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.

分析经典、随机和量子方法的表现，研究在长短期记忆模型中利用量子变分算法启发的随机方法，以取得量子机器学习在经典机器上的一些好处。

一种受量子机器学习启发的新型随机 LSTM 模型

A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning

The use of quantum computing for machine learning is among the most exciting
prospective applications of quantum technologies. However, machine learning
tasks where data is provided can be considerably different than commonly
studied computational tasks. In this work, we show that some problems that are
classically hard to compute can be easily predicted by classical machines
learning from data. Using rigorous prediction error bounds as a foundation, we
develop a methodology for assessing potential quantum advantage in learning
tasks. The bounds are tight asymptotically and empirically predictive for a
wide range of learning models. These constructions explain numerical results
showing that with the help of data, classical machine learning models can be
competitive with quantum models even if they are tailored to quantum problems.
We then propose a projected quantum model that provides a simple and rigorous
quantum speed-up for a learning problem in the fault-tolerant regime. For
near-term implementations, we demonstrate a significant prediction advantage
over some classical models on engineered data sets designed to demonstrate a
maximal quantum advantage in one of the largest numerical tests for gate-based
quantum machine learning to date, up to 30 qubits.

探讨了量子计算在机器学习中的应用和机器学习任务中存在的与传统计算不同之处，提出了一种用于评估量子学习任务潜在优势的方法，并提出了一种通过量子速度提高学习效率的量子机器学习模型。