Towards practical Quantum Credit Risk Analysis

TL;DR

This paper advances quantum credit risk analysis by developing a more realistic and flexible quantum algorithm, addressing practical limitations, and demonstrating its potential scalability and benefits for financial risk modeling.

Contribution

It introduces a new quantum algorithm variant for credit risk analysis that handles complex risk models and real data, moving towards practical quantum advantage.

Findings

Enhanced risk model incorporating multiple systemic factors

Flexible Loss Given Default input for real data compatibility

Experimental validation on simulators showing scalability

Abstract

In recent years, a CRA (Credit Risk Analysis) quantum algorithm with a quadratic speedup over classical analogous methods has been introduced. We propose a new variant of this quantum algorithm with the intent of overcoming some of the most significant limitations (according to business domain experts) of this approach. In particular, we describe a method to implement a more realistic and complex risk model for the default probability of each portfolio's asset, capable of taking into account multiple systemic risk factors. In addition, we present a solution to increase the flexibility of one of the model's inputs, the Loss Given Default, removing the constraint to use integer values. This specific improvement addresses the need to use real data coming from the financial sector in order to establish fair benchmarking protocols. Although these enhancements come at a cost in terms of circuit depth and width, they nevertheless show a path towards a more realistic software solution. Recent progress in quantum technology shows that eventually, the increase in the number and reliability of qubits will allow for useful results and meaningful scales for the financial sector, also on real quantum hardware, paving the way for a concrete quantum advantage in the field. The paper also describes experiments conducted on simulators to test the circuit proposed and contains an assessment of the scalability of the approach presented.