Advanced Optimization for Big Data Streams with Quantum Insights for Real-time Big Data Analytics
Abstract
Big data analytics encounters scalability, latency, and privacy challenges, especially within real-time streaming contexts. We propose the Privacy-Aware Quantum Stream (PAQS), a distributed framework inspired by quantum principles, to overcome these obstacles. PAQS utilizes quantum superposition to effectively represent high-dimensional data, quantum entanglement for sophisticated correlation analysis and anomaly detection, and federated learning combined with homomorphic encryption to maintain privacy without compromising performance. The adaptive switching mechanism balances quantum-inspired and classical processing according to sensitivity and dimensionality criteria. Experiments are conducted on three datasets—OpenStreetMap, MIMIC-III, and KITTI, which show significant improvements: a throughput of 2.53 TB/sec, a 60 % reduction in latency, an anomaly detection accuracy of 92.3 %, and an 85.4 % decrease in privacy violations when compared to baselines. These findings validate that PAQS provides consistent, secure, and scalable real-time analytics, positioning it as a strong solution for smart cities, healthcare, and autonomous transportation applications.
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Agarwal, P., & Alam, M. (2021). Exploring quantum computing to revolutionize big data analytics for various industrial sectors. In Big Data Analytics (pp. 113–130). Auerbach Publications.
Al-Shiakhli, S. (2019). Big data analytics: A literature review perspective.
Andreou, A., Mavromoustakis, C. X., Markakis, E., Bourdena, A., & Mastorakis, G. (2025). Sustainable AI With Quantum-Inspired Optimization: Enabling End-to-End Automation in Cloud-Edge Computing. IEEE Access, 13, 54622–54635. https://doi.org/10.1109/ACCESS.2025.3554024
Ashraf, S., Memon, F., Akram, F., Siddique, T. A., & Aziz, Y. (2025). Modeling wireless sensors network using shrewd neural networks. In Blockchain and Digital Twin for Smart Healthcare (pp. 373–393). Elsevier.
Asri, H., Mousannif, H., Al Moatassime, H., & Noel, T. (2015, June). Big data in healthcare: Challenges and opportunities. In 2015 International Conference on Cloud Technologies and Applications (CloudTech) (pp. 1–7). IEEE. https://doi.org/10.1109/CloudTech.2015.7337020
Avila, A., Reiser, R. H., Yamin, A. C., & Pilla, M. L. (2017, October). Efficient In-Situ Quantum Computing Simulation of Shor's and Grover's Algorithms. In 2017 International Symposium on Computer Architecture and High Performance Computing Workshops (SBAC-PADW) (pp. 61–66). IEEE.
Bishwas, A. K., Mani, A., & Palade, V. (2018). An all-pair quantum SVM approach for big data multiclass classification. Quantum Information Processing, 17, 1–16.
Gautam, A., & Chatterjee, I. (2020). Big data and cloud computing: A critical review. International Journal of Operations Research and Information Systems (IJORIS), 11(3), 19–38. https://doi.org/10.4018/IJORIS.2020070102
Havlíček, V., Córcoles, A. D., Temme, K., Harrow, A. W., Kandala, A., Chow, J. M., & Gambetta, J. M. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747), 209–212. https://doi.org/10.1038/s41586-019-0980-2
Khanal, B., Orduz, J., Rivas, P., & Baker, E. (2023). Supercomputing leverages quantum machine learning and Grover's algorithm. The Journal of Supercomputing, 79(6), 6918–6940.
Khurana, R. A. H. U. L. (2022). Applications of quantum computing in telecom e-commerce: Analysis of QKD, QAOA, and QML for data encryption, speed optimization, and AI-driven customer experience. Quarterly Journal of Emerging Technologies and Innovations, 7(9), 1–15.
Knoppers, B. M., & Thorogood, A. M. (2017). Ethics and big data in health. Current Opinion in Systems Biology, 4, 53–57. https://doi.org/10.1016/j.coisb.2017.07.001
Liu, J., & Jiang, L. (2024). Quantum data center: Perspectives. IEEE Network.
Ma, L., Gupta, R. K., & Onyema, E. M. (2022). Optimization of intelligent network information management system under big data and cloud computing. Scalable Computing: Practice and Experience, 23(3), 91–101.
Mohbey, K. K. (2019). An efficient framework for smart city using big data technologies and internet of things. In Progress in Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2017, Volume 2 (pp. 319–328). Springer Singapore.
Mohbey, K. K., & Kumar, S. (2022). The impact of big data in predictive analytics towards technological development in cloud computing. International Journal of Engineering Systems Modelling and Simulation, 13(1), 61–75.
Osaba, E., Villar-Rodriguez, E., & Oregi, I. (2022). A systematic literature review of quantum computing for routing problems. IEEE Access, 10, 55805–55817.
Qadir, J., Ali, A., ur Rasool, R., Zwitter, A., Sathiaseelan, A., & Crowcroft, J. (2016). Crisis analytics: Big data-driven crisis response. Journal of International Humanitarian Action, 1, 1–21. https://doi.org/10.1186/s41018-016-0013-9
Ramezani, S. B., Sommers, A., Manchukonda, H. K., Rahimi, S., & Amirlatifi, A. (2020, July). Machine learning algorithms in quantum computing: A survey. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE.
Rath, M., & Date, H. (2024). Quantum data encoding: A comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy. EPJ Quantum Technology, 11(1), 72.
Sandhu, A. K. (2021). Big data with cloud computing: Discussions and challenges. Big Data Mining and Analytics, 5(1), 32–40.
Shaikh, T. A., & Ali, R. (2016, December). Quantum computing in big data analytics: A survey. In 2016 IEEE International Conference on Computer and Information Technology (CIT) (pp. 112–115). IEEE.
Shi, Y., Tang, J., & Wan, D. (2022). Evaluation of economic recovery capability under the background of COVID-19: Application of a big data information processing model. In MATEC Web of Conferences (Vol. 355, p. 02050). EDP Sciences. https://doi.org/10.1051/matecconf/202235502050
Taherdoost, H. (2024). A systematic review of big data innovations in smart grids. Results in Engineering, 102132. https://doi.org/10.1016/j.rineng.2024.102132
Tsai, C. W., Lai, C. F., Chao, H. C., & Vasilakos, A. V. (2015). Big data analytics: A survey. Journal of Big Data, 2, 1–32.
Wang, Y. (2022). When quantum computation meets data science: Making data science quantum.
Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big data and cloud computing: Innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13–53. https://doi.org/10.1080/17538947.2016.1239771
Al-Shiakhli, S. (2019). Big data analytics: A literature review perspective.
Andreou, A., Mavromoustakis, C. X., Markakis, E., Bourdena, A., & Mastorakis, G. (2025). Sustainable AI With Quantum-Inspired Optimization: Enabling End-to-End Automation in Cloud-Edge Computing. IEEE Access, 13, 54622–54635. https://doi.org/10.1109/ACCESS.2025.3554024
Ashraf, S., Memon, F., Akram, F., Siddique, T. A., & Aziz, Y. (2025). Modeling wireless sensors network using shrewd neural networks. In Blockchain and Digital Twin for Smart Healthcare (pp. 373–393). Elsevier.
Asri, H., Mousannif, H., Al Moatassime, H., & Noel, T. (2015, June). Big data in healthcare: Challenges and opportunities. In 2015 International Conference on Cloud Technologies and Applications (CloudTech) (pp. 1–7). IEEE. https://doi.org/10.1109/CloudTech.2015.7337020
Avila, A., Reiser, R. H., Yamin, A. C., & Pilla, M. L. (2017, October). Efficient In-Situ Quantum Computing Simulation of Shor's and Grover's Algorithms. In 2017 International Symposium on Computer Architecture and High Performance Computing Workshops (SBAC-PADW) (pp. 61–66). IEEE.
Bishwas, A. K., Mani, A., & Palade, V. (2018). An all-pair quantum SVM approach for big data multiclass classification. Quantum Information Processing, 17, 1–16.
Gautam, A., & Chatterjee, I. (2020). Big data and cloud computing: A critical review. International Journal of Operations Research and Information Systems (IJORIS), 11(3), 19–38. https://doi.org/10.4018/IJORIS.2020070102
Havlíček, V., Córcoles, A. D., Temme, K., Harrow, A. W., Kandala, A., Chow, J. M., & Gambetta, J. M. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747), 209–212. https://doi.org/10.1038/s41586-019-0980-2
Khanal, B., Orduz, J., Rivas, P., & Baker, E. (2023). Supercomputing leverages quantum machine learning and Grover's algorithm. The Journal of Supercomputing, 79(6), 6918–6940.
Khurana, R. A. H. U. L. (2022). Applications of quantum computing in telecom e-commerce: Analysis of QKD, QAOA, and QML for data encryption, speed optimization, and AI-driven customer experience. Quarterly Journal of Emerging Technologies and Innovations, 7(9), 1–15.
Knoppers, B. M., & Thorogood, A. M. (2017). Ethics and big data in health. Current Opinion in Systems Biology, 4, 53–57. https://doi.org/10.1016/j.coisb.2017.07.001
Liu, J., & Jiang, L. (2024). Quantum data center: Perspectives. IEEE Network.
Ma, L., Gupta, R. K., & Onyema, E. M. (2022). Optimization of intelligent network information management system under big data and cloud computing. Scalable Computing: Practice and Experience, 23(3), 91–101.
Mohbey, K. K. (2019). An efficient framework for smart city using big data technologies and internet of things. In Progress in Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2017, Volume 2 (pp. 319–328). Springer Singapore.
Mohbey, K. K., & Kumar, S. (2022). The impact of big data in predictive analytics towards technological development in cloud computing. International Journal of Engineering Systems Modelling and Simulation, 13(1), 61–75.
Osaba, E., Villar-Rodriguez, E., & Oregi, I. (2022). A systematic literature review of quantum computing for routing problems. IEEE Access, 10, 55805–55817.
Qadir, J., Ali, A., ur Rasool, R., Zwitter, A., Sathiaseelan, A., & Crowcroft, J. (2016). Crisis analytics: Big data-driven crisis response. Journal of International Humanitarian Action, 1, 1–21. https://doi.org/10.1186/s41018-016-0013-9
Ramezani, S. B., Sommers, A., Manchukonda, H. K., Rahimi, S., & Amirlatifi, A. (2020, July). Machine learning algorithms in quantum computing: A survey. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE.
Rath, M., & Date, H. (2024). Quantum data encoding: A comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy. EPJ Quantum Technology, 11(1), 72.
Sandhu, A. K. (2021). Big data with cloud computing: Discussions and challenges. Big Data Mining and Analytics, 5(1), 32–40.
Shaikh, T. A., & Ali, R. (2016, December). Quantum computing in big data analytics: A survey. In 2016 IEEE International Conference on Computer and Information Technology (CIT) (pp. 112–115). IEEE.
Shi, Y., Tang, J., & Wan, D. (2022). Evaluation of economic recovery capability under the background of COVID-19: Application of a big data information processing model. In MATEC Web of Conferences (Vol. 355, p. 02050). EDP Sciences. https://doi.org/10.1051/matecconf/202235502050
Taherdoost, H. (2024). A systematic review of big data innovations in smart grids. Results in Engineering, 102132. https://doi.org/10.1016/j.rineng.2024.102132
Tsai, C. W., Lai, C. F., Chao, H. C., & Vasilakos, A. V. (2015). Big data analytics: A survey. Journal of Big Data, 2, 1–32.
Wang, Y. (2022). When quantum computation meets data science: Making data science quantum.
Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big data and cloud computing: Innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13–53. https://doi.org/10.1080/17538947.2016.1239771
Acharya, M., & Kumar Mohbey, K. (2025). Advanced Optimization for Big Data Streams with Quantum Insights for Real-time Big Data Analytics. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 14, e32876. https://doi.org/10.14201/adcaij.32876
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