Constructing neural stationary states for open quantum many-body systems
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 …
Drawing phase diagrams of random quantum systems by deep learning the wave functions
T Ohtsuki, T Mano - Journal of the Physical Society of Japan, 2020 - journals.jps.jp
Applications of neural networks to condensed matter physics are becoming popular and
beginning to be well accepted. Obtaining and representing the ground and excited state …
beginning to be well accepted. Obtaining and representing the ground and excited state …
Purifying deep boltzmann machines for thermal quantum states
We develop two cutting-edge approaches to construct deep neural networks representing
the purified finite-temperature states of quantum many-body systems. Both methods …
the purified finite-temperature states of quantum many-body systems. Both methods …
Disentangling representations in restricted boltzmann machines without adversaries
A goal of unsupervised machine learning is to build representations of complex high-
dimensional data, with simple relations to their properties. Such disentangled …
dimensional data, with simple relations to their properties. Such disentangled …
Large-scale Ising emulation with four body interaction and all-to-all connections
Optical Ising machines with two-body interactions have shown potential in solving
combinatorial optimization problems which are extremely hard to solve with digital …
combinatorial optimization problems which are extremely hard to solve with digital …
Barriers and dynamical paths in alternating Gibbs sampling of restricted Boltzmann machines
Restricted Boltzmann machines (RBM) are bilayer neural networks used for the
unsupervised learning of model distributions from data. The bipartite architecture of RBM …
unsupervised learning of model distributions from data. The bipartite architecture of RBM …
Deep reinforcement learning for preparation of thermal and prethermal quantum states
We propose a method based on deep reinforcement learning that efficiently prepares a
quantum many-body pure state in thermal or prethermal equilibrium. The main physical …
quantum many-body pure state in thermal or prethermal equilibrium. The main physical …
Exact representations of many-body interactions with restricted-Boltzmann-machine neural networks
Restricted Boltzmann machines (RBMs) are simple statistical models defined on a bipartite
graph which have been successfully used in studying more complicated many-body …
graph which have been successfully used in studying more complicated many-body …
Convolutional restricted Boltzmann machine aided Monte Carlo: An application to Ising and Kitaev models
Machine learning is becoming widely used in analyzing the thermodynamics of many-body
condensed matter systems. Restricted Boltzmann machine (RBM) aided Monte Carlo …
condensed matter systems. Restricted Boltzmann machine (RBM) aided Monte Carlo …
Learning spin liquids on a honeycomb lattice with artificial neural networks
CX Li, S Yang, JB Xu - Scientific Reports, 2021 - nature.com
Abstract Machine learning methods provide a new perspective on the study of many-body
system in condensed matter physics and there is only limited understanding of their …
system in condensed matter physics and there is only limited understanding of their …