Paper ID: 2212.03223

Financial Risk Management on a Neutral Atom Quantum Processor

Lucas Leclerc, Luis Ortiz-Guitierrez, Sebastian Grijalva, Boris Albrecht, Julia R. K. Cline, Vincent E. Elfving, Adrien Signoles, Loïc Henriet, Gianni Del Bimbo, Usman Ayub Sheikh, Maitree Shah, Luc Andrea, Faysal Ishtiaq, Andoni Duarte, Samuel Mugel, Irene Caceres, Michel Kurek, Roman Orus, Achraf Seddik, Oumaima Hammammi, Hacene Isselnane, Didier M'tamon

Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithms, may deliver competitive, faster and more interpretable models. In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field. We implement this solution on a neutral atom Quantum Processing Unit with up to 60 qubits on a real-life dataset. We report competitive performances against the state-of-the-art Random Forest benchmark whilst our model achieves better interpretability and comparable training times. We examine how to improve performance in the near-term validating our ideas with Tensor Networks-based numerical simulations.

Submitted: Dec 6, 2022