Quantum Annealing for Electromagnetic Engineers—Part I: A computational method to solve various types of optimization problems.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".