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Universal Work Statistics in Long-Range Interacting Quantum Systems
Andrea Solfanelli1,2, Nicolò Defenu3
1SISSA, via Bonomea 265, 34136 Trieste, Italy.
Physical Review Letters
|February 10, 2025
Summary
Long-range interactions minimize energy losses in quantum thermal devices by reducing defect generation during nonadiabatic evolution, improving efficiency. This study explores their robustness and potential for optimizing quantum thermal cycles.
Area of Science:
- Quantum thermodynamics
- Condensed matter physics
- Quantum information science
Background:
- Quantum thermal devices require efficient energy transfer and minimal losses for optimal performance.
- Nonadiabatic evolution and defect generation are key challenges in quantum thermodynamics.
- The role of long-range interactions in quantum systems is an active area of research.
Purpose of the Study:
- To determine conditions where long-range interactions reduce energy losses during nonadiabatic evolution.
- To investigate the robustness of long-range systems against dynamic excitation compared to local systems.
- To explore the potential of long-range interactions for optimizing quantum thermal devices and cycles.
Main Methods:
- Investigated the response of systems with long-range interactions to diverse external drivings.
- Analyzed quantum work statistics to understand energy transfer efficiency and dynamical quantum criticality.
- Utilized the effective dimension approach for general applicability of findings.
Main Results:
- Long-range interactions were shown to reduce energy losses due to defect generation.
- Systems with long-range interactions exhibit enhanced robustness against dynamic excitation.
- Quantum work statistics provide insights into improved energy transfer efficiency.
Conclusions:
- Including long-range interacting media offers significant benefits for quantum thermodynamics applications.
- Optimized finite-time quantum thermal cycles are achievable with long-range interactions.
- Findings are general and applicable to various experimentally relevant scenarios.
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