Tomographie quantique d'état : approche de réseau neuronal

Dans cours

PHYS-754: Lecture series on scientific machine learning

This lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the v ...

Description

Cette séance de cours introduit une nouvelle approche de la tomographie quantique à l'aide d'un réseau neuronal différentiel basé sur la machine de Boltzmann restreinte (RBM). L'architecture RBM, avec des couches visibles et cachées, est expliquée en détail, en mettant l'accent sur son modèle basé sur l'énergie similaire aux principes de la physique. La séance de cours couvre le processus de formation pour les paramètres d'amplitude et de phase séparément, en utilisant les gradients et la divergence KL. Les résultats sont présentés pour divers scénarios, y compris l'état W et les systèmes à mailles 1D, démontrant la précision de la GAR dans la prédiction des états quantiques. L'importance de mesurer l'amplitude et la phase en tomographie quantique est mise en évidence, ainsi que les défis de les découpler. La séance de cours se termine par une discussion sur les applications potentielles de cette approche au-delà des GAR, telles que les réseaux feedforward.

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Enseignant·e·s (10)

Lenka Zdeborová

Lenka Zdeborová is a Professor of Physics and of Computer Science in École Polytechnique Fédérale de Lausanne where she leads the Statistical Physics of Computation Laboratory. She received a PhD in physics from University Paris-Sud and from Charles University in Prague in 2008. She spent two years in the Los Alamos National Laboratory as the Director's Postdoctoral Fellow. Between 2010 and 2020 she was a researcher at CNRS working in the Institute of Theoretical Physics in CEA Saclay, France. In 2014, she was awarded the CNRS bronze medal, in 2016 Philippe Meyer prize in theoretical physics and an ERC Starting Grant, in 2018 the Irène Joliot-Curie prize, in 2021 the Gibbs lectureship of AMS. She is an editorial board member for Journal of Physics A, Physical Review E, Physical Review X, SIMODS, Machine Learning: Science and Technology, and Information and Inference. Lenka's expertise is in applications of concepts from statistical physics, such as advanced mean field methods, replica method and related message-passing algorithms, to problems in machine learning, signal processing, inference and optimization. She enjoys erasing the boundaries between theoretical physics, mathematics and computer science.

Anne-Clémence Corminboeuf

David Richard Harvey

Alexander Mathis

Alexander studied pure mathematics with a minor in logic and theory of science at the Ludwig Maximilians University in Munich. For his PhD also at LMU, he worked on optimal coding approaches to elucidate the properties of grid cells. As a postdoctoral fellow with Prof. Venkatesh N. Murthy at Harvard University and Prof. Matthias Bethge at Tuebingen AI, he decided to study olfactory behaviors such as odor-guided navigation, social behaviors and the cocktail party problem in mice. During this time, he increasingly got interested sensorimotor behaviors beyond olfaction and started working on proprioception, motor adaption, as well as computer vision tools for measuring animal behavior. In his group, he is interested in elucidating how the brain gives rise to adaptive behavior. One of the major goals is to synthesize large datasets into computationally useful information. For those purposes, he develops algorithms and systems to analyze animal behavior (e.g. DeepLabCut), neural data, as well as creates experimentally testable computational models.

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