Implementing Credit Risk Analysis with Quantum Singular Value Transformation

TL;DR

This paper explores using Quantum Singular Value Transformation to improve the efficiency of quantum algorithms for credit risk analysis, aiming to make quantum methods more practical for financial applications.

Contribution

It introduces a novel approach employing QSVT to reduce quantum circuit complexity in credit risk estimation, enhancing scalability and practicality.

Findings

QSVT reduces quantum circuit costs for credit risk analysis

Simulation validates the efficiency of the proposed quantum approach

End-to-end implementation demonstrates practical feasibility

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.