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

TL;DR

This paper introduces a hybrid quantum machine learning model with amplitude encoding that significantly improves recovery rate prediction accuracy over classical models and other quantum approaches, demonstrating the potential of quantum computing in credit risk analysis.

Contribution

The paper presents a novel hybrid quantum neural network with amplitude encoding that outperforms classical neural networks and other quantum models in recovery rate prediction tasks.

Findings

Hybrid quantum model achieves lower RMSE than classical neural networks.

Amplitude encoding enhances data compression and model performance.

Preliminary results show promise for quantum models on noisy simulators.

Abstract

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.