Quantum machine learning (QML) is the use of quantum computing for the computation of machine learning algorithms. With the prevalence and importance of classical data, a hybrid quantum-classical approach to QML is called for. Parameterized Quantum Circuits (PQCs), and particularly Quantum Kernel PQCs, are generally used in the hybrid approach to QML. In this paper we discuss some important aspects of PQCs with quantum kernels including PQCs, quantum kernels, quantum kernels with quantum advantage, and the trainability of quantum kernels. We conclude that quantum kernels with hybrid kernel methods, a.k.a. quantum kernel methods, offer distinct advantages as a hybrid approach to QML. Not only do they apply to Noisy Intermediate-Scale Quantum (NISQ) devices, but they also can be used to solve all types of machine learning problems including regression, classification, clustering, and dimension reduction. Furthermore, beyond quantum utility, quantum advantage can be attained if the quantum kernels, i.e., the quantum feature encodings, are classically intractable.
翻译:量子机器学习(QML)是用于计算机器学习算法的量子计算(QML)。由于古典数据的普遍性和重要性,需要对QML采用混合量子古典方法。参数化量子电路(PQCs),特别是量子内核 PQCs,通常用于QML的混合方法中。我们讨论量子内核的一些重要方面,包括PQCs、量子内核、具有量子优势的量子内核和量子内核的可训练性。我们的结论是,使用混合内核法(a.k.a.量子内核方法)的量子内核提供了独特的优势,作为QML的混合方法。它们不仅适用于Noisy中间级Quntum(NISQ)装置,而且还可用于解决所有类型的机器学习问题,包括回归、分类、聚合和尺寸减少。此外,如果量子效用超出量子效用,如果量子内核是质质级、质质质质变等,则可以实现量子优势。