Fonction de Wigner

Résumé

vignette| Fonction de Wigner d'un état du type du "chat de Schrödinger" (mélange de 2 états opposés) La fonction de Wigner (également appelée distribution de quasi-probabilité de Wigner) a été introduite par Eugene Wigner en 1932 pour étudier les corrections quantiques à la mécanique statistique classique. L'objectif était de lier la fonction d'onde qui apparaît dans l'équation de Schrödinger à une distribution de probabilité dans l'espace des phases. La fonction de Wigner est une fonction génératrice de toutes les fonctions d'autocorrélation spatiale d'une fonction d'onde donnée en mécanique quantique. Ainsi, elle applique à la matrice de densité quantique la bijection entre les fonctions réelles de l'espace des phases et les opérateurs hermitiens introduite par Hermann Weyl en 1927 , dans un contexte lié à la théorie des représentations en mathématiques (voir l'article en anglais transformée de Weyl-Wigner). En effet, la fonction de Wigner est la transformée de Wigner-Weyl de la m

Source officielle

https://fr.wikipedia.org/wiki/Fonction_de_Wigner

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In the absence of a full analytical treatment of nonlinear structure formation in the universe, numerical simulations provide the critical link between the properties of the underlying model and the features of the observed structures. Currently N-body simulations are the main tool to study structure growth. We explore an alternative framework for numerical simulations of structure formation. The underlying idea is to replace the long-range gravitational force in the Vlasov-Poisson system by a purely local interaction. To this end we trade the classical phase space distribution for its quantum mechanical counterpart, the Wigner distribution function. Its dynamical equation is equivalent to the Schrödinger equation and reduces to the Vlasov equation in the formal semi-classical limit. The proposed framework allows in principle to simulate systems with arbitrary phase space distributions and could for instance be beneficial for simulations of warm dark matter, where the velocity dispersion is important. We discuss several methods to obtain a set of wavefunctions whose Wigner distribution is close to a given initial phase space distribution function. An auxiliary gauge field is introduced to mediate the gravitational interaction, thereby obtaining a local Schrödinger-Maxwell system. We also use the ideas of lattice gauge theories to obtain a fully gauge-invariant discretization of the equations of motion. Their iterative solution was implemented in a three-dimensional simulation code. We discuss its computational complexity and memory requirements. Several testbed simulations were performed with this method. We compared the gravitational collapse of a Gaussian wavefunction with an independent numerical solution of the spherically symmetric Schrödinger-Newton system. The results were found to be in good agreement. Finally, a simple example of the growth of cosmic perturbations is investigated within our framework. We conclude by outlining various possible directions to optimize and develop our method.

EPFL2011

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The Wigner phase operator (WPO) is identified as an operator valued measure (OVM) and its eigenstates are obtained. An operator satisfying the canonical commutation relation with the Wigner phase operator is also constructed and this establishes a Wigner distribution based operator formalism for the Wigner phase distribution. It is then argued that the WPO cannot represent a projective measurement of the phase; but is in fact to be interpreted as an operator valued measure for the phase. The non-positivity of the latter can be overcome by defining a positive operator valued measure (POVM) via a proper filter function, based on the view that phase measurements are coarse-grained in phase space, leading to the well known Q-distribution. The identification of the Q phase operator as a POVM is in good agreement with the earlier observation regarding the relation between operational phase measurement schemes and the Q-distribution. The Q phase POVM can be dilated in the sense of Gelfand-Naimark, to an operational setting of interference at a beam-splitter with another coherent state - this results in a von Neumann projector with well-defined phase in the expanded Hilbert space of the two modes.

Taylor & Francis Ltd2013

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Dictionary Learning Based on Sparse Distribution Tomography

Pedram Pad, Michaël Unser

We propose a new statistical dictionary learning algorithm for sparse signals that is based on an α-stable innovation model. The parameters of the underlying model—that is, the atoms of the dictionary, the sparsity index α and the dispersion of the transform-domain coefficients—are recovered using a new type of probability distribution tomography. Specifically, we drive our estimator with a series of random projections of the data, which results in an efficient algorithm. Moreover, since the projections are achieved using linear combinations, we can invoke the generalized central limit theorem to justify the use of our method for sparse signals that are not necessarily α-stable. We evaluate our algorithm by performing two types of experiments: image in-painting and image denoising. In both cases, we find that our approach is competitive with state-of-the-art dictionary learning techniques. Beyond the algorithm itself, two aspects of this study are interesting in their own right. The first is our statistical formulation of the problem, which unifies the topics of dictionary learning and independent component analysis. The second is a generalization of a classical theorem about isometries of

ℓ _{ p }

-norms that constitutes the foundation of our approach.

P. Pad2017

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Dictionary Learning Based on Sparse Distribution Tomography

Pedram Pad, Michaël Unser

ℓ _{ p }

-norms that constitutes the foundation of our approach.

P. Pad2017

EPFL2011