Quantum Multiple Kernel Learning in Financial Classification Tasks

TL;DR

This paper introduces a hybrid quantum multiple kernel learning method that enhances financial classification tasks by combining kernels, tested on real quantum hardware, showing improved accuracy and robustness over single kernel methods.

Contribution

The paper presents a novel hybrid quantum multiple kernel learning approach that improves classification performance in financial datasets and demonstrates its effectiveness on quantum hardware.

Findings

QMKL improves classification accuracy over single kernels.

QMKL is robust across various financial datasets.

Quantum hardware implementation benefits from error mitigation.

Abstract

Financial services is a prospect industry where unlocked near-term quantum utility could yield profitable potential, and, in particular, quantum machine learning algorithms could potentially benefit businesses by improving the quality of predictive models. Quantum kernel methods have demonstrated success in financial, binary classification tasks, like fraud detection, and avoid issues found in variational quantum machine learning approaches. However, choosing a suitable quantum kernel for a classical dataset remains a challenge. We propose a hybrid, quantum multiple kernel learning (QMKL) methodology that can improve classification quality over a single kernel approach. We test the robustness of QMKL on several financially relevant datasets using both fidelity and projected quantum kernel approaches. We further demonstrate QMKL on quantum hardware using an error mitigation pipeline and show the benefits of QMKL in the large qubit regime.