Quantum Computing Drives Innovation in Business Intelligence Across Marketing and Finance through Systematic Review and Strategic Foresight
-
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
- 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
- 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
- Saxena, A., Mancilla, J., Montalban, I., & Pere, C. (2023). Achieve optimized solutions for real-world financial problems using quantum machine learning algorithms. Packt Publishing.
- Ukpabi, D., et al. (2023). Framework for understanding quantum computing use cases. Futures, 154, 103277. https://doi.org/10.1016/j.futures.2023.103277
- Bova, F., Goldfarb, A., & Melko, R. (2021). Commercial applications of quantum computing. EPJ Quantum Technology, 8(1), 1–13.
- 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
- Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79. https://doi.org/10.22331/q-2018-08-06-79
- 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
- Feynman, R. P. (1982). Simulating physics with computers. International Journal of Theoretical Physics, 21, 467–488. https://doi.org/10.1007/BF02650179
- 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
- Ruane, J., McAfee, A., & Oliver, W. D. (2022). Quantum computing for business leaders. Harvard Business Review, 100(1–2), 113–121.
- 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
- 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
- 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.
- 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.
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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.
- 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
- Møller, A. M., & Myles, P. S. (2016). What makes a good systematic review and meta-analysis? British Journal of Anaesthesia, 117(4), 428–430.
- 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
- 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.
- 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.
- 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.
- 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.
- Dadkhah, M., Araban, S., & Paydar, S. (2020). A systematic literature review on semantic web enabled software testing. Journal of Systems and Software, 162, 110485.
- 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.
- 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.
- 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
- 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
- Majumdar, M. G. (2023). Quantum 3.0: Quantum learning, quantum heuristics and beyond. arXiv preprint. https://doi.org/10.48550/arXiv.2305.18091
- 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
- Tychola, K. A., Kalampokas, T., & Papakostas, G. A. (2023). Quantum machine learning - An overview. Electronics, 12(11), 2379. https://doi.org/10.3390/electronics12112379
- Li, X., Dinh, T. N., & Thai, M. T. (2023). Quantum social computing approaches for influence maximization. IEEE GLOBECOM, 2022, Vietnam
- 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
- 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.
- Rivas, A., et al. (2021). Hybrid quantum variational autoencoders for representation learning. IEEE CSCI, 52–57. https://doi.org/10.1109/CSCI54926.2021.00085
- 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.
- 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
- 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
- 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
- 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
- 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
-
Downloads
-
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
