当前位置: X-MOL 学术Phys. Rev. Lett. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Reinforcement Learning Approach to Nonequilibrium Quantum Thermodynamics
Physical Review Letters ( IF 8.6 ) Pub Date : 2021-01-13 , DOI: 10.1103/physrevlett.126.020601
Pierpaolo Sgroi , G. Massimo Palma , Mauro Paternostro

We use a reinforcement learning approach to reduce entropy production in a closed quantum system brought out of equilibrium. Our strategy makes use of an external control Hamiltonian and a policy gradient technique. Our approach bears no dependence on the quantitative tool chosen to characterize the degree of thermodynamic irreversibility induced by the dynamical process being considered, requires little knowledge of the dynamics itself, and does not need the tracking of the quantum state of the system during the evolution, thus embodying an experimentally nondemanding approach to the control of nonequilibrium quantum thermodynamics. We successfully apply our methods to the case of single- and two-particle systems subjected to time-dependent driving potentials.

中文翻译:

非平衡量子热力学的强化学习方法

我们使用强化学习方法来减少在不平衡状态下产生的封闭量子系统中的熵产生。我们的策略利用了外部控制哈密顿量和策略梯度技术。我们的方法不依赖于选择的定量工具来表征所考虑的动力学过程引起的热力学不可逆性的程度,对动力学本身了解甚少,并且在演化过程中不需要跟踪系统的量子状态,因此,在控制非平衡量子热力学方面体现了一种实验上非要求性的方法。我们成功地将我们的方法应用于受到时间依赖的驱动电势的单粒子系统和两粒子系统的情况。
更新日期:2021-01-13
down
wechat
bug