标题： 具有幅值编码的混合量子神经网络：提升恢复率预测 显示英文标题

标题： Hybrid Quantum Neural Networks with Amplitude Encoding: Advancing Recovery Rate Predictions

摘要： 预测回收率在债券投资策略中起着关键作用，通过增强风险评估、优化投资组合分配、提高定价准确性和支持有效的信用风险管理。然而，由于复杂的非线性依赖关系、高维特征空间和有限的样本量，准确预测仍然具有挑战性——在这种条件下，经典机器学习模型容易过拟合。我们提出了一种结合幅值编码的混合量子机器学习（QML）模型，利用参数化量子电路（PQC）的单位约束和量子位的指数数据压缩能力。我们在一个包含1996年至2023年1,725个观测值和256个特征的全球回收率数据集上评估了该模型。我们的混合方法显著优于使用角度编码的经典神经网络和QML模型，分别实现了0.228的较低均方根误差（RMSE），而经典神经网络和QML模型分别为0.246和0.242。它在与XGBoost等集成树方法的竞争中也表现良好。尽管在噪声中等规模量子（NISQ）硬件上仍存在实际实施挑战，但我们的量子模拟和在噪声模拟器上的初步结果展示了混合量子-经典架构在提高回收率预测的准确性和鲁棒性方面的潜力。这些发现说明了量子机器学习在塑造信用风险预测未来方面的潜力。 显示英文摘要

摘要： Recovery rate prediction plays a pivotal role in bond investment strategies by enhancing risk assessment, optimizing portfolio allocation, improving pricing accuracy, and supporting effective credit risk management. However, accurate forecasting remains challenging due to complex nonlinear dependencies, high-dimensional feature spaces, and limited sample sizes-conditions under which classical machine learning models are prone to overfitting. We propose a hybrid Quantum Machine Learning (QML) model with Amplitude Encoding, leveraging the unitarity constraint of Parametrized Quantum Circuits (PQC) and the exponential data compression capability of qubits. We evaluate the model on a global recovery rate dataset comprising 1,725 observations and 256 features from 1996 to 2023. Our hybrid method significantly outperforms both classical neural networks and QML models using Angle Encoding, achieving a lower Root Mean Squared Error (RMSE) of 0.228, compared to 0.246 and 0.242, respectively. It also performs competitively with ensemble tree methods such as XGBoost. While practical implementation challenges remain for Noisy Intermediate-Scale Quantum (NISQ) hardware, our quantum simulation and preliminary results on noisy simulators demonstrate the promise of hybrid quantum-classical architectures in enhancing the accuracy and robustness of recovery rate forecasting. These findings illustrate the potential of quantum machine learning in shaping the future of credit risk prediction.