| Issue |
E3S Web Conf.
Volume 616, 2025
2nd International Conference on Renewable Energy, Green Computing and Sustainable Development (ICREGCSD 2025)
|
|
|---|---|---|
| Article Number | 02024 | |
| Number of page(s) | 14 | |
| Section | Green Computing | |
| DOI | https://doi.org/10.1051/e3sconf/202561602024 | |
| Published online | 24 February 2025 | |
Optimization Techniques on Quantum and Classical Systems: A Comprehensive Comparative Study
1 Research Scholar, EEE, JNTU Kakinada and Assoc. Prof, EEE, V.J.I.T, Hyderabad, India
2 Asst. Professor, EEE, CVR College of Engineering, Hyderabad, India
3 Asst.Professor, EEE, GNITS, Hyderabad, India
4 Asso.Professor, EEE, CVR College of Engineering, Hyderabad, India
* Corresponding author: hussainshaikresearch@gmail.com
Quantum optimization is a promising field revolutionizing problem-solving across domains. This study compares Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO), and Genetic Algorithm (GA) on three platforms : a local computer, a local computer with quantum integration, and an IBM quantum machine. Results indicate PSO’s consistent performance across all setups, with the IBM quantum machine having a longer elapsed time. For MFO, the optimal solution is found using the IBM quantum machine, despite its longer execution time. Similarly, GA achieves the best results on the IBM quantum machine. These findings suggest that while quantum computers excel in solving complex problems, their execution time for simpler tasks remains higher than classical setups. Future research should address challenges like noise, limited qubits, and high material costs to improve quantum computers’ efficiency and availability.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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