Equilibrium and nonequilibrium steady states with the repeated interaction protocol: relaxation dynamics and energetic cost
Bibliographic record
Abstract
Abstract We study the dynamics of a qubit system interacting with thermalized bath-ancilla spins via a repeated interaction scheme. Considering generic initial conditions for the system and employing a Heisenberg-type interaction between the system and the ancillas, we analytically prove the following: (i) the populations and coherences of the qubit system evolve independently toward a nonequilibrium steady-state solution, which is diagonal in the qubit’s energy eigenbasis. The population relaxes to this state geometrically, whereas the coherences decay through a more compound behavior. (ii) In the long time limit, the system approaches a steady state that generally differs from the thermal state of the ancilla. We derive this steady-state solution and show its dependence on the interaction parameters and collision frequency. (iii) We bound the number of interaction steps required to achieve the steady state within a specified error tolerance, and we evaluate the energetic cost associated with the process. Our key finding is that deterministic system-ancilla interactions do not typically result in the system thermalizing to the thermal state of the ancilla. Instead, they generate a distinct nonequilibrium steady state, which we explicitly derive. However, we also identify an operational regime that leads to thermalization with a few long and possibly randomized collisions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".