Shared Information in Stationary States at Criticality
arXiv:0912.2963 · doi:10.1103/PhysRevE.80.030102
Abstract
We consider bipartitions of one-dimensional extended systems whose probability distribution functions describe stationary states of stochastic models. We define estimators of the shared information between the two subsystems. If the correlation length is finite, the estimators stay finite for large system sizes. If the correlation length diverges, so do the estimators. The definition of the estimators is inspired by information theory. We look at several models and compare the behavior of the estimators in the finite-size scaling limit. Analytical and numerical methods as well as Monte Carlo simulations are used. We show how the finite-size scaling functions change for various phase transitions, including the case where one has conformal invariance.
25 pages, 12 figures
References in corpus (12)
- Estimating Mutual Information
- Entanglement Entropy and Quantum Field Theory
- Nonequilibrium Steady States of Matrix Product Form: A Solver's Guide
- Entanglement entropy of random quantum critical points in one dimension
- A no-go theorem for a two-dimensional self-correcting quantum memory based on stabilizer codes
- Ground state projection of quantum spin systems in the valence bond basis
- Valence Bond Entanglement Entropy
- The raise and peel model of a fluctuating interface
- Exact valence bond entanglement entropy and probability distribution in the XXX spin chain and the Potts model
- Hardcore dimer aspects of the SU(2) Singlet wavefunction
- Conformal invariance and its breaking in a stochastic model of a fluctuating interface
- Different facets of the raise and peel model
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