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.
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.
no code implementations • 28 Nov 2016 • Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, Seth Lloyd
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data.
no code implementations • 20 Jul 2016 • Leonardo Banchi, Nicola Pancotti, Sougato Bose
We show how to train a quantum network of pairwise interacting qubits such that its evolution implements a target quantum algorithm into a given network subset.