Quantum Multi-view Kernel Learning with Local Information

TL;DR

This paper introduces L-QMVKL, a quantum multi-view kernel learning method that incorporates local structural information to improve accuracy in complex data analysis, surpassing classical approaches.

Contribution

It proposes a novel quantum multi-view kernel learning framework that effectively fuses view-specific quantum kernels with local information, enhancing performance on complex datasets.

Findings

L-QMVKL achieves higher accuracy than classical methods.

Incorporating local information improves quantum kernel performance.

Numerical simulations validate the effectiveness of the proposed approach.

Abstract

Kernel methods serve as powerful tools to capture nonlinear patterns behind data in machine learning. The quantum kernel, integrating kernel theory with quantum computing, has attracted widespread attention. However, existing studies encounter performance bottlenecks when processing complex data with localized structural patterns, stemming from the limitation in single-view feature representation and the exclusive reliance on global data structure. In this paper, we propose quantum multi-view kernel learning with local information, called L-QMVKL. Specifically, based on the multi-kernel learning, a representative method for multi-view data processing, we construct the quantum multi-kernel that combines view-specific quantum kernels to effectively fuse cross-view information. Further leveraging local information to capture intrinsic structural information, we design a sequential training strategy for the quantum circuit parameters and weight coefficients with the use of the hybrid global-local kernel alignment. We evaluate the effectiveness of L-QMVKL through comprehensive numerical simulations on the Mfeat dataset, demonstrating significant accuracy improvements achieved through leveraging multi-view methodology and local information. Meanwhile, the results show that L-QMVKL exhibits a higher accuracy than its classical counterpart. Our work holds promise for advancing the theoretical and practical understanding of quantum kernel methods.