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Variational Monte Carlo

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/HUJI-Deep/FlowKet

공식 코드

Deep autoregressive models for the efficient variational simulation of many-body quantum systems

https://github.com/catniplab/svmc

공식 코드

Streaming Variational Monte Carlo

https://github.com/cqsl/electron-tvmc

공식 코드

Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation

https://github.com/cqsl/tre-tvmc

공식 코드

Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation

https://github.com/deepmind/ferminet

공식 코드

Discovering Quantum Phase Transitions with Fermionic Neural Networks

https://github.com/deepmind/spectral_inference_networks

공식 코드

Spectral Inference Networks: Unifying Deep and Spectral Learning

https://github.com/deepqmc/deepqmc

공식 코드

Convergence to the fixed-node limit in deep variational Monte Carlo

https://github.com/google-deepmind/ferminet

공식 코드

Accurate Computation of Quantum Excited States with Neural Networks

https://github.com/jeffminlin/vmcnet

공식 코드

Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation

https://github.com/lorrespz/nqs_hyperbolic_rnn

공식 코드

Hyperbolic recurrent neural network as the first type of non-Euclidean neural quantum state ansatz

https://github.com/mdsunivie/deeperwin

공식 코드

Variational Monte Carlo on a Budget -- Fine-tuning pre-trained Neural Wavefunctions

https://github.com/n-gao/pesnet

공식 코드

Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions

https://github.com/netket/netket

공식 코드

NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems

https://github.com/rehmoritz/vmc_pde

공식 코드

Variational Monte Carlo Approach to Partial Differential Equations with Neural Networks

https://github.com/rqhe1/acnn

공식 코드

Variational optimization of the amplitude of neural-network quantum many-body ground states

https://github.com/shravanvn/cnqs

공식 코드

Continuous-variable neural-network quantum states and the quantum rotor model

https://github.com/tomdbar/naqs-for-quantum-chemistry

공식 코드

Autoregressive neural-network wavefunctions for ab initio quantum chemistry

https://github.com/wangleiphy/VAN.jl

공식 코드

Solving Statistical Mechanics Using Variational Autoregressive Networks

https://github.com/wdphy16/mps-rnn

공식 코드

From Tensor Network Quantum States to Tensorial Recurrent Neural Networks

https://github.com/wdphy16/neural-cluster-update

공식 코드

Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks

https://github.com/wdphy16/stat-mech-van

공식 코드

Solving Statistical Mechanics Using Variational Autoregressive Networks

https://github.com/Ericolony/QAOA

Natural evolution strategies and variational Monte Carlo

https://github.com/HUJI-Deep/PyKet

Deep autoregressive models for the efficient variational simulation of many-body quantum systems

https://github.com/dsunivie/deeperwin

Towards a Foundation Model for Neural Network Wavefunctions

https://github.com/jongharyu/neural-svd

Spectral Inference Networks: Unifying Deep and Spectral Learning

https://github.com/microsoft/folx

Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo

https://github.com/mipunivie/deeperwin

Towards a Foundation Model for Neural Network Wavefunctions

https://github.com/reisenhofer/deeperwin

Towards a Foundation Model for Neural Network Wavefunctions

https://github.com/ywolfeee/lapjax

Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.