Quantum Fusion: Enhancing Predictive Power through Entangled Machine Learning Ensembles

Authors

  • Seema Babusing Rathod, Rupali Atul Mahajan, Archana Chougule, Smita Bhagwat, Pallavi Rege, Nitin Sakhare

Keywords:

Quantum Computing, Machine Learning, Quantum Fusion, Novel Paradigm

Abstract

In the ever-evolving landscape of machine learning, this study explores the integration of quantum computing principles into predictive modelling through the novel concept of Quantum Fusion. By leveraging the unique properties of entanglement, Quantum Fusion enhances the predictive power of machine learning ensembles. The study demonstrates significant improvements in key performance metrics, including accuracy, precision, recall, and F1 score, when compared to traditional machine learning ensembles. The introduction of Quantum Entanglement emerges as a pivotal factor in achieving these advancements. The results not only underscore the superiority of Quantum Fusion but also contribute to the growing body of research on quantum-enhanced machine learning. As quantum computing continues to advance, the implications of Quantum Fusion have the potential to redefine the capabilities of predictive modelling, opening new frontiers in solving complex problems.

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References

Al-Hashedi, A., Al-Fuhaidi, B., Mohsen, A. M., Ali, Y., Gamal Al-Kaf, H. A., Al-Sorori, W., & Maqtary, N. (2022). Ensemble Classifiers for Arabic Sentiment Analysis of Social Network (Twitter Data) towards COVID-19-Related Conspiracy Theories. Applied Computational Intelligence and Soft Computing, 2022, 1–10. https://doi.org/10.1155/2022/6614730

Alsayat, A., & Ahmadi, H. (2023). A Hybrid Method Using Ensembles of Neural Network and Text Mining for Learner Satisfaction Analysis from Big Datasets in Online Learning Platform. Neural Processing Letters, 55(3), 3267–3303. https://doi.org/10.1007/s11063-022-11009-y

Banchi, L., Fingerhuth, M., Babej, T., Ing, C., & Arrazola, J. M. (2020). Molecular docking with Gaussian Boson Sampling. Science Advances, 6(23), eaax1950. https://doi.org/10.1126/sciadv.aax1950

Ganguly, S., Morapakula, S. N., & Coronado, L. M. P. (2022). Quantum Natural Language Processing Based Sentiment Analysis Using Lambeq Toolkit. 2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T), 1–6. https://doi.org/10.1109/ICPC2T53885.2022.9776836

Joshi, M., Karthikeyan, S., & Mishra, M. K. (2023). Recent Trends and Open Challenges in Blind Quantum Computation. In I. Woungang, S. K. Dhurandher, K. K. Pattanaik, A. Verma, & P. Verma (Eds.), Advanced Network Technologies and Intelligent Computing (Vol. 1798, pp. 485–496). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-28183-9_34

Luo, W., Cao, L., Shi, Y., Wan, L., Zhang, H., Li, S., Chen, G., Li, Y., Li, S., Wang, Y., Sun, S., Karim, M. F., Cai, H., Kwek, L. C., & Liu, A. Q. (2023). Recent progress in quantum photonic chips for quantum communication and internet. Light: Science & Applications, 12(1), 175. https://doi.org/10.1038/s41377-023-01173-8

Mostafa, A. M., Aljasir, M., Alruily, M., Alsayat, A., & Ezz, M. (2023). Innovative Forward Fusion Feature Selection Algorithm for Sentiment Analysis Using Supervised Classification. Applied Sciences, 13(4), 2074. https://doi.org/10.3390/app13042074

Omar, A., & Abd El-Hafeez, T. (2023). Quantum computing and machine learning for Arabic language sentiment classification in social media. Scientific Reports, 13(1), 17305. https://doi.org/10.1038/s41598-023-44113-7

Ur Rasool, R., Ahmad, H. F., Rafique, W., Qayyum, A., Qadir, J., & Anwar, Z. (2023). Quantum Computing for Healthcare: A Review. Future Internet, 15(3), 94. https://doi.org/10.3390/fi15030094

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Published

16.03.2024

How to Cite

Smita Bhagwat, Pallavi Rege, Nitin Sakhare, . S. B. R. R. A. M. A. C. . (2024). Quantum Fusion: Enhancing Predictive Power through Entangled Machine Learning Ensembles. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 956–967. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5376

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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