Quantum Annealing for Electromagnetic Engineers—Part I: A computational method to solve various types of optimization problems.
It is well known that electromagnetic computations are computationally demanding. Interestingly, many such problems can be recast to be solved by quantum annealing. Quantum annealing, a kind of quantum computer, utilizes quantum tunneling for state transitions, which enables one to find the global minimum in a complex energy landscape. Part I of this article explains quantum annealing for the classical electromagnetic community, assuming little knowledge of quantum theory. It reviews the basic principle and recent advances in quantum annealing to extend its applications, such as a hybrid quantum-classical annealing algorithm. Part II presents various examples of electromagnetic problems that can be solved by quantum annealing. These are 1) optimization of a reconfigurable directional metasurface, 2) finding current distribution in an arbitrary wire antenna, 3) finding charge and field distributions in a static condition, and 4) optimization of source excitation to focus fields in hyperthermia. Lastly, the performance of the quantum annealer is compared with classical solvers to deduce the type of applications in which a quantum annealer of current technologies can be preferred in practice.
Authors
- Eungkyu Lee (ORCID: https://orcid.org/0000-0002-0211-0727)
- Qi Jian Lim (ORCID: https://orcid.org/0000-0002-2802-6718)
- Sangbin Lee (ORCID: https://orcid.org/0009-0006-3982-1908)
- Sanghoek Kim (ORCID: https://orcid.org/0000-0002-3268-7287)
- Zhen Peng (ORCID: https://orcid.org/0000-0001-9907-017X)
- Young‐Min Kim (ORCID: https://orcid.org/0000-0003-3573-8753)
- Soyul Han (ORCID: https://orcid.org/0009-0000-2729-9733)
- Charles D. Ross
Institutions
- University of Illinois Urbana-Champaign (US)
- Kyung Hee University (KR)
- Korea Electronics Technology Institute (KR)
Publication Details
- Journal
- IEEE Antennas and Propagation Magazine
- Published
- 2024-12-13
- DOI
- https://doi.org/10.1109/map.2024.3498695
- Citations
- 6
- Primary Topic
- Experimental Learning in Engineering
- Type
- article
- Field-Weighted Citation Impact
- 1.92