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Abstract We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and flexible… Continue Reading
The use of artificial neural networks to represent quantum wave-functions has recently attracted interest as a way to solve complex many-body problems. The potential of these variational… Continue Reading
Machine learning algorithms based on artificial neural networks have proven very useful for a variety of classification problems. Here we apply them to a well-known problem in crystallography, namely… Continue Reading
We propose an exact construction for atypical excited states of a class of non-integrable quantum many-body Hamiltonians in one dimension (1D), two dimensions (2D), and three dimensins (3D) that… Continue Reading
Neural-network quantum states have been successfully used to study a variety of lattice and continuous-space problems. Despite a great deal of general methodological developments, representing… Continue Reading
We study the heat and spin transport properties in a ring of interacting spins coupled to heat baths at different temperatures. We show that interactions, by inducing avoided crossings, can be a… Continue Reading
While driven interacting quantum matter is generically subject to heating and scrambling, certain classes of systems evade this paradigm. We study such an exceptional class in periodically driven… Continue Reading
Kenny Choo,1 Giuseppe Carleo,2 Nicolas Regnault,3 and Titus Neupert1 Department of Physics, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland Center for Computational Quantum… Continue Reading
One of the new features in the H.264/AVC encoder is the use of Lagrangian Rate-Distortion Optimised (RDO) mode decision at the macroblock (MB) level. This brute-force algorithm searches through the… Continue Reading