Quantum Computing Drives Innovation in Business Intelligence Across Marketing and Finance through Systematic Review and Strategic Foresight

  • Authors

    • Arnold C. Alguno Graduate School of Business, University of the Visayas, Corner D. Jakosalem, Cebu City, 6000, Philippines and Department of Materials and Resources Engineering and Technology, Mindanao State University -Iligan Institute of Technology, Iligan City, 9200, Philippines
    • Rey Y. Capangpangan Graduate School of Business, University of the Visayas, Corner D. Jakosalem, Cebu City, 6000, Philippines and Department of Physical Sciences and Mathematics, College of Marine and Allied Sciences, Mindanao State University at Naawan, Poblacion, Naawan, Misamis Oriental
    • Yuri U. Pendon Graduate School of Business, University of the Visayas, Corner D. Jakosalem, Cebu City, 6000, Philippines
    • Rosemarie Cruz-Español Graduate School of Business, University of the Visayas, Corner D. Jakosalem, Cebu City, 6000, Philippines
    https://doi.org/10.14419/803cts61

    Received date: November 2, 2025

    Accepted date: December 8, 2025

    Published date: January 21, 2026

  • Quantum computing; business intelligence; marketing analytics; financial forecasting, systematic review, strategic foresight, hybrid quantum models
  • Abstract

    The emerging quantum computing technology (QC) transforms business intelligence by creating new methods for marketing and finance to use data in their decision-making processes. This research combines a systematic literature review with a meta-analysis of 26 peer-reviewed articles spanning from 2014 to 2025 to show how quantum computing improves predictive analytics and customer segmentation and sentiment analysis, portfolio optimization, and risk modeling. The research uses PRISMA guidelines together with strategic foresight tools to detect how hybrid quantum-classical systems replace classical models by delivering exponential speed and accuracy, and scalability benefits. The research demonstrates that QSVM, QAOA, and quantum-enhanced neural networks serve as primary methodologies to create real-time, emotionally intelligent, highly adaptive decision systems. The thematic analysis, together with conceptual mapping, shows that quantum tools are converging into a unified interdisciplinary space that combines marketing with finance and supply chains and pricing strategies. The study reveals important research deficiencies in multilingual datasets as well as benchmarking and ethical readiness, and suggests a strategic integration plan for global implementation by 2030. The research demonstrates that quantum computing represents a strategic capability that enables future-ready data-driven enterprises to enter a new business intelligence era based on quantum resilience.

  • References

    1. How, M.-L., & Cheah, S.-M. (2023). Business renaissance: Opportunities and challenges at the dawn of the quantum computing era. Businesses, 3(4), 585–605. https://doi.org/10.3390/businesses3040036
    2. Padmanaban, H. (2024). Navigating the complexity of regulations, harnessing AI/ML for precise reporting. Journal of Artificial Intelligence General Science, 3(1). https://doi.org/10.60087/jaigs.v3i1.65
    3. Saxena, A., Mancilla, J., Montalban, I., & Pere, C. (2023). Achieve optimized solutions for real-world financial problems using quantum machine learning algorithms. Packt Publishing.
    4. Ukpabi, D., et al. (2023). Framework for understanding quantum computing use cases. Futures, 154, 103277. https://doi.org/10.1016/j.futures.2023.103277
    5. Bova, F., Goldfarb, A., & Melko, R. (2021). Commercial applications of quantum computing. EPJ Quantum Technology, 8(1), 1–13.
    6. Ahmadi, A. (2023). Quantum computing and artificial intelligence: The synergy of two revolutionary technologies. Asian Journal of Electrical Sci-ences, 12(2), 15–27. https://doi.org/10.51983/ajes-2023.12.2.4118
    7. Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79. https://doi.org/10.22331/q-2018-08-06-79
    8. Efe, A. (2023). Assessment of artificial intelligence and quantum computing in the smart management information systems. Bilişim Teknolojileri Dergisi, 16(3), 177–192. https://doi.org/10.17671/gazibtd.1190670
    9. Feynman, R. P. (1982). Simulating physics with computers. International Journal of Theoretical Physics, 21, 467–488. https://doi.org/10.1007/BF02650179
    10. Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., et al. (2019). Quantum supremacy using a programmable superconducting processor. Na-ture, 574(7779), 505–510. https://doi.org/10.1038/s41586-019-1666-5
    11. Ruane, J., McAfee, A., & Oliver, W. D. (2022). Quantum computing for business leaders. Harvard Business Review, 100(1–2), 113–121.
    12. Sáez-Ortuño, L., Huertas-García, R., Forgas-Coll, S., & Sánchez-García, J. (2024). Quantum computing for market research. Journal of Innovation & Knowledge, 9, 100510. https://doi.org/10.1016/j.jik.2024.100510
    13. Sarkar, S., Khadka, U., Hossain, S., & Khan, N. (2024). Quantum machine learning for advanced data processing in business analytics: A path to-ward next-generation solutions. Advanced International Journal of Multidisciplinary Research, 2(5), 1–15. https://doi.org/10.62127/aijmr.2024.v02i05.1107
    14. Kagermann, H., Süssenguth, F., Körner, J., & Liepold, A. (2020). The innovation potential of second-generation quantum technologies (acatech IMPULSE). acatech – National Academy of Science and Engineering.
    15. Balaji, G. M., & Vadivazhagan, K. (2024). Elevating sentiment analysis with resilient grey wolf optimization-based Gaussian-enhanced quantum deep neural networks in online shopping. Journal of Theoretical and Applied Information Technology, 102(1), 214–234.
    16. Bar, N. F., Yenilmez, M., Aksu, S., & Karakose, M. (2024). A quantum computing-based approach for sentiment analysis in bilateral conversations. 2024 IEEE International Conference on Information Technology (IT), 1–7. https://doi.org/10.1109/IT61232.2024.10475718
    17. Buonaiuto, G., Guarasci, R., & Esposito, M. (2024). Quantum transfer learning for sentiment analysis: An experiment on an Italian corpus. Proceed-ings of the QUASAR '24 Conference. https://doi.org/10.1145/3660318.3660325
    18. Singh, J., Bhangu, K. S., Ali, F., AlZubi, A. A., & Shah, B. (2025). Quantum-inspired framework for big data analytics: Evaluating the impact of movie trailers and its financial returns. Journal of Big Data, 12(22). https://doi.org/10.1186/s40537-025-01069-x
    19. Taiwo, I., Ogunbajo, A., & Abidola, A. Q. (2025). Quantum computing-enhanced AI systems for advanced business intelligence applications. In-ternational Journal of Science and Research Archive, 14(1), 1839–1847. https://doi.org/10.30574/ijsra.2025.14.1.0314
    20. Rebentrost, P., Gupt, B., & Bromley, T. R. (2018). Quantum computational finance: Monte Carlo pricing of financial derivatives. Physical Review A, 98(2), 022321. https://doi.org/10.1103/PhysRevA.98.022321
    21. He, B., Ji, X., & Lv, J. (2023). Optimization of credit scorecard combinations based on the quantum annealing algorithm. Highlights in Science, En-gineering and Technology, 61, 57–62.
    22. Orús, R., Mugel, S., & Lizaso, E. (2019). Quantum computing for finance: Overview and prospects. Reviews in Physics, 4, 100028. https://doi.org/10.1016/j.revip.2019.100028
    23. Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2010). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. International Journal of Surgery, 8(5), 336–341.
    24. Wohlin, C., Mendes, E., Felizardo, K. R., & Kalinowski, M. (2020). Guidelines for the search strategy to update systematic literature reviews in software engineering. Information and Software Technology, 127, 106366
    25. Møller, A. M., & Myles, P. S. (2016). What makes a good systematic review and meta-analysis? British Journal of Anaesthesia, 117(4), 428–430.
    26. Mourão, E., Pimentel, J. F., Murta, L., Kalinowski, M., Mendes, E., & Wohlin, C. (2020). On the performance of hybrid search strategies for sys-tematic literature reviews in software engineering. Information and Software Technology, 123, 106294
    27. Gill, S. S., Kumar, A., Singh, H., Singh, M., Kaur, K., Usman, M., & Buyya, R. (2022). Quantum computing: A taxonomy, systematic review and future directions. Software: Practice and Experience, 52(1), 1–14.
    28. Wang, J., Shen, L., & Zhou, W. (2021). A bibliometric analysis of quantum computing literature: Mapping and evidences from Scopus. Technologi-cal Analysis & Strategic Management, 33(12), 1347–1363.
    29. Mills, K., Creedy, D. K., & West, R. (2018). Experiences and outcomes of health professional students undertaking education on Indigenous health: A systematic integrative literature review. Nurse Education Today, 69, 149–158.
    30. Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering – A systematic literature review. Information and Software Technology, 51(1), 7–15.
    31. Dadkhah, M., Araban, S., & Paydar, S. (2020). A systematic literature review on semantic web enabled software testing. Journal of Systems and Software, 162, 110485.
    32. Badampudi, D., Wohlin, C., & Petersen, K. (2015). Experiences from using snowballing and database searches in systematic literature studies. Pro-ceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering, 1–10.
    33. Engström, H., Berg Marklund, B., Backlund, P., & Toftedahl, M. (2018). Game development from a software and creative product perspective: A quantitative literature review approach. Entertainment Computing, 27, 10–22.
    34. Bielan, O., Zahreba, I., Ochkas, M., Kantsir, I., & Zelenskyi, A. (2024). Methodological support for financial risk management in enterprises. Paki-stan Journal of Life and Social Sciences, 22(2), 9893–9902. https://doi.org/10.57239/PJLSS-2024-22.2.00747
    35. Aljaafari, M. (2023). Quantum computing for social business optimization: A practitioner’s perspective [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-2795910/v1
    36. Majumdar, M. G. (2023). Quantum 3.0: Quantum learning, quantum heuristics and beyond. arXiv preprint. https://doi.org/10.48550/arXiv.2305.18091
    37. Solikhun, A., Widodo, D. S., & Astuti, R. (2023). Analysis of the quantum perceptron algorithm for classification of bank marketing data. AIP Conference Proceedings, 2714(1), 030002. https://doi.org/10.1063/5.0129287
    38. Tychola, K. A., Kalampokas, T., & Papakostas, G. A. (2023). Quantum machine learning - An overview. Electronics, 12(11), 2379. https://doi.org/10.3390/electronics12112379
    39. Li, X., Dinh, T. N., & Thai, M. T. (2023). Quantum social computing approaches for influence maximization. IEEE GLOBECOM, 2022, Vietnam
    40. Ganguly, S., Morapakula, S. N., & Pozo Coronado, L. M. (2023). Quantum natural language processing based sentiment analysis using lambeq toolkit. IEEE ICPC2T Conference, 2022, India
    41. Khurana, R. (2022). Applications of quantum computing in telecom e-commerce: Analysis of QKD, QAOA, and QML. Quarterly Journal of Emerging Technologies and Innovations, 7(9), 1–15.
    42. Rivas, A., et al. (2021). Hybrid quantum variational autoencoders for representation learning. IEEE CSCI, 52–57. https://doi.org/10.1109/CSCI54926.2021.00085
    43. Sundar, G. N., & Narmadha, D. (2021). Performance optimization of quantum computing applications using D-Wave Two quantum computer. In Proceedings of the Fifth International Conference on Trends in Electronics and Informatics (ICOEI). IEEE.
    44. Owolabi, O. S., Uche, P. C., Adeniken, N. T., Tanoh, V. G., & Emi-Johnson, O. G. (2024). Quantum computing applications, challenges, and pro-spects in financial portfolio optimization. World Journal of Advanced Research and Reviews, 22(3), 14–22. https://doi.org/10.30574/wjarr.2024.22.3.1648
    45. Yang, X., Zhu, J., & De Meo, P. (2024). A quantum-like zero-shot approach for sentiment analysis in finance. Journal of Intelligent Information Sys-tems, 63(3), 705–721. https://doi.org/10.1007/s10844-024-00912-6
    46. Li, Y., Qu, Y., Zhou, R.-G., & Zhang, J. (2025). QMLSC: A quantum multimodal learning model for sentiment classification. Information Fusion, 120, Article 103049. https://doi.org/10.1016/j.inffus.2025.103049
    47. Geetha, V., Saxena, S., Tamboli, A., & Kadam, D. (2024). Parallelized clustering-based optimization for CVRP: Leveraging quantum computing and GPU acceleration. 2024 IEEE CSITSS. https://doi.org/10.1109/CSITSS64042.2024.10817035
    48. Nguyen, H. T., et al. (2023). Quantum social computing approaches for influence maximization. IEEE Transactions on Quantum Engineering, 4(1), 1–5. https://doi.org/10.1109/TQE.2023.10477831
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  • How to Cite

    Alguno, A. C. ., Capangpangan , R. Y. ., Pendon, Y. U. . ., & Cruz-Español , R. (2026). Quantum Computing Drives Innovation in Business Intelligence Across Marketing and Finance through Systematic Review and Strategic Foresight. International Journal of Accounting and Economics Studies, 13(1), 263-277. https://doi.org/10.14419/803cts61