Abstract
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.
Abstract (translated)
过去几年中的量子机器学习(QML)工作表明，QML算法可以与经典算法一样良好地工作，甚至在一些情况下可以表现更好。在最近的工作成果中，许多当前的QML模型利用Variational Quantum Algorithm(VQA)电路，因为它们的尺度通常足够大，与NisysQ设备兼容，而自动微分以优化电路参数是机器学习(ML)常见的方法。虽然这些结果在量子机器更为容易获取的时代具有有趣的潜力，如果通过非量子方法能够实现类似结果，则可能为实践者带来更近期的优势。因此，本工作的性质是研究基于Variational Quantum LSTM模型的随机方法的利用，试图接近报告的性能和快速收敛的成功。通过分析经典、随机和量子方法的性能，该工作旨在阐明是否可能通过引入随机性方面实现QML在经典机器上的主要报告利益。
URL
https://arxiv.org/abs/2305.10212
