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Record W4404280756 · doi:10.1103/physrevb.110.174304

Open quantum systems with noncommuting coupling operators: An analytic approach

2024· article· en· W4404280756 on OpenAlexafffund
Jakub Garwoła, Dvira Segal

Bibliographic record

VenuePhysical review. B./Physical review. B · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCold Atom Physics and Bose-Einstein Condensates
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsCoupling (piping)QuantumComputer scienceAlgebra over a fieldTheoretical physicsPhysicsEpistemologyQuantum mechanicsPure mathematicsMathematicsPhilosophyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

We present an analytic approach to treat open quantum systems strongly coupled to multiple environments via noncommuting system operators: a prime example is a qubit concurrently coupled to both decohering and dissipative baths. Our approach, which accommodates strong system-bath couplings, generalizes the recently developed reaction-coordinate polaron transform method [N. Anto-Sztrikacs et al., PRX Quantum 4, 020307 (2023)] to handle couplings to baths via noncommuting system operators. Our approach creates an effective Hamiltonian that reveals the cooperative effect of the baths on the system. For a spin impurity coupled to both dissipative and decohering environments, the effective Hamiltonian predicts the suppression of relaxation by decoherence, a phenomenon previously observed in simulations but lacking so far a theoretical foundation. We also apply the method to an ensemble of spins coupled to local baths through noncommuting operators, demonstrating the engineering of the Kitaev XY spin chain interaction. Noncommutativity is a feature of quantum systems; future prospects of our approach include the study of thermal machines that leverage such genuine quantum effects.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.386
Teacher spread0.355 · 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 teacher head, not a consensus.

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

Citations11
Published2024
Admission routes2
Has abstractyes

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