Unlocking the Potential of Quantum Machine Learning: A Paradigm Shift in Optimization


  • Saurabh Choudhary, Rajanish Kumar Jain, Sachin Malhotra, Nitin Rastogi, Midhunchakkaravarthy


Quantum Machine Learning; Optimization; Quantum Computing; Quantum Optimization;


Quantum Machine Learning (QML) is an exciting new field that combines quantum computing and machine learning, revolutionizing the way we develop systems. This article explores the significant role QML plays in traditional communication and quantum optimization methods. We delve into the fundamentals of quantum computing, compare classical methods with quantum optimization, and examine QML algorithms to illustrate their applications across different industries.

1 Krishna Engineering College, Ghaziabad, India; email – saurabh.chy75@gmail.com

3 Krishna Engineering College, Ghaziabad, India; email – rajneesh8m@gmail.com

2 Krishna Engineering College, Ghaziabad, India; email – sachin.malhotra2312@gmail.c

4 Athenaeum Jupiter Pvt Ltd, Gurugram, India; email – nitin@athenaeducation.co.in

5 Lincoln University College, Malaysia; email – midhun.research@gmail.com

*Correspondence: Sachin Malhotra

HoD, Department of Computer Science & Engineering Krishna Engineering College, Mohan Nagar,Ghaziabad, India

Zip Code - 201007

Email: sachin.malhtra2312@gmail.com

Phone: +91 9911217804


With a solid understanding of quantum mechanics and machine learning concepts, our research breaks down optimization techniques, highlighting their advantages and disadvantages compared to quantum methods. We introduce QML algorithms, such as quantum neural networks and quantum approximate optimization algorithms, and provide explanations of their workings.

Moving beyond theory, we demonstrate how QML can effectively address real-world optimization problems in finance, transportation, healthcare, and other domains. Our examples showcase how QML can enhance performance, reduce costs, and foster innovation. Despite its potential, the integration of QML into daily business faces challenges. We explore issues such as hardware limitations, error correction, scalability, and noise reduction. Additionally, we present potential solutions and suggest future research directions to overcome these challenges.

In summary, our research underscores that QML, as a fusion of classical and quantum optimization, is poised to transform business practices and drive innovation. As quantum hardware advances and our understanding of quantum algorithms deepens, the game-changing capabilities of QML will revolutionize our approach to complex development problems, propelling progress and innovation across various industries.


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How to Cite

Saurabh Choudhary,. (2024). Unlocking the Potential of Quantum Machine Learning: A Paradigm Shift in Optimization. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1876–1896. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5758



Research Article