Impact of network architecture on the exciton dynamics in an open quantum battery
Bibliographic record
Abstract
Understanding the interplay between network architecture and exciton dynamics is crucial for optimizing the performance of open quantum systems, particularly in excitonic quantum batteries (QBs). This study explores how variations in network topology influence exciton storage and transport in QBs modeled as open quantum networks with exchange symmetries embedded in their structural design. We simulate exciton dynamics in systems with different architectures-including single-ring and stacked ring configurations of varying sizes-initialized in one of their symmetry-protected dark states. For single-ring systems, our findings reveal how different initial dark states influence exciton transfer during the discharge phase, how ring size affects the discharge rate, and how noise impacts storage efficiency as a function of ring size. For stacked ring systems, we demonstrate how the efficiency of exciton transfer to the sink depends on the inter-ring coupling strength. Overall, these results offer detailed insights into how architectural modifications can be leveraged to enhance the performance of excitonic QBs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".