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hellloxiaotian / BRDNetImage denoising using deep CNN with batch renormalization(Neural Networks,2020)
titu1994 / BatchRenormalizationBatch Renormalization algorithm implementation in Keras
hongyehu / RG FlowThis is project page for the paper "RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior". Paper link: https://arxiv.org/abs/2010.00029
li012589 / NeuralRGPytorch source code for arXiv paper Neural Network Renormalization Group, a generative model using variational renormalization group and normalizing flow.
KellerJordan / REPAIRCode release for REPAIR: REnormalizing Permuted Activations for Interpolation Repair
ludvb / BatchrenormBatch Renormalization in Pytorch
VictorVanthilt / TNRKit.jlTNRKit is a Tensor Network Renormalization Julia package that aims to implement as many TNR algorithms as possible
ragnarstroberg / ImsrgIn-Medium Similarity Renormalization Group software for nuclear structure calculations. Written in C++, with Python bindings.
sanshar / BlockBlock implements the density matrix renormalization group (DMRG) algorithm for quantum chemistry.
rokzitko / NrgljubljanaNRG Ljubljana is a numerical renormalization group implementation for solving quantum impurity problems in theoretical physics
Gilt-TNR / Gilt TNRAn implementation of the Gilt-TNR tensor network renormalization algorithm for square and cubical lattices.
aromanro / IsingMonteCarloA program implementing Metropolis Monte Carlo for the 2D square-lattice Ising model and the spin block renormalization
RSMI-NE / RSMI NEA Python package for efficient optimisation of real-space renormalization group transformations using Tensorflow.
dominikkiese / PFFRGSolver.jlPseudofermion functional renormalization group solver
etano / DmrgpyDensity Matrix Renormalization Group (DMRG) in Python
nplresearch / Higher Order LRGPython code to perform higher-order Laplacian renormalization of higher-order networks
liwt31 / SimpleMPSMatrix product states (MPS) based density matrix renormalization group (DMRG)
EverettYou / MLRGMachine Learning Renormalization Group
fbuessen / SpinParserPseudofermion functional renormalization group solver for (frustrated) quantum magnets in two and three spatial dimensions.
NuclearPhysicsWorkshops / FRIB TASummerSchoolQuantumComputingRecent developments in quantum information systems and technologies offer the possibility to address some of the most challenging large-scale problems in science, whether they are represented by complicated interacting quantum mechanical systems or classical systems. The last years have seen a rapid and exciting development in algorithms and quantum hardware. The emphasis of this summer school is to highlight, through a series of lectures and hands-on exercises and practice sessions, how quantum computing algorithms can be used to study nuclear few- and many-body problems of relevance for low-energy nuclear physics. And how quantum computing algorithms can aid in studying systems with increasingly many more degrees of freedom compared with more classical few- and many-body methods. Several quantum algorithms for solving quantum-mechanical few- and many-particle problems with be discussed. The lectures will start with the basic ideas of quantum computing. Thereafter, through examples from nuclear physics, we will elucidate how different quantum algorithms can be used to study these systems. The results from various quantum computing algorithms will be compared to standard methods like full configuration interaction theory, field theories on the lattice, in-medium similarity renormalization group and coupled cluster theories.
