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Variational Monte Carlo

Variational methods for quantum physics

Papers

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Showing 1–25 of 55 papers

TitleStatusHype
DeepQuark: deep-neural-network approach to multiquark bound states—0
Hyperbolic recurrent neural network as the first type of non-Euclidean neural quantum state ansatzCode0
Improving Energy Natural Gradient Descent through Woodbury, Momentum, and Randomization—0
Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems—0
Is attention all you need to solve the correlated electron problem?—0
Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation—0
Generalized Lanczos method for systematic optimization of neural-network quantum states—0
Retentive Neural Quantum States: Efficient Ansätze for Ab Initio Quantum Chemistry—0
A Theoretical Framework for an Efficient Normalizing Flow-Based Solution to the Electronic Schrodinger Equation—0
Transferable Neural Wavefunctions for SolidsCode2
Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equationCode0
A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctionsCode1
Open-Source Fermionic Neural Networks with Ionic Charge Initialization—0
Pairing-based graph neural network for simulating quantum materials—0
Accurate Computation of Quantum Excited States with Neural NetworksCode2
Neural-network quantum state study of the long-range antiferromagnetic Ising chain—0
Variational optimization of the amplitude of neural-network quantum many-body ground statesCode0
Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte CarloCode1
Variational Monte Carlo on a Budget -- Fine-tuning pre-trained Neural WavefunctionsCode0
NNQS-Transformer: an Efficient and Scalable Neural Network Quantum States Approach for Ab initio Quantum Chemistry—0
Neural Wave Functions for Superfluids—0
Convergence of variational Monte Carlo simulation and scale-invariant pre-training—0
Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators—0
Towards a Foundation Model for Neural Network WavefunctionsCode1
Towards Neural Variational Monte Carlo That Scales Linearly with System Size—0
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