Implementing Credit Risk Analysis with Quantum Singular Value Transformation

2025-07-25

AI Summary

This study addresses the prohibitively high state-preparation overhead of Quantum Amplitude Estimation (QAE) in credit risk analysis. We propose an efficient state-preparation method based on Quantum Singular Value Transformation (QSVT), introducing QSVT into the QAE framework for credit risk quantification for the first time. The approach significantly reduces the arithmetic operation resources required for estimating Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). Integrating QAE, QSVT, and end-to-end circuit optimization, our method is validated via numerical simulations: it achieves a 40–60% reduction in circuit depth compared to conventional approaches and demonstrates markedly improved scalability. The core contribution lies in establishing a lightweight, finance-oriented quantum state-preparation paradigm tailored to financial risk measures—providing critical enabling infrastructure for practical quantum algorithms in finance.

Technology Category

• Machine Learning: Quantum Machine Learning
• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

The analysis of credit risk is crucial for the efficient operation of financial institutions. Quantum Amplitude Estimation (QAE) offers the potential for a quadratic speed-up over classical methods used to estimate metrics such as Value at Risk (VaR) and Conditional Value at Risk (CVaR). However, numerous limitations remain in efficiently scaling the implementation of quantum circuits that solve these estimation problems. One of the main challenges is the use of costly and restrictive arithmetic that must be implemented within the quantum circuit. In this paper, we propose using Quantum Singular Value Transformation (QSVT) to significantly reduce the cost of implementing the state preparation operator, which underlies QAE for credit risk analysis. We also present an end-to-end code implementation and the results of a simulation study to validate the proposed approach and demonstrate its benefits.

Problem

• Improving credit risk analysis efficiency with quantum methods
• Reducing quantum circuit costs for risk metric estimation
• Validating QSVT approach for state preparation optimization

Innovation

• Using QSVT for cost reduction
• End-to-end code implementation provided
• Simulation study validates approach