Canonical Typicality For Other Ensembles Than Micro-Canonical
Abstract
We generalize L\'evy's lemma, a concentration-of-measure result for the uniform probability distribution on high-dimensional spheres, to a much more general class of measures, so-called GAP measures. For any given density matrix on a separable Hilbert space , GAP is the most spread out probability measure on the unit sphere of that has density matrix and thus forms the natural generalization of the uniform distribution. We prove concentration-of-measure whenever the largest eigenvalue of is small. We use this fact to generalize and improve well-known and important typicality results of quantum statistical mechanics to GAP measures, namely canonical typicality and dynamical typicality. Canonical typicality is the statement that for ``most'' pure states of a given ensemble, the reduced density matrix of a sufficiently small subsystem is very close to a -independent matrix. Dynamical typicality is the statement that for any observable and any unitary time-evolution, for ``most'' pure states from a given ensemble the (coarse-grained) Born distribution of that observable in the time-evolved state is very close to a -independent distribution. So far, canonical typicality and dynamical typicality were known for the uniform distribution on finite-dimensional spheres, corresponding to the micro-canonical ensemble, and for rather special mean-value ensembles. Our result shows that these typicality results hold also for GAP, provided the density matrix has small eigenvalues. Since certain GAP measures are quantum analogs of the canonical ensemble of classical mechanics, our results can also be regarded as a version of equivalence of ensembles.
Keywords
Cite
@article{arxiv.2307.15624,
title = {Canonical Typicality For Other Ensembles Than Micro-Canonical},
author = {Stefan Teufel and Roderich Tumulka and Cornelia Vogel},
journal= {arXiv preprint arXiv:2307.15624},
year = {2025}
}
Comments
44 pages LaTeX, no figures; v4 minor improvements throughout the paper