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Found 3 papers, 1 papers with code
Deep Machine Learning Reconstructing Lattice Topology with Strong Thermal Fluctuations
In this work, we demonstrate the validity of the deep convolutional neural network (CNN) on reconstructing the lattice topology (i. e., spin connectivities) in the presence of strong thermal fluctuations and unbalanced data.
Predicting Quantum Potentials by Deep Neural Network and Metropolis Sampling
The hybridizations of machine learning and quantum physics have caused essential impacts to the methodology in both fields.
Quantum simulation for thermodynamics of infinite-size many-body systems by O(10) sites
In this work we propose to simulate many-body thermodynamics of infinite-size quantum lattice models in one, two, and three dimensions, in terms of few-body models of only O(10) sites, which we coin as quantum entanglement simulators (QES's).
Strongly Correlated Electrons Computational Physics Quantum Physics
