Machine learning action parameters in lattice quantum chromodynamics

  title={Machine learning action parameters in lattice quantum chromodynamics},
  author={Phiala E. Shanahan and Daniel Trewartha and William Detmold},
  journal={arXiv: High Energy Physics - Lattice},
Numerical lattice quantum chromodynamics studies of the strong interaction are important in many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The… 

Super-resolving the Ising model with convolutional neural networks

It is found that it is possible to predict thermodynamic quantities for lattice sizes larger than those used in training by extrapolating the parameters of the network and this method is used to extrapolate the exponents of the 2D Ising critical point towards the thermodynamic limit, which results in good agreement with theory.

Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning

This work investigates action parameter regression as a pretext task while using layer-wise relevance propagation (LRP) to identify the most important observables depending on the location in the phase diagram and argues that due to its broad applicability, attribution methods such as LRP could prove a useful and versatile tool in the search for new physical insights.

Finding the deconfinement temperature in lattice Yang-Mills theories from outside the scaling window with machine learning

The neural network of the machine-learning algorithm, trained on 'bare' lattice configurations at an unphysical value of the lattice parameters as an input, builds up a gauge-invariant function, and finds correlations with the target observable that is valid in the physical region of the parameter space.

Regressive and generative neural networks for scalar field theory

This work analyzes a broad range of chemical potentials and finds that the network is robust and able to recognize patterns far away from the point where it was trained, and elaborate on potential uses of such a generative approach for sampling outside the training region.

On Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models

It is shown that generative models can be used to estimate the absolute value of the free energy, which is in contrast to existing MCMC-based methods, which are limited to only estimate free energy differences.

Machine learning estimators for lattice QCD observables

A novel technique using machine learning (ML) to reduce the computational cost of evaluating lattice quantum chromodynamics (QCD) observables is presented. The ML is trained on a subset of background

Machine learning spectral functions in lattice QCD

It is shown that the SVAE in most cases is comparable to the maximum entropy method (MEM) in the quality of reconstructing spectral functions and even outperforms the MEM in the case where the spectral function has sharp peaks with insufficient number of data points in the correlator.

Interpreting machine learning of topological quantum phase transitions

There has been growing excitement over the possibility of employing artificial neural networks (ANNs) to gain new theoretical insight into the physics of quantum many-body problems.

Neural-Network Quantum State of Transverse-Field Ising Model

This work constructs the neural-network quantum state of transverse-field Ising model (TFIM) by an unsupervised machine learning method and uses this quantum state to calculate entanglement entropy (EE) of the system and gets results consistent with previous work very well.



Machine Learning Phases of Strongly Correlated Fermions

This work shows that a three dimensional convolutional network trained on auxiliary field configurations produced by quantum Monte Carlo simulations of the Hubbard model can correctly predict the magnetic phase diagram of the model at the average density of one (half filling).

Solving the quantum many-body problem with artificial neural networks

A variational representation of quantum states based on artificial neural networks with a variable number of hidden neurons and a reinforcement-learning scheme that is capable of both finding the ground state and describing the unitary time evolution of complex interacting quantum systems.

Machine Learning of Explicit Order Parameters: From the Ising Model to SU(2) Lattice Gauge Theory

A procedure for reconstructing the decision function of an artificial neural network as a simple function of the input, provided the decisionfunction is sufficiently symmetric.

Lattice QCD input for nuclear structure and reactions

Explorations of the properties of light nuclear systems beyond their lowestlying spectra have begun with Lattice Quantum Chromodynamics. While progress has been made in the past year in pursuing

Machine learning phases of matter

It is shown that modern machine learning architectures, such as fully connected and convolutional neural networks, can identify phases and phase transitions in a variety of condensed-matter Hamiltonians.

Self-learning Monte Carlo method

A general-purpose Monte Carlo method, dubbed self-learning Monte Carlo (SLMC), is proposed, in which an efficient update algorithm is first learned from the training data generated in trial simulations and then used to speed up the actual simulation.

Machine Learning Topological Invariants with Neural Networks

After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data.

Multiscale Monte Carlo equilibration: Two-color QCD with two fermion flavors

We demonstrate the applicability of a recently proposed multiscale thermalization algorithm to two-color quantum chromodynamics (QCD) with two mass-degenerate fermion flavors. The algorithm involves

Learning Thermodynamics with Boltzmann Machines

A Boltzmann machine is developed that is capable of modeling thermodynamic observables for physical systems in thermal equilibrium and can faithfully reproduce the observables of the physical system.