Machine learning phases of an Abelian gauge theory

Abstract

The phase transition of the two-dimensional $U(1)$ quantum link model on the triangular lattice is investigated by employing a supervised neural network (NN) consisting of only one input layer, one hidden layer of two neurons, and one output layer. No information on the studied model is used when the NN training is conducted. Instead, two artificially made configurations are considered as the training set. Interestingly, the obtained NN not only estimates the critical point accurately but also uncovers the physics correctly. The results presented here imply that a supervised NN, which has a very simple architecture and is trained without any input from the investigated model, can identify the targeted phase structure with high precision.

arXiv comment7 pages, 12 figures

References in corpus (34)

• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• Machine learning phases of matter
• Learning phase transitions by confusion
• Finding Density Functionals with Machine Learning
• Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
• Discovering Phases, Phase Transitions and Crossovers through Unsupervised Machine Learning: A critical examination
• Quantum Link Models: A Discrete Approach to Gauge Theories
• Machine Learning Topological States
• Identifying topological order through unsupervised machine learning
• QCD as a Quantum Link Model
• Detection of phase transition via convolutional neural network
• Unsupervised machine learning and band topology
• Unsupervised machine learning account of magnetic transitions in the Hubbard model
• Automation and control of laser wakefield accelerators using Bayesian optimisation
• Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector
• Optimal Feedback Cooling of a Charged Levitated Nanoparticle with Adaptive Control
• Emergence of Compositional Representations in Restricted Boltzmann Machines
• Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems
• Machine Learning ${\mathbb{Z}}_2$ Quantum Spin Liquids with Quasi-particle Statistics
• Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
• Machine learning of phase transitions in the percolation and XY models
• Enhanced Higgs to ${\tau }^{+}{\tau }^{-}$ Searches with Deep Learning
• Machine learning of quantum phase transitions
• Machine learning action parameters in lattice quantum chromodynamics
• Finding the origin of noise transients in LIGO data with machine learning
• The (2+1)-d U(1) Quantum Link Model Masquerading as Deconfined Criticality
• Unsupervised machine learning of quantum phase transitions using diffusion maps
• Applications of neural networks to the studies of phase transitions of two-dimensional Potts models
• Determination of the $WW$ polarization fractions in $pp\to W^{\pm }{W}^{\pm }jj$ using a deep machine learning technique
• Constraints on the Physical Properties of GW190814 through Simulations based on DECam Follow-up Observations by the Dark Energy Survey
• From the $SU(2)$ Quantum Link Model on the Honeycomb Lattice to the Quantum Dimer Model on the Kagomé Lattice: Phase Transition and Fractionalized Flux Strings
• A Neural Networks study of the phase transitions of Potts model
• Machine learning phases and criticalities without using real data for training
• Identification of strongly interacting organic semimetals