# Credit Risk Analysis using Quantum Computers

## TL;DR

This paper introduces a quantum algorithm for more efficient credit risk estimation, specifically calculating economic capital requirements, and analyzes its scalability and hardware requirements for practical implementation.

## Contribution

It presents a novel quantum algorithm for credit risk analysis and provides detailed estimates of qubit needs, circuit depth, and runtime for realistic problem sizes.

## Key findings

- Quantum algorithm reduces computational complexity compared to classical methods.
- Estimated qubit count and circuit depth for practical quantum hardware.
- Analysis indicates potential for efficient credit risk estimation on future quantum computers.

## Abstract

We present and analyze a quantum algorithm to estimate credit risk more efficiently than Monte Carlo simulations can do on classical computers. More precisely, we estimate the economic capital requirement, i.e. the difference between the Value at Risk and the expected value of a given loss distribution. The economic capital requirement is an important risk metric because it summarizes the amount of capital required to remain solvent at a given confidence level. We implement this problem for a realistic loss distribution and analyze its scaling to a realistic problem size. In particular, we provide estimates of the total number of required qubits, the expected circuit depth, and how this translates into an expected runtime under reasonable assumptions on future fault-tolerant quantum hardware.