IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2308.01844.html
   My bibliography  Save this paper

A novel approach for quantum financial simulation and quantum state preparation

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2308.01844
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yen-Jui Chang & Wei-Ting Wang & Hao-Yuan Chen & Shih-Wei Liao & Ching-Ray Chang, 2023. "Preparing random state for quantum financing with quantum walks," Papers 2302.12500, arXiv.org, revised Mar 2023.
    2. Abha Naik & Esra Yeniaras & Gerhard Hellstern & Grishma Prasad & Sanjay Kumar Lalta Prasad Vishwakarma, 2023. "From Portfolio Optimization to Quantum Blockchain and Security: A Systematic Review of Quantum Computing in Finance," Papers 2307.01155, arXiv.org.
    3. 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.
    4. Siyi Liu & Xin Liu & Chuancai Zhang & Lingli Zhang, 2023. "Institutional and individual investors' short‐term reactions to the COVID‐19 crisis in China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(4), pages 4333-4355, December.
    5. Koichi Miyamoto & Kenji Kubo, 2021. "Pricing multi-asset derivatives by finite difference method on a quantum computer," Papers 2109.12896, arXiv.org.
    6. Gric, Zuzana & Bajzík, Josef & Badura, Ondřej, 2023. "Does sentiment affect stock returns? A meta-analysis across survey-based measures," International Review of Financial Analysis, Elsevier, vol. 89(C).
    7. Mark-Oliver Wolf & Tom Ewen & Ivica Turkalj, 2023. "Quantum Architecture Search for Quantum Monte Carlo Integration via Conditional Parameterized Circuits with Application to Finance," Papers 2304.08793, arXiv.org, revised Sep 2023.
    8. Roman Rietsche & Christian Dremel & Samuel Bosch & Léa Steinacker & Miriam Meckel & Jan-Marco Leimeister, 2022. "Quantum computing," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2525-2536, December.
    9. Vicente Moret-Bonillo & Samuel Magaz-Romero & Eduardo Mosqueira-Rey, 2022. "Quantum Computing for Dealing with Inaccurate Knowledge Related to the Certainty Factors Model," Mathematics, MDPI, vol. 10(2), pages 1-21, January.
    10. Rana Muhammad Adnan & Abolfazl Jaafari & Aadhityaa Mohanavelu & Ozgur Kisi & Ahmed Elbeltagi, 2021. "Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm," Sustainability, MDPI, vol. 13(11), pages 1-19, May.
    11. Jianjun Chen & Yongming Li & Ariel Neufeld, 2023. "Quantum Monte Carlo algorithm for solving Black-Scholes PDEs for high-dimensional option pricing in finance and its complexity analysis," Papers 2301.09241, arXiv.org, revised Apr 2024.
    12. Francesca Cibrario & Or Samimi Golan & Giacomo Ranieri & Emanuele Dri & Mattia Ippoliti & Ron Cohen & Christian Mattia & Bartolomeo Montrucchio & Amir Naveh & Davide Corbelletto, 2024. "Quantum Amplitude Loading for Rainbow Options Pricing," Papers 2402.05574, arXiv.org, revised Feb 2024.
    13. Jinge Bao & Patrick Rebentrost, 2022. "Fundamental theorem for quantum asset pricing," Papers 2212.13815, arXiv.org, revised Apr 2023.
    14. Taylor, Richard D., 2020. "Quantum Artificial Intelligence: A “precautionary” U.S. approach?," Telecommunications Policy, Elsevier, vol. 44(6).
    15. Olawale Ayoade & Pablo Rivas & Javier Orduz, 2022. "Artificial Intelligence Computing at the Quantum Level," Data, MDPI, vol. 7(3), pages 1-16, February.
    16. Wei-Ming Li & Shi-Ju Ran, 2022. "Non-Parametric Semi-Supervised Learning in Many-Body Hilbert Space with Rescaled Logarithmic Fidelity," Mathematics, MDPI, vol. 10(6), pages 1-15, March.
    17. Jo~ao F. Doriguello & Alessandro Luongo & Jinge Bao & Patrick Rebentrost & Miklos Santha, 2021. "Quantum algorithm for stochastic optimal stopping problems with applications in finance," Papers 2111.15332, arXiv.org, revised Jul 2023.
    18. Ajagekar, Akshay & You, Fengqi, 2022. "Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    19. Daniel J. Egger & Claudio Gambella & Jakub Marecek & Scott McFaddin & Martin Mevissen & Rudy Raymond & Andrea Simonetto & Stefan Woerner & Elena Yndurain, 2020. "Quantum Computing for Finance: State of the Art and Future Prospects," Papers 2006.14510, arXiv.org, revised Jan 2021.
    20. Skavysh, Vladimir & Priazhkina, Sofia & Guala, Diego & Bromley, Thomas R., 2023. "Quantum monte carlo for economics: Stress testing and macroeconomic deep learning," Journal of Economic Dynamics and Control, Elsevier, vol. 153(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2308.01844. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.