411 research outputs found
Thermal States as Convex Combinations of Matrix Product States
We study thermal states of strongly interacting quantum spin chains and prove
that those can be represented in terms of convex combinations of matrix product
states. Apart from revealing new features of the entanglement structure of
Gibbs states our results provide a theoretical justification for the use of
White's algorithm of minimally entangled typical thermal states. Furthermore,
we shed new light on time dependent matrix product state algorithms which yield
hydrodynamical descriptions of the underlying dynamics.Comment: v3: 10 pages, 2 figures, final published versio
Causal structure of the entanglement renormalization ansatz
We show that the multiscale entanglement renormalization ansatz (MERA) can be
reformulated in terms of a causality constraint on discrete quantum dynamics.
This causal structure is that of de Sitter space with a flat spacelike
boundary, where the volume of a spacetime region corresponds to the number of
variational parameters it contains. This result clarifies the nature of the
ansatz, and suggests a generalization to quantum field theory. It also
constitutes an independent justification of the connection between MERA and
hyperbolic geometry which was proposed as a concrete implementation of the
AdS-CFT correspondence
Extending additivity from symmetric to asymmetric channels
We prove a lemma which allows one to extend results about the additivity of
the minimal output entropy from highly symmetric channels to a much larger
class. A similar result holds for the maximal output -norm. Examples are
given showing its use in a variety of situations. In particular, we prove the
additivity and the multiplicativity for the shifted depolarising channel.Comment: 8 pages. This is the latest version of the first half of the original
paper. The other half will appear in another pape
Tensor completion in hierarchical tensor representations
Compressed sensing extends from the recovery of sparse vectors from
undersampled measurements via efficient algorithms to the recovery of matrices
of low rank from incomplete information. Here we consider a further extension
to the reconstruction of tensors of low multi-linear rank in recently
introduced hierarchical tensor formats from a small number of measurements.
Hierarchical tensors are a flexible generalization of the well-known Tucker
representation, which have the advantage that the number of degrees of freedom
of a low rank tensor does not scale exponentially with the order of the tensor.
While corresponding tensor decompositions can be computed efficiently via
successive applications of (matrix) singular value decompositions, some
important properties of the singular value decomposition do not extend from the
matrix to the tensor case. This results in major computational and theoretical
difficulties in designing and analyzing algorithms for low rank tensor
recovery. For instance, a canonical analogue of the tensor nuclear norm is
NP-hard to compute in general, which is in stark contrast to the matrix case.
In this book chapter we consider versions of iterative hard thresholding
schemes adapted to hierarchical tensor formats. A variant builds on methods
from Riemannian optimization and uses a retraction mapping from the tangent
space of the manifold of low rank tensors back to this manifold. We provide
first partial convergence results based on a tensor version of the restricted
isometry property (TRIP) of the measurement map. Moreover, an estimate of the
number of measurements is provided that ensures the TRIP of a given tensor rank
with high probability for Gaussian measurement maps.Comment: revised version, to be published in Compressed Sensing and Its
Applications (edited by H. Boche, R. Calderbank, G. Kutyniok, J. Vybiral
The âGalilean Style in Scienceâ and the Inconsistency of Linguistic Theorising
Chomskyâs principle of epistemological tolerance says that in theoretical linguistics contradictions between the data and the hypotheses may be temporarily tolerated in order to protect the explanatory power of the theory. The paper raises the following problem: What kinds of contradictions may be tolerated between the data and the hypotheses in theoretical linguistics? First a model of paraconsistent logic is introduced which differentiates between week and strong contradiction. As a second step, a case study is carried out which exemplifies that the principle of epistemological tolerance may be interpreted as the tolerance of week contradiction. The third step of the argumentation focuses on another case study which exemplifies that the principle of epistemological tolerance must not be interpreted as the tolerance of strong contradiction. The reason for the latter insight is the unreliability and the uncertainty of introspective data. From this finding the author draws the conclusion that it is the integration of different data types that may lead to the improvement of current theoretical linguistics and that the integration of different data types requires a novel methodology which, for the time being, is not available
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