Medida de complexidade estatística quântica

Abstract

Statistical complexity measures are distinctive tools that can detect significant physical phenomena. Many of these phenomena occur in such a way that they leave signatures that can be interpreted as changes in the state of the system, which can be read as statistical complexity through these measures, measured on a scale of order and disorder. We investigate the classical statistical complexity measure (CSCM) defined by López-Ruiz et al. and apply it to a simple model of discrete time evolution, characterized by finite Markov chains in dimension 2, for stochastic and bistochastic evolutions. Additionally, we introduce a quantum version for measuring statistical complexity: the quantum statistical complexity measure (QSCM), in the context of quantum information theory, and use it as a signaling function for quantum correlation transitions, measured on a scale of order and disorder. We consider the possibility that such transitions characterize intriguing physical phenomena, such as quantum phase transitions or sudden variations in correlation distributions. Our measure is applied to two analytically solvable Hamiltonian models: the one-dimensional quantum Ising model in the reduced single-particle state, and the Heisenberg chain XXZ of spin-1/2 in the state reduced by two particles. We analyze the measurement behavior in quantum phase transitions for finite system sizes and in the thermodynamic limit using the Bethe ansatz technique. We conclude that the quantum statistical complexity measure can serve as a signaling function of correlation transitions and distinguish quantum phase transitions, due to the abruptness that physical quantities experience at these transition points and the way in which the reduced states of these critical systems change within a scale of order and disorder.