QUANTUM COMPUTING FOR OPTIMIZATION PROBLEMS: A REVIEW AND FUTURE DIRECTIONS
Keywords:
Quantum Computing, Optimization Problems, Quantum Algorithms, Quantum Approximate Optimization Algorithm, Quantum AnnealingAbstract
Quantum computing, leveraging principles of quantum mechanics, has shown potential in solving complex optimization problems more efficiently than classical approaches. This paper reviews recent advancements in quantum algorithms designed for optimization tasks and evaluates their performance against classical methods. We present a comprehensive analysis of quantum optimization algorithms such as Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing, discussing their applications, advantages, and limitations. Future directions are proposed, focusing on improving algorithm efficiency and practical implementation challenges.
References
Farhi, E., Goldstone, J., & Gutmann, S. (2014). A Quantum Approximate Optimization Algorithm. arXiv preprint arXiv:1411.4028.
Kadowaki, T., & Nishimori, H. (1998). Quantum Annealing of Ising Spin Glasses. Physical Review E, 58(5), 5355-5363.
Preskill, J. (2018). Quantum Computing in the NISQ Era and Beyond. Quantum, 2, 79. doi:10.22331/q-2018-08-06-79.
Montanaro, A. (2016). Quantum Algorithms: An Overview. npj Quantum Information, 2, 15023. doi:10.1038/npjqi.2015.23.
McClean, J. R., Romero, J., Babbush, R., & Aspuru-Guzik, A. (2016). The Theory of Variational Hybrid Quantum-Classical Algorithms. New Journal of Physics, 18(2), 023023. doi:10.1088/1367-2630/18/2/023023.
Shor, P. W. (1997). Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer. SIAM Journal on Computing, 26(5), 1484-1509. doi:10.1137/S0097539795293172.
Mohseni, M., Read, P., Neven, H., Boixo, S., Denchev, V. S., Babbush, R., ... & Martinis, J. M. (2017). Commercialize Quantum Technologies in Five Years. Nature, 543(7644), 171-174. doi:10.1038/543171a.
Lloyd, S. (1996). Universal Quantum Simulators. Science, 273(5278), 1073-1078. doi:10.1126/science.273.5278.1073.
Albash, T., & Lidar, D. A. (2018). Adiabatic Quantum Computation. Reviews of Modern Physics, 90(1), 015002. doi:10.1103/RevModPhys.90.015002.
Rieffel, E. G., & Polak, W. H. (2011). Quantum Computing: A Gentle Introduction. MIT Press.
Jiang, Z., Chen, H., & Du, S. S. (2021). Quantum Algorithms for Solving Linear Systems of Equations: An Overview. Quantum Information Processing, 20(6), 190. doi:10.1007/s11128-021-03148-x.
Benedetti, M., Garcia-Pintos, D., Perdomo, O., Leyton-Ortega, V., Nam, Y., & Perdomo-Ortiz, A. (2019). A Generative Modeling Approach for Benchmarking and Training Shallow Quantum Circuits. npj Quantum Information, 5, 45. doi:10.1038/s41534-019-0157-8.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 COMPUSOFT: An International Journal of Advanced Computer Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
©2023. COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY by COMPUSOFT PUBLICATION is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY. Permissions beyond the scope of this license may be available at Creative Commons Attribution 4.0 International Public License.
