Optimization of Markov process violates detailed balance condition
arXiv:1509.08212 · doi:10.1103/PhysRevE.93.012129
Abstract
We consider the optimization of Markovian dynamics to pursue the fastest convergence to the stationary state. The brachistochrone method is applied to the continuous-time master equation for finite-size systems. The principle of least action leads to a brachistochrone equation for the transition-rate matrix. Three-state systems are explicitly analyzed, and we find that the solution violates the detailed balance condition. The properties of the solution are studied in detail to observe the optimality of the solution. We also discuss the counterdiabatic driving for the Markovian dynamics. The transition-rate matrix is then divided into two parts, and the state is given by an eigenstate of the first part. The second part violates the detailed balance condition and plays the role of a counterdiabatic term.
14 pages, 13 figures, published as "Conflict between fastest relaxation of a Markov process and detailed balance condition"
References in corpus (7)
- Shortcuts to adiabaticity
- Quantum Computation as Geometry
- Markov Chain Monte Carlo Method without Detailed Balance
- Violation of detailed balance accelerates relaxation
- Dynamics of the One-Dimensional Ising Model without Detailed Balance Condition
- How fast and robust is the quantum adiabatic passage?
- Mathematical understanding of detailed balance condition violation and its application to Langevin dynamics
Cited by in corpus (11)
- Shortcuts to adiabaticity: concepts, methods, and applications
- Time-information uncertainty relations in thermodynamics
- Nonadiabatic Control of Geometric Pumping
- Eigenvalue analysis of an irreversible random walk with skew detailed balance conditions
- Hamiltonian engineering for adiabatic quantum computation: Lessons from shortcuts to adiabaticity
- Efficient Irreversible Monte Carlo samplers
- Thermodynamic bounds on spectral perturbations, with applications to oscillations and relaxation dynamics
- Stochastic gradient method with accelerated stochastic dynamics
- Transfer Entropy and Flow of Information in Two-Skyrmion System
- Fast Markov Chain Monte Carlo Algorithms via Lie Groups
- Sampling and statistical physics via symmetry