1 code implementation • NeurIPS 2019 • Ivan Glasser, Ryan Sweke, Nicola Pancotti, Jens Eisert, Ignacio Cirac

Inspired by these developments, and the natural correspondence between tensor networks and probabilistic graphical models, we provide a rigorous analysis of the expressive power of various tensor-network factorizations of discrete multivariate probability distributions.

1 code implementation • 8 Jul 2019 • Ivan Glasser, Ryan Sweke, Nicola Pancotti, Jens Eisert, J. Ignacio Cirac

Inspired by these developments, and the natural correspondence between tensor networks and probabilistic graphical models, we provide a rigorous analysis of the expressive power of various tensor-network factorizations of discrete multivariate probability distributions.

1 code implementation • 29 Mar 2019 • Giuseppe Carleo, Kenny Choo, Damian Hofmann, James E. T. Smith, Tom Westerhout, Fabien Alet, Emily J. Davis, Stavros Efthymiou, Ivan Glasser, Sheng-Hsuan Lin, Marta Mauri, Guglielmo Mazzola, Christian B. Mendl, Evert van Nieuwenburg, Ossian O'Reilly, Hugo Théveniaut, Giacomo Torlai, Alexander Wietek

We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques.

Quantum Physics Disordered Systems and Neural Networks Strongly Correlated Electrons Computational Physics Data Analysis, Statistics and Probability

no code implementations • 15 Jun 2018 • Ivan Glasser, Nicola Pancotti, J. Ignacio Cirac

We discuss the relationship between generalized tensor network architectures used in quantum physics, such as string-bond states, and architectures commonly used in machine learning.

no code implementations • 11 Oct 2017 • Ivan Glasser, Nicola Pancotti, Moritz August, Ivan D. Rodriguez, J. Ignacio Cirac

In particular we demonstrate that short-range Restricted Boltzmann Machines are Entangled Plaquette States, while fully connected Restricted Boltzmann Machines are String-Bond States with a nonlocal geometry and low bond dimension.

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