Machine learning and the physical sciences
Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …
for a vast array of data processing tasks, which has entered most scientific disciplines in …
Restricted Boltzmann machines in quantum physics
Restricted Boltzmann machines in quantum physics | Nature Physics Skip to main content Thank
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NetKet 3: Machine learning toolbox for many-body quantum systems
We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum
physics. NetKet is built around neural-network quantum states and provides efficient …
physics. NetKet is built around neural-network quantum states and provides efficient …
Neural-network approach to dissipative quantum many-body dynamics
MJ Hartmann, G Carleo - Physical review letters, 2019 - APS
In experimentally realistic situations, quantum systems are never perfectly isolated and the
coupling to their environment needs to be taken into account. Often, the effect of the …
coupling to their environment needs to be taken into account. Often, the effect of the …
Deep autoregressive models for the efficient variational simulation of many-body quantum systems
Artificial neural networks were recently shown to be an efficient representation of highly
entangled many-body quantum states. In practical applications, neural-network states inherit …
entangled many-body quantum states. In practical applications, neural-network states inherit …
Symmetries and many-body excitations with neural-network quantum states
Artificial neural networks have been recently introduced as a general ansatz to represent
many-body wave functions. In conjunction with variational Monte Carlo calculations, this …
many-body wave functions. In conjunction with variational Monte Carlo calculations, this …
Constructing neural stationary states for open quantum many-body systems
N Yoshioka, R Hamazaki - Physical Review B, 2019 - APS
We propose a scheme based on the neural-network quantum states to simulate the
stationary states of open quantum many-body systems. Using the high expressive power of …
stationary states of open quantum many-body systems. Using the high expressive power of …
Equivalence of restricted Boltzmann machines and tensor network states
The restricted Boltzmann machine (RBM) is one of the fundamental building blocks of deep
learning. RBM finds wide applications in dimensional reduction, feature extraction, and …
learning. RBM finds wide applications in dimensional reduction, feature extraction, and …
Dirac-type nodal spin liquid revealed by refined quantum many-body solver using neural-network wave function, correlation ratio, and level spectroscopy
Pursuing fractionalized particles that do not bear properties of conventional measurable
objects, exemplified by bare particles in the vacuum such as electrons and elementary …
objects, exemplified by bare particles in the vacuum such as electrons and elementary …
Modern applications of machine learning in quantum sciences
In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …
advances in the application of machine learning methods in quantum sciences. We cover …