IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2308.01844.html

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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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. Abha Satyavan Naik & Esra Yeniaras & Gerhard Hellstern & Grishma Prasad & Sanjay Kumar Lalta Prasad Vishwakarma, 2025. "From portfolio optimization to quantum blockchain and security: a systematic review of quantum computing in finance," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-67, December.
    5. Deepak Ranga & Aryan Rana & Sunil Prajapat & Pankaj Kumar & Kranti Kumar & Athanasios V. Vasilakos, 2024. "Quantum Machine Learning: Exploring the Role of Data Encoding Techniques, Challenges, and Future Directions," Mathematics, MDPI, vol. 12(21), pages 1-32, October.
    6. 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.
    7. De Backer, Stijn & Rocha, Luis E.C. & Ryckebusch, Jan & Schoors, Koen, 2025. "On the potential of quantum walks for modeling financial return distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 657(C).
    8. Martin Ringbauer & Marcel Hinsche & Thomas Feldker & Paul K. Faehrmann & Juani Bermejo-Vega & Claire L. Edmunds & Lukas Postler & Roman Stricker & Christian D. Marciniak & Michael Meth & Ivan Pogorelo, 2025. "Verifiable measurement-based quantum random sampling with trapped ions," Nature Communications, Nature, vol. 16(1), pages 1-9, December.
    9. Koichi Miyamoto & Kenji Kubo, 2021. "Pricing multi-asset derivatives by finite difference method on a quantum computer," Papers 2109.12896, arXiv.org.
    10. 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).
    11. Liyun Su & Dan Li & Dongyang Qiu, 2025. "BLS-QLSTM: a novel hybrid quantum neural network for stock index forecasting," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-15, December.
    12. 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.
    13. 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.
    14. 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.
    15. Madilyn Louisa & Gumgum Darmawan & Bertho Tantular, 2025. "Enhancing Stock Price Forecasting with CNN-BiGRU-Attention: A Case Study on INDY," Mathematics, MDPI, vol. 13(13), pages 1-16, June.
    16. Bikram Khanal & Pablo Rivas, 2024. "A Modified Depolarization Approach for Efficient Quantum Machine Learning," Mathematics, MDPI, vol. 12(9), pages 1-17, May.
    17. Kamila Zaman & Alberto Marchisio & Muhammad Kashif & Muhammad Shafique, 2024. "PO-QA: A Framework for Portfolio Optimization using Quantum Algorithms," Papers 2407.19857, arXiv.org.
    18. Zribi, Wissal & Boufateh, Talel & Lahouel, Bechir Ben & Urom, Christian, 2024. "Uncertainty shocks, investor sentiment and environmental performance: Novel evidence from a PVAR approach," International Review of Financial Analysis, Elsevier, vol. 93(C).
    19. Isaiah Hull & Or Sattath & Eleni Diamanti & Göran Wendin, 2024. "Quantum Algorithms," Contributions to Economics, in: Quantum Technology for Economists, chapter 0, pages 37-103, Springer.
    20. Fangjun Hu & Saeed A. Khan & Nicholas T. Bronn & Gerasimos Angelatos & Graham E. Rowlands & Guilhem J. Ribeill & Hakan E. Türeci, 2024. "Overcoming the coherence time barrier in quantum machine learning on temporal data," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

    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.