Financial Risk Management on a Neutral Atom Quantum Processor
This work addresses financial risk management for institutions by offering a potentially faster and more interpretable model, though it appears incremental as it combines quantum and classical techniques without a major breakthrough.
The authors tackled the problem of predicting credit rating downgrades (fallen-angels forecasting) in financial risk management by proposing a quantum-enhanced machine learning solution implemented on a neutral atom quantum processor with up to 60 qubits, achieving competitive performance against a state-of-the-art Random Forest benchmark with better interpretability and comparable training times.
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.