Quick Navigation
Topics
Quantum Thermodynamics
Quantum Chemistry
Approaching Resource-Theoretic Optimal Performance with Structured Environments
arXiv
Authors: Lea Lautenbacher, Giovanni Spaventa, Susana F. Huelga, Martin B. Plenio
Year
2026
Paper ID
76185
Status
Preprint
Abstract Read
~2 min
Abstract Words
146
Citations
N/A
Abstract
Resource-theoretic approaches to thermodynamics provide powerful, model-independent bounds on the efficiency of physical processes, because they do not rely on microscopic details of the environment. Whether such bounds can be approached by realistic dynamics generated by explicit system-environment interactions remains an open question. Photoisomerization, a fundamental molecular photoreaction, offers a concrete setting to examine this issue. We introduce a tunable microscopic model of a molecular photoswitch coupled to a structured vibrational environment, which interpolates continuously between Markovian and non-Markovian regimes. Resource-theoretic analysis predicts in particular that Markovian Thermal Operations achieve strictly lower yields than general Thermal Operations. We show that environmental memory lifts dynamical restrictions associated with Markovian thermal evolutions, thereby enlarging the set of transformations accessible to the microscopic dynamics. Approaching the thermal operation bound, however, depends on the microscopic coupling structure that generates this memory and directs the resulting dynamics towards the target transformation.
Why This Paper Matters
- This paper contributes to the Quantum Thermodynamics research area in the Quantum Articles archive.
- It adds a 2026 reference point for readers tracking recent quantum research.
- Resource-theoretic approaches to thermodynamics provide powerful, model-independent bounds on the efficiency of physical processes, because they do not rely on microscopic...
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.