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A novel approach for quantum financial simulation and quantum state preparation

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  • Yen-Jui Chang
  • Wei-Ting Wang
  • Hao-Yuan Chen
  • Shih-Wei Liao
  • Ching-Ray Chang

Abstract

Quantum state preparation is vital in quantum computing and information processing. The ability to accurately and reliably prepare specific quantum states is essential for various applications. One of the promising applications of quantum computers is quantum simulation. This requires preparing a quantum state representing the system we are trying to simulate. This research introduces a novel simulation algorithm, the multi-Split-Steps Quantum Walk (multi-SSQW), designed to learn and load complicated probability distributions using parameterized quantum circuits (PQC) with a variational solver on classical simulators. The multi-SSQW algorithm is a modified version of the split-steps quantum walk, enhanced to incorporate a multi-agent decision-making process, rendering it suitable for modeling financial markets. The study provides theoretical descriptions and empirical investigations of the multi-SSQW algorithm to demonstrate its promising capabilities in probability distribution simulation and financial market modeling. Harnessing the advantages of quantum computation, the multi-SSQW models complex financial distributions and scenarios with high accuracy, providing valuable insights and mechanisms for financial analysis and decision-making. The multi-SSQW's key benefits include its modeling flexibility, stable convergence, and instantaneous computation. These advantages underscore its rapid modeling and prediction potential in dynamic financial markets.

Suggested Citation

  • Yen-Jui Chang & Wei-Ting Wang & Hao-Yuan Chen & Shih-Wei Liao & Ching-Ray Chang, 2023. "A novel approach for quantum financial simulation and quantum state preparation," Papers 2308.01844, arXiv.org, revised Apr 2024.
    • Handle: RePEc:arx:papers:2308.01844
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    References listed on IDEAS

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    1. Ahmed, Bouteska, 2020. "Understanding the impact of investor sentiment on the price formation process: A review of the conduct of American stock markets," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    2. Shouvanik Chakrabarti & Rajiv Krishnakumar & Guglielmo Mazzola & Nikitas Stamatopoulos & Stefan Woerner & William J. Zeng, 2020. "A Threshold for Quantum Advantage in Derivative Pricing," Papers 2012.03819, arXiv.org, revised May 2021.
    3. Joonhee Choi & Adam L. Shaw & Ivaylo S. Madjarov & Xin Xie & Ran Finkelstein & Jacob P. Covey & Jordan S. Cotler & Daniel K. Mark & Hsin-Yuan Huang & Anant Kale & Hannes Pichler & Fernando G. S. L. Br, 2023. "Preparing random states and benchmarking with many-body quantum chaos," Nature, Nature, vol. 613(7944), pages 468-473, January.
    4. Dylan Herman & Cody Googin & Xiaoyuan Liu & Alexey Galda & Ilya Safro & Yue Sun & Marco Pistoia & Yuri Alexeev, 2022. "A Survey of Quantum Computing for Finance," Papers 2201.02773, arXiv.org, revised Jun 2022.
    5. Nikitas Stamatopoulos & Guglielmo Mazzola & Stefan Woerner & William J. Zeng, 2021. "Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms," Papers 2111.12509, arXiv.org, revised Jul 2022.
    6. Vojtěch Havlíček & Antonio D. Córcoles & Kristan Temme & Aram W. Harrow & Abhinav Kandala & Jerry M. Chow & Jay M. Gambetta, 2019. "Supervised learning with quantum-enhanced feature spaces," Nature, Nature, vol. 567(7747), pages 209-212, March.
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