Year: 2023
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Miyabe, Shungo, Quanz, Brian, Shimada, Noriaki, Mitra, Abhijit, Yamamoto, Takahiro, Rastunkov, Vladimir, Alevras, Dimitris, Metcalf, Mekena, King, Daniel J. M., Mamouei, Mohammad, Jackson, Matthew D., Brown, Martin, Intallura, Philip, Park, Jae-Eun
Institutions: IBM Research - Tokyo, IBM (United States), HSBC Holdings
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: HSBC Bank USA
OpenAlex ID: W4389325789