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.
翻译:能够处理金融界所收集的大数据集的机器学习模型往往会成为运行费用昂贵的黑盒。量子计算模式建议新的优化技术,结合古典算法,可以提供具有竞争力、更快和更可解释的模型。在这项工作中,我们提出了一个量子强化的机器学习解决方案,用于预测信用评级降级,又称金融风险管理领域的坠落天使预测。我们在一个中性原子量子处理器上实施这一解决方案,在现实数据集上拥有多达60公分。我们报告对照最先进的随机森林基准进行的竞争性业绩,而我们的模型则实现更好的解释性和可比的培训时间。我们研究如何在近期内改进我们的想法,用基于Tensor网络的数字模拟来验证我们的想法。