Optimal qubit-mediated quantum heat transfer via noncommuting operators and strong coupling effects
Bibliographic record
Abstract
Heat transfer in quantum systems is a current topic of interest due to emerging quantum technologies that attempt to miniaturize engines and examine fundamental aspects of thermodynamics. In this work, we consider heat transfer between two thermal reservoirs in which a central spin degree of freedom mediates the process. Our objective is to identify the system-bath coupling operators that maximize heat transfer at arbitrary system-bath coupling strengths. By employing a Markovian embedding method in the form of the reaction-coordinate mapping, we study numerically heat transfer at arbitrary system-bath coupling energy and for general system-bath coupling operators between the baths and the central qubit system. We find a stark contrast in the conditions required for optimal heat transfer depending on whether the system is weakly or strongly coupled to the heat baths. In the weak-coupling regime, optimal heat transfer requires identical coupling operators that facilitate maximum sequential transport, resonant with the central qubit. In contrast, in the strong-coupling regime, noncommuting system-bath coupling operators between the hot and cold reservoirs are necessary to achieve optimal heat transfer. We further employ the Effective Hamiltonian theory and gain partial analytical insights into the observed phenomena. We discuss the limitations of this approximate method in capturing the behavior of the heat current for noncommuting coupling operators, calling for its future extensions to capture transport properties in systems with general interaction Hamiltonians.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| 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".