VaR Estimation with Quantum Computing Noise Correction Using Neural Networks

Luis de Pedro (), Raúl París Murillo, Jorge E. López de Vergara, Sergio López-Buedo and Francisco J. Gómez-Arribas
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Luis de Pedro: Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain
Raúl París Murillo: Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain
Jorge E. López de Vergara: Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain
Sergio López-Buedo: Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain
Francisco J. Gómez-Arribas: Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain

Mathematics, 2023, vol. 11, issue 20, 1-19

Abstract: In this paper, we present the development of a quantum computing method for calculating the value at risk ( V a R ) for a portfolio of assets managed by a finance institution. We extend the conventional Monte Carlo algorithm to calculate the V a R of an arbitrary number of assets by employing random variable algebra and Taylor series approximation. The resulting algorithm is suitable to be executed in real quantum computers. However, the noise affecting current quantum computers renders them almost useless for the task. We present a methodology to mitigate the noise impact by using neural networks to compensate for the noise effects. The system combines the output from a real quantum computer with the neural network processing. The feedback is used to fine tune the quantum circuits. The results show that this approach is useful for estimating the V a R in finance institutions, particularly when dealing with a large number of assets. We demonstrate the validity of the proposed method with up to 139 assets. The accuracy of the method is also proven. We achieved an error of less than 1 % in the empirical measurements with respect to the parametric model.

Keywords: neural network; qubit; quantum computing; Monte Carlo; value at risk ( VaR ) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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