A Quantum Machine Learning Approach for Bridging the Gap Between Quantum and Classical Computing.

Authors

  • Surjeet Associate professor, Affiliation- Bharti Vidyapeeth’s College Of Engineering, New Delhi
  • Chetan Bulla Associate Technology, Business intelligence, Synechron Pvt Ltd.
  • Amit Arya Assistant Professor, Madanapalle Institute of Technology & Science
  • Sumaira Idrees Department of Health Informatics, College of Public Health and Tropical Medicine, Jazan University, Jazan, 45142, Kingdom of Saudi Arabia.
  • Gurwinder Singh Associate Professor, Department of AIT-CSE, Chandigarh University, Punjab, India.
  • S. Govind Rao Professor in CSE, Gokaraju Rangaraju Institute of Engineerin and Technology (Griet), Bachupally, Hyderbad- 500090
  • Abhinandan Shirahatti Associate Professor, Department of Computer Science and Engineering , KIT College of Engineering (Autonomous ), Kolhapur, Maharashtra.

Keywords:

Quantum-Classical Computing, Computational Boundaries, Integration Challenges, Symbiotic Evolution

Abstract

The relentless march of technological progress has ushered in an era where the computational boundaries between quantum and classical systems have become increasingly distinct. This research paper endeavors to traverse this schism, proposing innovative approaches aimed at bridging the gap between quantum and classical computing paradigms. The burgeoning capabilities of quantum computing hold promise for solving complex problems that elude classical systems; however, practical implementation faces formidable challenges. Through an in-depth exploration of the current landscape, this research identifies key gaps and issues impeding the seamless integration of quantum and classical computing. Our methodology involves a comprehensive review of existing approaches, coupled with theoretical modeling and empirical investigations where applicable. The results unveil novel insights into the convergence of quantum and classical computing, offering a nuanced understanding of the intricate interplay between these computational realms. The discussion interprets these findings, examining their implications for advancing technology and overcoming limitations in both quantum and classical computing. While acknowledging the inherent limitations of our study, we propose avenues for future research, envisioning a harmonious coalescence of quantum and classical computing that unlocks unprecedented computational power. This research contributes to the ongoing dialogue on the frontiers of computing, presenting a compelling case for the symbiotic evolution of quantum and classical computational methodologies.

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References

Malik, A. J., & Verma, C. S. (2023). On quantum computing and geometry optimization. bioRxiv, 2023-03.

Innes, M., Edelman, A., Fischer, K., Rackauckas, C., Saba, E., Shah, V. B., & Tebbutt, W. (2019). A differentiable programming system to bridge machine learning and scientific computing. arXiv preprint arXiv:1907.07587.

Broughton, M., Verdon, G., McCourt, T., Martinez, A. J., Yoo, J. H., Isakov, S. V., ... & Mohseni, M. (2020). Tensorflow quantum: A software framework for quantum machine learning. arXiv preprint arXiv:2003.02989.

Baum, Y., Amico, M., Howell, S., Hush, M., Liuzzi, M., Mundada, P., ... & Biercuk, M. J. (2021). Experimental deep reinforcement learning for error-robust gate-set design on a superconducting quantum computer. PRX Quantum, 2(4), 040324.

Sergi, C. M. (2019). Digital pathology: the time is now to bridge the gap between medicine and technological singularity. Interactive Multimedia-Multimedia Production and Digital Storytelling.

Dunjko, V., Taylor, J. M., & Briegel, H. J. (2017, October). Advances in quantum reinforcement learning. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 282-287). IEEE.

Dunjko, V., Taylor, J. M., & Briegel, H. J. (2017, October). Advances in quantum reinforcement learning. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 282-287). IEEE.

Nam, Y., Chen, J. S., Pisenti, N. C., Wright, K., Delaney, C., Maslov, D., ... & Kim, J. (2020). Ground-state energy estimation of the water molecule on a trapped-ion quantum computer. npj Quantum Information, 6(1), 33.

Kwak, Y., Yun, W. J., Kim, J. P., Cho, H., Park, J., Choi, M., ... & Kim, J. (2023). Quantum distributed deep learning architectures: Models, discussions, and applications. ICT Express, 9(3), 486-491.

Batra, K., Zorn, K. M., Foil, D. H., Minerali, E., Gawriljuk, V. O., Lane, T. R., & Ekins, S. (2021). Quantum machine learning algorithms for drug discovery applications. Journal of chemical information and modeling, 61(6), 2641-2647.

Glover, F., Kochenberger, G., & Du, Y. (2019). Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models. 4or, 17, 335-371.

Riandari, F., Alesha, A., & Sihotang, H. T. (2021). Quantum computing for production planning. International Journal of Enterprise Modelling, 15(3), 163-175.

Schuld, M., Sinayskiy, I., & Petruccione, F. (2014). Quantum computing for pattern classification. In PRICAI 2014: Trends in Artificial Intelligence: 13th Pacific Rim International Conference on Artificial Intelligence, Gold Coast, QLD, Australia, December 1-5, 2014. Proceedings 13 (pp. 208-220). Springer International Publishing.

Martın-Guerrero, J. D., & Lamata, L. (2020). Quantum machine learning. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

Andersson, M. P., Jones, M. N., Mikkelsen, K. V., You, F., & Mansouri, S. S. (2022). Quantum computing for chemical and biomolecular product design. Current Opinion in Chemical Engineering, 36, 100754.

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Published

12.01.2024

How to Cite

Surjeet, S., Bulla, C. ., Arya, A. ., Idrees, S. ., Singh, G. ., Rao, S. G. ., & Shirahatti, A. . (2024). A Quantum Machine Learning Approach for Bridging the Gap Between Quantum and Classical Computing. International Journal of Intelligent Systems and Applications in Engineering, 12(12s), 553–560. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4539

Issue

Vol. 12 No. 12s (2024)

Section

Research Article

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