"Quantum Machine Learning Algorithms for Complex Optimization Problems"

Authors

  • Vaishali Rajput, Rupesh Mahajan, Umakant Butkar, Satyajit Ramesh Potdar, Sweety G. Jachak, Devidas S. Thosar

Keywords:

Quantum Machine Learning (QML), Complex Optimization Problems, Quantum Annealing, Variational Quantum Eigensolvers, Quantum Neural Networks, Hybrid Quantum-Classical Algorithms

Abstract

In recent years, the intersection of quantum computing and machine learning has emerged as a promising frontier for addressing complex optimization problems that are computationally intractable for classical approaches. This paper presents a comprehensive review and analysis of quantum machine learning (QML) algorithms tailored for complex optimization tasks. We explore the theoretical foundations of quantum-enhanced algorithms, including quantum annealing, variational quantum eigensolvers, and quantum neural networks, highlighting their potential advantages over classical methods in terms of convergence speed and solution accuracy. The paper further investigates practical implementations and hybrid quantum-classical strategies that leverage quantum resources to tackle large-scale optimization problems in diverse fields such as combinatorial optimization, financial modeling, and structural design. We also discuss current challenges and limitations, including hardware constraints and algorithmic scalability, and propose future research directions to bridge the gap between theoretical potential and practical application. Our findings suggest that while QML holds substantial promise, significant advancements are required to fully realize its capabilities in solving complex optimization problems.

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Published

12.06.2024

How to Cite

Vaishali Rajput. (2024). "Quantum Machine Learning Algorithms for Complex Optimization Problems". International Journal of Intelligent Systems and Applications in Engineering, 12(4), 2435 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6663

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

License

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