IDEAS home Printed from https://ideas.repec.org/a/wly/revfec/v43y2025i1p62-77.html

Market informed portfolio optimization methods with hybrid quantum computing

Author

Listed:
  • Giancarlo Martínez Salirrosas
  • Jinglun Gao
  • Arthur Yu
  • Anish Ravi Verma

Abstract

This document presents a portfolio optimization framework that employs a hybrid quantum computing algorithm and a futures market sentiment indicator—The Market Sentiment Meter (MSM) variable, developed jointly by CME Group and 1QBit. The methodology used was the Variational Quantum Eigensolver (VQE). The work presented here is divided into four portfolio optimization problem formulations, of binary and continuous variable formulations, determining which assets to pick their weights. This work demonstrates that adding the MSM variable can improve the performance of hybrid quantum solutions, by informing the asset selection problem with market environment information through the four MSM states.

Suggested Citation

  • Giancarlo Martínez Salirrosas & Jinglun Gao & Arthur Yu & Anish Ravi Verma, 2025. "Market informed portfolio optimization methods with hybrid quantum computing," Review of Financial Economics, John Wiley & Sons, vol. 43(1), pages 62-77, January.
    • Handle: RePEc:wly:revfec:v:43:y:2025:i:1:p:62-77
    DOI: 10.1002/rfe.1219
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/rfe.1219
    Download Restriction: no
    File URL: https://libkey.io/10.1002/rfe.1219?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Gary Kochenberger & Jin-Kao Hao & Fred Glover & Mark Lewis & Zhipeng Lü & Haibo Wang & Yang Wang, 2014. "The unconstrained binary quadratic programming problem: a survey," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 58-81, July.
    2. Alberto Peruzzo & Jarrod McClean & Peter Shadbolt & Man-Hong Yung & Xiao-Qi Zhou & Peter J. Love & Alán Aspuru-Guzik & Jeremy L. O’Brien, 2014. "A variational eigenvalue solver on a photonic quantum processor," Nature Communications, Nature, vol. 5(1), pages 1-7, September.
    3. Gili Rosenberg & Poya Haghnegahdar & Phil Goddard & Peter Carr & Kesheng Wu & Marcos L'opez de Prado, 2015. "Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer," Papers 1508.06182, arXiv.org, revised Aug 2016.
    4. Samuel Mugel & Enrique Lizaso & Roman Orus, 2020. "Use Cases of Quantum Optimization for Finance," Papers 2010.01312, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vincent Gurgul & Ying Chen & Stefan Lessmann, 2026. "Variational Quantum Circuit-Based Reinforcement Learning for Dynamic Portfolio Optimization," Papers 2601.18811, arXiv.org, revised Jan 2026.

    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. 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.
    2. Lins, Isis Didier & Araújo, Lavínia Maria Mendes & Maior, Caio Bezerra Souto & Teixeira, Erico Souza & Bezerra, Pâmela Thays Lins & Moura, Márcio José das Chagas & Droguett, Enrique López, 2025. "Quantum-based optimization methods for the linear redundancy allocation problem: A comparative analysis," Reliability Engineering and System Safety, Elsevier, vol. 262(C).
    3. 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).
    4. Martin Vesely, 2023. "Finding the Optimal Currency Composition of Foreign Exchange Reserves with a Quantum Computer," Papers 2303.01909, arXiv.org.
    5. 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.
    6. Byron Tasseff & Tameem Albash & Zachary Morrell & Marc Vuffray & Andrey Y. Lokhov & Sidhant Misra & Carleton Coffrin, 2024. "On the emerging potential of quantum annealing hardware for combinatorial optimization," Journal of Heuristics, Springer, vol. 30(5), pages 325-358, December.
    7. Yuri Alexeev & Marwa H. Farag & Taylor L. Patti & Mark E. Wolf & Natalia Ares & Alán Aspuru-Guzik & Simon C. Benjamin & Zhenyu Cai & Shuxiang Cao & Christopher Chamberland & Zohim Chandani & Federico , 2025. "Artificial intelligence for quantum computing," Nature Communications, Nature, vol. 16(1), pages 1-19, December.
    8. Nazlı Uğur Köylüoğlu & Swarnadeep Majumder & Mirko Amico & Sarah Mostame & Ewout van den Berg & M. A. Rajabpour & Zlatko Minev & Khadijeh Najafi, 2026. "Measuring central charge on a universal quantum processor," Nature Communications, Nature, vol. 17(1), pages 1-8, December.
    9. Bahram Alidaee & Haibo Wang, 2017. "A note on heuristic approach based on UBQP formulation of the maximum diversity problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(1), pages 102-110, January.
    10. Abbas, Amira & Ambainis, Andris & Augustino, Brandon & Baertschi, Andreas & Buhrman, Harry & Coffrin, Carleton & Cortiana, Giorgio & Dunjko, Vedran & Egger, Daniel J. & Elmegreen, Bruce G. & Franco, N, 2024. "Challenges and opportunities in quantum optimization," Other publications TiSEM eb4b8a22-9322-4251-8802-9, Tilburg University, School of Economics and Management.
    11. Fu, Wei & Xie, Haipeng & Xin, Yu, 2026. "Quantum-accelerated post-event restoration through quantum surrogate absolute-value Lagrangian relaxation," Reliability Engineering and System Safety, Elsevier, vol. 266(PA).
    12. Wang, Haibo & Alidaee, Bahram, 2019. "Effective heuristic for large-scale unrelated parallel machines scheduling problems," Omega, Elsevier, vol. 83(C), pages 261-274.
    13. Eric R. Anschuetz & Bobak T. Kiani, 2022. "Quantum variational algorithms are swamped with traps," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    14. Enrico Fontana & Dylan Herman & Shouvanik Chakrabarti & Niraj Kumar & Romina Yalovetzky & Jamie Heredge & Shree Hari Sureshbabu & Marco Pistoia, 2024. "Characterizing barren plateaus in quantum ansätze with the adjoint representation," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    15. Aritra Sarkar & Zaid Al-Ars & Koen Bertels, 2021. "QuASeR: Quantum Accelerated de novo DNA sequence reconstruction," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-23, April.
    16. Howard Su & Huan-Hsin Tseng, 2025. "On Quantum BSDE Solver for High-Dimensional Parabolic PDEs," Papers 2506.14612, arXiv.org, revised Sep 2025.
    17. Fred Glover & Gary Kochenberger & Rick Hennig & Yu Du, 2022. "Quantum bridge analytics I: a tutorial on formulating and using QUBO models," Annals of Operations Research, Springer, vol. 314(1), pages 141-183, July.
    18. Martí, Rafael & Martínez-Gavara, Anna & Pérez-Peló, Sergio & Sánchez-Oro, Jesús, 2022. "A review on discrete diversity and dispersion maximization from an OR perspective," European Journal of Operational Research, Elsevier, vol. 299(3), pages 795-813.
    19. Juan S. Borrero & Colin Gillen & Oleg A. Prokopyev, 2017. "Fractional 0–1 programming: applications and algorithms," Journal of Global Optimization, Springer, vol. 69(1), pages 255-282, September.
    20. Soodeh Habibi & Michal Kočvara & Michael Stingl, 2025. "On the numerical solution of Lasserre relaxations of unconstrained binary quadratic optimization problem," Journal of Global Optimization, Springer, vol. 93(1), pages 63-85, September.

    More about this item

    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:wly:revfec:v:43:y:2025:i:1:p:62-77. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1873-5924 .

    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.