论文标题
具有特定于任务的量子度量学习的变分量子内核
Variational Quantum Kernels with Task-Specific Quantum Metric Learning
论文作者
论文摘要
量子内核方法，即带量子核的内核方法，作为一种混合量子 - 古典方法的量子机器学习（QML）提供了明显的优势，包括适用于噪音中间尺度量子（NISQ）设备的适用性和用于解决所有类型的机器学习问题的用法。内核方法依赖于较高（可能是无限）尺寸特征空间中点之间相似性的概念。对于机器学习，相似性的概念假定特征空间中的点应在机器学习任务空间中接近。在本文中，我们讨论了与特定于任务的量子度量学习的变异量子内核的使用，以生成针对机器学习任务的最佳量子嵌入（又称量子特征编码）。这种特定于任务的最佳量子嵌入（隐式支持特征选择）不仅对量子内核方法在改善后者的性能方面具有价值，而且对于基于参数化的量子电路（PQC）的非内核QML方法，它们也可能是有价值的。这进一步证明了量子内核方法的量子实用程序和量子优势（具有经典的量子嵌入）。
Quantum kernel methods, i.e., kernel methods with quantum kernels, offer distinct advantages as a hybrid quantum-classical approach to quantum machine learning (QML), including applicability to Noisy Intermediate-Scale Quantum (NISQ) devices and usage for solving all types of machine learning problems. Kernel methods rely on the notion of similarity between points in a higher (possibly infinite) dimensional feature space. For machine learning, the notion of similarity assumes that points close in the feature space should be close in the machine learning task space. In this paper, we discuss the use of variational quantum kernels with task-specific quantum metric learning to generate optimal quantum embeddings (a.k.a. quantum feature encodings) that are specific to machine learning tasks. Such task-specific optimal quantum embeddings, implicitly supporting feature selection, are valuable not only to quantum kernel methods in improving the latter's performance, but they can also be valuable to non-kernel QML methods based on parameterized quantum circuits (PQCs) as pretrained embeddings and for transfer learning. This further demonstrates the quantum utility, and quantum advantage (with classically-intractable quantum embeddings), of quantum kernel methods.
