使用自动微分方法优化电路参数。尽管这些结果在量子机器更易获取的时代展现出诱人前景，但若能通过非量子方法实现类似效果，则从业者或许能获得更近期的优势。为此，本研究旨在探究如何利用受变分量子长短期记忆模型启发的随机方法，以接近已报道的成功性能与快速收敛优势。通过分析经典方法、随机方法与量子方法的性能，本研究试图阐明：是否可以通过融入量子机器学习的随机性特征，在经典机器上实现该领域若干被广泛报道的重大优势。