Exploring the Optimal Cycle for Quantum Heat Engine using Reinforcement Learning
arXiv:2308.06794 · doi:10.1103/PhysRevA.109.022246
Abstract
Quantum thermodynamic relationships in emerging nanodevices are significant but often complex to deal with. The application of machine learning in quantum thermodynamics has provided a new perspective. This study employs reinforcement learning to output the optimal cycle of quantum heat engine. Specifically, the soft actor-critic algorithm is adopted to optimize the cycle of three-level coherent quantum heat engine with the aim of maximal average power. The results show that the optimal average output power of the coherent three-level heat engine is 1.28 times greater than the original cycle (steady limit). Meanwhile, the efficiency of the optimal cycle is greater than the Curzon-Ahlborn efficiency as well as reporting by other researchers. Notably, this optimal cycle can be fitted as an Otto-like cycle by applying the Boltzmann function during the compression and expansion processes, which illustrates the effectiveness of the method.
References in corpus (67)
- Quantum Thermodynamics
- Quantum Thermodynamic Cycles and quantum heat engines
- A single-atom heat engine
- Efficiency at maximum power of low dissipation Carnot engines
- Experimental realization of a Szilard engine with a single electron
- Experimental demonstration of quantum effects in the operation of microscopic heat engines
- Quantum Equivalence and Quantum Signatures in Heat Engines
- Experimental characterization of a spin quantum heat engine
- The quantum harmonic Otto cycle
- Entanglement Generation is Not Necessary for Optimal Work Extraction
- On-chip Maxwell's demon as an information-powered refrigerator
- Reinforcement Learning in Different Phases of Quantum Control
- Experimental study of mutual information in a Maxwell Demon
- The power of a critical heat engine
- Extractable Work from Correlations
- Quantum absorption refrigerator with trapped ions
- Quantum Supremacy of Many-Particle Thermal Machines
- Experimental rectification of entropy production by a Maxwell's Demon in a quantum system
- An endoreversible quantum heat engine driven by atomic collisions
- Coherence effects in the performance of the quantum Otto heat engine
- Slow dynamics and thermodynamics of open quantum systems
- Efficiency of a quantum Otto heat engine operating under a reservoir at effective negative temperatures
- Otto refrigerator based on a superconducting qubit: classical and quantum performance
- Carnot Cycle at Finite Power: Attainability of Maximal Efficiency
- A Brief History of the GKLS Equation
- Boosting work characteristics and overall heat engine performance via shortcuts to adiabaticity: quantum and classical systems
- Optimal cycles for low-dissipation heat engines
- When does reinforcement learning stand out in quantum control? A comparative study on state preparation
- Optimal work extraction and thermodynamics of quantum measurements and correlations
- Deep Reinforcement Learning for Quantum Gate Control
- A two-qubit engine fueled by entangling operations and local measurements
- Universal Coherence-Induced Power Losses of Quantum Heat Engines in Linear Response
- Identifying optimal cycles in quantum thermal machines with reinforcement-learning
- Efficiency of harmonic quantum Otto engines at maximal power
- Analog of a quantum heat engine using a single-spin qubit
- Quantum Performance of Thermal Machines over Many Cycles
- Superadiabatic quantum friction suppression in finite-time thermodynamics
- Spin quantum heat engines with shortcuts to adiabaticity
- Efficiency at Maximum Power of Laser Quantum Heat Engine Enhanced by Noise-Induced Coherence
- Global optimization of quantum dynamics with AlphaZero deep exploration
- Quantum Signatures in the Quantum Carnot Cycle
- Single atom energy-conversion device with a quantum load
- Universal constraint for efficiency and power of a low-dissipation heat engine
- Thermodynamics of Gambling Demons
- Shortcut-to-adiabaticity Otto engine: A twist to finite-time thermodynamics
- Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation
- Spin Quantum Heat Engine Quantified by Quantum Steering
- Non-Markov Enhancement of Maximum Power for Quantum Thermal Machines
- Maximum power and corresponding efficiency for two-level heat engines and refrigerators: optimality of fast cycles
- A Reinforcement Learning approach for Quantum State Engineering
- Swift heat transfer by fast-forward driving in open quantum systems
- Reinforcement learning approach to non-equilibrium quantum thermodynamics
- A quantum enhanced finite-time Otto cycle
- Optimal thermodynamic control in open quantum systems
- Optimal control of quantum thermal machines using machine learning
- Speeding-up a quantum refrigerator via counter-diabatic driving
- Maxwell's lesser demon: a quantum engine driven by pointer measurements
- Boosting the performance of the Quantum Otto heat engines
- Achieve Higher Efficiency at Maximum Power with Finite-time Quantum Otto Cycle
- Supremacy of incoherent sudden cycles
- Speeding up Thermalisation via Open Quantum System Variational Optimisation
- Maximum power heat engines and refrigerators in the fast-driving regime
- Two-Stroke Optimization Scheme for Mesoscopic Refrigerators
- Machine classification for probe based quantum thermometry
- Model-free optimization of power/efficiency tradeoffs in quantum thermal machines using reinforcement learning
- Thermodynamics of a continuous quantum heat engine: Interplay between population and coherence
- Continuous Three-level Quantum Heat Engine with High Performance Under Medium Temperature Difference
Cited by in corpus (4)
- Innovative Designs and Insights into Quantum Thermal Machines
- Minimum time connection between non-equilibrium steady states: the Brownian gyrator
- Artificially intelligent Maxwell's demon for optimal control of open quantum systems
- Stochastic Thermodynamics at the Quantum-Classical Boundary: A Self-Consistent Framework Based on Adiabatic-Response Theory