Machine Learning for Quantum Computing Bridging the Gap between AI and Quantum Algorithms

Authors

  • B. J. Dange, Kaustubh Manikrao Gaikwad, H. E. Khodke, Santosh Gore, S. N. Gunjal, Kalyani Kadam, Sayali Karmode

Keywords:

Machine Learning, Quantum Computing, Artificial Intelligence, Quantum Algorithms, Hybrid Approaches.

Abstract

This study explains how machine learning techniques are applied to enhance quantum algorithms and examines the interplay between machine learning and quantum computing. It explores quantum data analysis, quantum machine learning, and hybrid quantum-classical techniques, emphasizing their contributions to bridging the gap between artificial intelligence and quantum algorithms. Additionally, it analyzes how quantum data production, quantum-assisted optimization, and quantum neural networks could influence the direction of AI-quantum integration in the future.

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Published

26.03.2024

How to Cite

B. J. Dange, Kaustubh Manikrao Gaikwad, H. E. Khodke, Santosh Gore, S. N. Gunjal, Kalyani Kadam, Sayali Karmode. (2024). Machine Learning for Quantum Computing Bridging the Gap between AI and Quantum Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 600–605. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5456

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

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