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.

翻译：回收率预测通过增强风险评估、优化投资组合配置、提高定价精度以及支持有效的信用风险管理，在债券投资策略中发挥着关键作用。然而，由于复杂的非线性依赖关系、高维特征空间以及有限的样本量——这些条件下经典机器学习模型容易过拟合——准确预测仍然具有挑战性。我们提出了一种采用幅度编码的混合量子机器学习模型，该模型利用了参数化量子电路的单位性约束以及量子比特的指数级数据压缩能力。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经典神经网络和采用角度编码的量子机器学习模型，实现了更低的均方根误差（0.228），而对比模型的误差分别为0.246和0.242。同时，其性能与XGBoost等集成树方法相比也具有竞争力。尽管在噪声中等规模量子硬件上实际应用仍面临挑战，但我们的量子模拟以及在噪声模拟器上的初步结果表明，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具有前景。这些发现阐明了量子机器学习在塑造信用风险预测未来方面的潜力。