Quantum Multiple Kernel Learning in Financial Classification Tasks

• URL: http://arxiv.org/abs/2312.00260v1
• Date: Fri, 1 Dec 2023 00:18:43 GMT
• Title: Quantum Multiple Kernel Learning in Financial Classification Tasks
• Authors: Shungo Miyabe, Brian Quanz, Noriaki Shimada, Abhijit Mitra, Takahiro Yamamoto, Vladimir Rastunkov, Dimitris Alevras, Mekena Metcalf, Daniel J.M. King, Mohammad Mamouei, Matthew D. Jackson, Martin Brown, Philip Intallura, and Jae-Eun Park
• Abstract summary: We propose a hybrid, quantum multiple kernel learning (QMKL) methodology that can improve classification quality over a single kernel approach. We show QMKL on quantum hardware using an error mitigation pipeline and show the benefits of QMKL in the large qubit regime.
• Score: 2.8564636890651607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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