Quantum Machine Learning: Quantum Kernel Methods

TL;DR

This paper explores quantum kernel methods in quantum machine learning, demonstrating potential quantum advantages in classification tasks and proposing extensions to deep learning architectures like CNNs.

Contribution

It introduces a quantum kernel approach for SVMs that shows significant advantage over classical kernels and discusses extending quantum kernels to CNN feature extraction.

Findings

Quantum kernels outperform classical kernels in certain classification tasks.

Quantum advantage is linked to the ability to recognize intrinsic data patterns.

Extensions to CNNs suggest broader applicability of quantum kernels.

Abstract

Quantum algorithms based on quantum kernel methods have been investigated previously [1]. A quantum advantage is derived from the fact that it is possible to construct a family of datasets for which, only quantum processing can recognise the intrinsic labelling patterns, while for classical computers the dataset looks like noise. This is due to the algorithm leveraging inherent efficiencies in the computation of logarithms in a cyclic group. The discrete log problem.is a well-known advantage of quantum vs classical computation: where it is possible to generate all the members of the group using a single mathematical operation. Kernel methods are a powerful and popular technique in classical Machine Learning. The use of a quantum feature space that can only be calculated efficiently on a quantum computer potentially allows for deriving a quantum advantage. In this paper, we intend to first describe the application of such a kernel method to a Quantum version of the classical Support Vector Machine (SVM) algorithm to identify conditions under which, a quantum advantage is realised. A data dependent projected quantum kernel was shown to provide significant advantage over classical kernels. Further, we present results of investigations and ideas pertaining to extending the use of quantum kernels as a feature extraction layer in a Convolutional Neural Networks (CNN) that is a widely used architecture in deep-learning applications.