Quantum Computing Approaches to Time-Domain Simulation of Electromagnetic Transients in Interconnected Power Systems
Bibliographic record
Abstract
The advent of quantum computing has heralded unprecedented possibilities in diverse scientific domains, including electrical engineering. This research paper delves into the innovative integration of quantum computing methodologies for the time-domain simulation of electromagnetic transients in interconnected power systems. Electromagnetic transients are pivotal phenomena that influence the stability, reliability, and efficiency of power systems, necessitating accurate and rapid simulation techniques. Classical computational paradigms, albeit powerful, encounter substantial limitations in terms of computational speed and capacity when dealing with large-scale, complex interconnected power networks. To address these challenges, this paper introduces quantum algorithms that leverage the principles of superposition and entanglement, ensuring a quantum leap in simulation capabilities. A comprehensive comparison with conventional simulation methodologies is presented, highlighting the quantum algorithms' superior efficiency and precision. The quantum circuit models for various power system components are meticulously constructed and optimized for quantum resource utilization. Furthermore, the paper explores error mitigation strategies and quantum error correction codes tailored for power system applications, ensuring robustness in the presence of quantum noise and decoherence. The empirical results, obtained from simulations on quantum processors and simulators, underscore the substantial advantages and potential of quantum computing in revolutionizing electromagnetic transient analysis. This research not only paves the way for accelerated and accurate simulations but also contributes to the enhanced stability and reliability of modern interconnected power systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".