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Record W2743571097 · doi:10.1103/physreve.96.062120

Quantum heat engine with coupled superconducting resonators

2017· article· en· W2743571097 on OpenAlexafffund
Ali Ü. C. Hardal, Nur Aslan, C. M. Wilson

Bibliographic record

VenuePhysical review. E · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Waterloo
FundersUniversity of WaterlooVillum Fonden
KeywordsPhysicsResonatorCoherence (philosophical gambling strategy)QuantumHeat engineExcited stateQuantum mechanicsQuantum fluctuationCircuit quantum electrodynamicsQuantum electrodynamicsQuantum computerOptoelectronicsThermodynamics

Abstract

fetched live from OpenAlex

We propose a quantum heat engine composed of two superconducting transmission line resonators interacting with each other via an optomechanical-like coupling. One resonator is periodically excited by a thermal pump. The incoherently driven resonator induces coherent oscillations in the other one due to the coupling. A limit cycle, indicating finite power output, emerges in the thermodynamical phase space. The system implements an all-electrical analog of a photonic piston. Instead of mechanical motion, the power output is obtained as a coherent electrical charging in our case. We explore the differences between the quantum and classical descriptions of our system by solving the quantum master equation and classical Langevin equations. Specifically, we calculate the mean number of excitations, second-order coherence, as well as the entropy, temperature, power, and mean energy to reveal the signatures of quantum behavior in the statistical and thermodynamic properties of the system. We find evidence of a quantum enhancement in the power output of the engine at low temperatures.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.323
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations64
Published2017
Admission routes2
Has abstractyes

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