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

• URL: http://arxiv.org/abs/2501.15828v4
• Date: Wed, 05 Feb 2025 04:27:28 GMT
• Title: Hybrid Quantum Neural Networks with Amplitude Encoding: Advancing Recovery Rate Predictions
• Authors: Ying Chen, Paul Griffin, Paolo Recchia, Lei Zhou, Hongrui Zhang,
• Abstract summary: Recovery rate prediction plays a pivotal role in bond investment strategies.<n>Forecasting faces challenges like high-dimensional features, small sample sizes, and overfitting.<n>We propose a hybrid Quantum Machine Learning model incorporating unitized Quantum Circuits (PQC) within a neural network framework.
• Score: 6.699192644249841
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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