Financial Risk Management on a Neutral Atom Quantum Processor

arXiv:2212.03223v2 Announce Type: replace-cross
Abstract: 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 …

abstract algorithms arxiv atom become black boxes computing cond-mat.str-el cs.ce cs.lg datasets faster financial large datasets machine machine learning machine learning models management optimization paradigm processor quant-ph quantum quantum computing quantum processor risk type work world