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Study on Quantum Finance Algorithm: Quantum Monte Carlo Algorithm based on European Option Pricing

Jian-Guo Hu1,*, Shao-Yi Wu1,*, Yi Yang1, Qin-Sheng Zhu1, Xiao-Yu Li1, Shan Yang2
1 School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
2 Department of Chemistry, Physics and Atmospheric Science, Jackson State University, Jackson, MS, USA
* Corresponding Authors: Jian-Guo Hu. Email: ; Shao-Yi Wu. Email:

Journal of Quantum Computing 2022, 4(1), 53-61. https://doi.org/10.32604/jqc.2022.027683

Received 06 January 2022; Accepted 29 April 2022; Issue published 12 August 2022

Abstract

As one of the major methods for the simulation of option pricing, Monte Carlo method assumes random fluctuations in the distribution of asset prices. Under certain uncertainties process, different evolution paths could be simulated so as to finally yield the expectation value of the asset price, which requires a lot of simulations to ensure the accuracy based on huge and expensive calculations. In order to solve the above computational problem, quantum Monte Carlo (QMC) has been established and applied in the relevant systems such as European call options. In this work, both MC and QM methods are adopted to simulate European call options. Based on the preparation of quantum states in QMC algorithm and the construction of quantum circuits by simulating a quantum hardware environment on a traditional computer, the amplitude estimation (AE) algorithm is found to play a secondary role in accelerating the pricing of European options. More importantly, the payoff function and the time required for the simulation in QMC method show some improvements than those in MC method.

Keywords

Monte carlo method (MC); option pricing; quantum monte carlo (QMC); amplitude estimation (AE); payoff function

Cite This Article

J. Hu, S. Wu, Y. Yang, Q. Zhu, X. Li et al., "Study on quantum finance algorithm: quantum monte carlo algorithm based on european option pricing," Journal of Quantum Computing, vol. 4, no.1, pp. 53–61, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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