Abstract
AbstractWe generalize Lévy’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 $$\rho $$
ρ
on a separable Hilbert space $${\mathcal {H}}$$
H
, $${\textrm{GAP}}(\rho )$$
GAP
(
ρ
)
is the most spread-out probability measure on the unit sphere of $${\mathcal {H}}$$
H
that has density matrix $$\rho $$
ρ
and thus forms the natural generalization of the uniform distribution. We prove concentration-of-measure whenever the largest eigenvalue $$\Vert \rho \Vert $$
‖
ρ
‖
of $$\rho $$
ρ
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 $$\psi $$
ψ
of a given ensemble, the reduced density matrix of a sufficiently small subsystem is very close to a $$\psi $$
ψ
-independent matrix. Dynamical typicality is the statement that for any observable and any unitary time evolution, for “most” pure states $$\psi $$
ψ
from a given ensemble the (coarse-grained) Born distribution of that observable in the time-evolved state $$\psi _t$$
ψ
t
is very close to a $$\psi $$
ψ
-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 $${\textrm{GAP}}(\rho )$$
GAP
(
ρ
)
, provided the density matrix $$\rho $$
ρ
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.
Funder
Studienstiftung des Deutschen Volkes
Deutsche Forschungsgemeinschaft
Eberhard Karls Universität Tübingen
Publisher
Springer Science and Business Media LLC