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Related Concept Videos

The Carnot Cycle01:30

The Carnot Cycle

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Converting work to heat is an irreversible process, and the purpose of a heat engine is to reverse the effect partially. Heat engines aim to increase the efficiency of the reversal, that is, maximize the work retrieved from heat. If the efficiency of a heat engine were 100%, it would imply reversing the process completely without introducing any other effect. Thus, it would violate the second law of thermodynamics.
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Related Experiment Video

Updated: Jul 8, 2025

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation
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General limit to thermodynamic annealing performance.

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Summary

Annealing can find system minima, but often gets stuck in local ones. This study derives a bound on annealing performance, revealing a trade-off between system updates and energy accumulation for better global minimum discovery.

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Area of Science:

  • Statistical Mechanics
  • Computational Physics
  • Optimization Algorithms

Background:

  • Annealing is a widely used optimization technique for finding minima in complex landscapes.
  • However, annealing often converges to local minima instead of the desired global minimum, limiting its effectiveness.

Purpose of the Study:

  • To analyze the conditions under which annealing achieves approximate success within a finite time.
  • To derive a general bound on the distance between the final system state and the ground state during annealing.

Main Methods:

  • Connecting annealing to the principles of stochastic thermodynamics.
  • Deriving a bound based on system state updates and accumulated nonequilibrium energy.
  • Analyzing the trade-off relationship between these two quantities.

Main Results:

  • A general bound on the distance to the ground state is derived, dependent on protocol and landscape properties.
  • A trade-off is identified between the number of state updates and the accumulation of nonequilibrium energy.
  • Methods for analytically and physically bounding these quantities are presented.

Conclusions:

  • The derived bound offers a general framework for assessing annealing performance.
  • This approach is applicable to both simulated and physical implementations of annealing.
  • Understanding the trade-off enables optimization of annealing protocols for improved global minimum convergence.