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

2025-01-27

AI Summary

Addressing the challenges of small sample size, high-dimensional features, and susceptibility to overfitting in bond recovery rate prediction, this paper proposes a hybrid quantum-classical neural network. Our method introduces amplitude encoding—deployed for the first time in recovery rate forecasting—to achieve exponential compression of financial time-series features via quantum state amplitudes. Crucially, we abandon conventional orthogonality constraints and instead leverage the intrinsic unitarity of parameterized quantum circuits (PQCs) to enhance training stability and generalization. Evaluated on a global dataset of 1,725 defaulted bonds spanning 1996–2023, our model achieves an RMSE of 0.228, outperforming both classical neural networks (RMSE = 0.246) and angle-encoding quantum baselines (RMSE = 0.242). The approach delivers superior predictive accuracy while maintaining computational efficiency, demonstrating the practical viability of quantum-enhanced modeling in credit risk analytics.

Technology Category

• Machine Learning: Quantum Machine Learning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Recovery rate prediction plays a pivotal role in bond investment strategies, enhancing risk assessment, optimizing portfolio allocation, improving pricing accuracy, and supporting effective credit risk management. However, forecasting faces challenges like high-dimensional features, small sample sizes, and overfitting. We propose a hybrid Quantum Machine Learning model incorporating Parameterized Quantum Circuits (PQC) within a neural network framework. PQCs inherently preserve unitarity, avoiding computationally costly orthogonality constraints, while amplitude encoding enables exponential data compression, reducing qubit requirements logarithmically. Applied to a global dataset of 1,725 observations (1996-2023), our method achieved superior accuracy (RMSE 0.228) compared to classical neural networks (0.246) and quantum models with angle encoding (0.242), with efficient computation times. This work highlights the potential of hybrid quantum-classical architectures in advancing recovery rate forecasting.

Problem

• Bond Recovery Rate Prediction
• Big Information Small Sample
• Investment Risk Assessment

Innovation

• Hybrid Quantum Neural Network
• Amplitude Encoding
• Bond Recovery Rate Prediction