2,734 research outputs found
Quantifying dependencies for sensitivity analysis with multivariate input sample data
We present a novel method for quantifying dependencies in multivariate
datasets, based on estimating the R\'{e}nyi entropy by minimum spanning trees
(MSTs). The length of the MSTs can be used to order pairs of variables from
strongly to weakly dependent, making it a useful tool for sensitivity analysis
with dependent input variables. It is well-suited for cases where the input
distribution is unknown and only a sample of the inputs is available. We
introduce an estimator to quantify dependency based on the MST length, and
investigate its properties with several numerical examples. To reduce the
computational cost of constructing the exact MST for large datasets, we explore
methods to compute approximations to the exact MST, and find the multilevel
approach introduced recently by Zhong et al. (2015) to be the most accurate. We
apply our proposed method to an artificial testcase based on the Ishigami
function, as well as to a real-world testcase involving sediment transport in
the North Sea. The results are consistent with prior knowledge and heuristic
understanding, as well as with variance-based analysis using Sobol indices in
the case where these indices can be computed
Compression of quantum measurement operations
We generalize recent work of Massar and Popescu dealing with the amount of
classical data that is produced by a quantum measurement on a quantum state
ensemble. In the previous work it was shown how spurious randomness generally
contained in the outcomes can be eliminated without decreasing the amount of
knowledge, to achieve an amount of data equal to the von Neumann entropy of the
ensemble. Here we extend this result by giving a more refined description of
what constitute equivalent measurements (that is measurements which provide the
same knowledge about the quantum state) and also by considering incomplete
measurements. In particular we show that one can always associate to a POVM
with elements a_j, an equivalent POVM acting on many independent copies of the
system which produces an amount of data asymptotically equal to the entropy
defect of an ensemble canonically associated to the ensemble average state and
the initial measurement (a_j). In the case where the measurement is not
maximally refined this amount of data is strictly less than the von Neumann
entropy, as obtained in the previous work. We also show that this is the best
achievable, i.e. it is impossible to devise a measurement equivalent to the
initial measurement (a_j) that produces less data. We discuss the
interpretation of these results. In particular we show how they can be used to
provide a precise and model independent measure of the amount of knowledge that
is obtained about a quantum state by a quantum measurement. We also discuss in
detail the relation between our results and Holevo's bound, at the same time
providing a new proof of this fundamental inequality.Comment: RevTeX, 13 page
Large deviations of cascade processes on graphs
Simple models of irreversible dynamical processes such as Bootstrap
Percolation have been successfully applied to describe cascade processes in a
large variety of different contexts. However, the problem of analyzing
non-typical trajectories, which can be crucial for the understanding of the
out-of-equilibrium phenomena, is still considered to be intractable in most
cases. Here we introduce an efficient method to find and analyze optimized
trajectories of cascade processes. We show that for a wide class of
irreversible dynamical rules, this problem can be solved efficiently on
large-scale systems
Entropy-power uncertainty relations : towards a tight inequality for all Gaussian pure states
We show that a proper expression of the uncertainty relation for a pair of
canonically-conjugate continuous variables relies on entropy power, a standard
notion in Shannon information theory for real-valued signals. The resulting
entropy-power uncertainty relation is equivalent to the entropic formulation of
the uncertainty relation due to Bialynicki-Birula and Mycielski, but can be
further extended to rotated variables. Hence, based on a reasonable assumption,
we give a partial proof of a tighter form of the entropy-power uncertainty
relation taking correlations into account and provide extensive numerical
evidence of its validity. Interestingly, it implies the generalized
(rotation-invariant) Schr\"odinger-Robertson uncertainty relation exactly as
the original entropy-power uncertainty relation implies Heisenberg relation. It
is saturated for all Gaussian pure states, in contrast with hitherto known
entropic formulations of the uncertainty principle.Comment: 15 pages, 5 figures, the new version includes the n-mode cas
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