Exploring Quantum-Enhanced Estimation of Financial Risk Metrics with Quantum RNG

TL;DR

This paper investigates the use of quantum random number generators to improve the precision of financial risk metrics estimation, demonstrating potential benefits in accuracy and risk measure estimates.

Contribution

It introduces Quantum-Enhanced Monte Carlo methods utilizing QRNG for more precise financial risk metric estimation, combining quantum and classical techniques.

Findings

Quantum-based methods show improved accuracy in risk estimation.

Quantum methods yield slightly lower VaR and CVaR estimates.

Results indicate potential for enhanced risk management precision.

Abstract

In this paper, we present an approach for estimating significant financial metrics within risk management by utilizing quantum phenomena for random number generation. We explore Quantum-Enhanced Monte Carlo, a method that combines traditional and quantum techniques for enhanced precision through Quantum Random Numbers Generation (QRNG). The proposed methods can be based on the use of photonic phenomena or quantum processing units to generate random numbers. The results are promising, hinting at improved accuracy with the proposed methods and slightly lower estimates (both for VaR and CVaR estimation) using the quantum-based methodology.