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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
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    References listed on IDEAS

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    1. 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.
    2. Samuel Mugel & Enrique Lizaso & Roman Orus, 2020. "Use Cases of Quantum Optimization for Finance," Papers 2010.01312, arXiv.org.
    3. 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.
    4. 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.
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    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.

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