Ecole Polytechnique

A computer-implemented method for determining a quantum phase parameter characterizing a property of a quantum phase of a solid-state material sample, which comprises determining, for a bath site of an impurity-bath model of the solid-state material sample, respective parameters Vp and εp of the bath site, where Vp represents a coupling strength of the bath site, and εp represents an energy of the bath site, configuring a quantum circuit for computing a Green's function of the solid-state material sample corresponding to the impurity-bath model, wherein one or more quantum gates of the quantum circuit are configured based on the determined respective parameters Vp and εp, wherein the quantum circuit is configured to use one or more noisy qubits, wherein respective noise patterns of the noisy qubits are used to represent dissipation of the impurity-bath model; and executing, on a quantum computer, the quantum circuit to determine an estimate of the quantum phase parameter.

Il est proposé un dispositif électronique comportant au moins une couche réalisée dans un matériau comportant une pérovskite inorganique : * de composition stœchiométrique ABX<3> où : - A est un élément parmi Cs et Rb, - B est un élément ou un mélange d'éléments parmi Ge, Sn et Pb, et - X est un élément ou un mélange d'éléments parmi Cl, Br et I, * et ayant un facteur de Goldschmidt t compris entre 0,87 et 0,93, caractérisant une stabilité satisfaisante.

The disclosure notably relates to a computer-implemented method of using a machine-learning function preconfigured for taking an input 3D layout and a given noise level, and for predicting an output 3D layout. The function is preconfigured with a conditioning drop-out with respect to at least one layout parameter. The method further comprises obtaining a set of conditioning inputs and, for each conditioning input, determining one or more conditioning candidate 3D layouts and determining a plurality of perturbed conditioning candidate 3D layouts. The method further comprises, applying the preconfigured function to each perturbed conditioning candidate, in which the one layout parameter is dropped out, computing reconstruction errors, and averaging the reconstruction errors, thereby obtaining a score. This forms an improved solution for predicting 3D layouts.

The present invention concerns a method for preparing a spherical core-shell skin model, said method comprising the steps of: a) forming microdroplets of an aqueous solution comprising fibroblasts and collagen, in an oil, wherein the formed collagen microdroplets comprise at least one fibroblast per microdroplet; b) gelling at least a portion of the formed collagen microdroplets comprising said at least one fibroblast, thereby obtaining collagen beads comprising at least one fibroblast per bead; c) replacing the oil surrounding the collagen beads comprising said at least one fibroblast with a culture medium, d) contacting (i) the collagen beads comprising said at least one fibroblast with (ii) keratinocytes, and e) incubating (i) said collagen beads with (ii) said keratinocytes for a period of time enabling the formation of an external layer by said keratinocytes around said collagen beads, thereby obtaining a spherical core-shell skin model, the core comprising at least one fibroblast and the shell comprising at least one keratinocyte.

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