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Variational Monte Carlo

Variational methods for quantum physics

Papers

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Showing 1–50 of 55 papers

TitleStatusHype
Transferable Neural Wavefunctions for SolidsCode2
Accurate Computation of Quantum Excited States with Neural NetworksCode2
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave FunctionsCode1
Explicitly antisymmetrized neural network layers for variational Monte Carlo simulationCode1
Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte CarloCode1
A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctionsCode1
Autoregressive neural-network wavefunctions for ab initio quantum chemistryCode1
Solving Statistical Mechanics Using Variational Autoregressive NetworksCode1
Spectral Inference Networks: Unifying Deep and Spectral LearningCode1
Towards a Foundation Model for Neural Network WavefunctionsCode1
NetKet 3: Machine Learning Toolbox for Many-Body Quantum SystemsCode1
Variational Monte Carlo Approach to Partial Differential Equations with Neural NetworksCode1
Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equationCode0
Streaming Variational Monte CarloCode0
Natural evolution strategies and variational Monte CarloCode0
Variational Monte Carlo on a Budget -- Fine-tuning pre-trained Neural WavefunctionsCode0
Convergence to the fixed-node limit in deep variational Monte CarloCode0
Deep autoregressive models for the efficient variational simulation of many-body quantum systemsCode0
Hyperbolic recurrent neural network as the first type of non-Euclidean neural quantum state ansatzCode0
From Tensor Network Quantum States to Tensorial Recurrent Neural NetworksCode0
Unbiased Monte Carlo Cluster Updates with Autoregressive Neural NetworksCode0
Continuous-variable neural-network quantum states and the quantum rotor modelCode0
Discovering Quantum Phase Transitions with Fermionic Neural NetworksCode0
Variational optimization of the amplitude of neural-network quantum many-body ground statesCode0
Rayleigh-Gauss-Newton optimization with enhanced sampling for variational Monte Carlo—0
Retentive Neural Quantum States: Efficient Ansätze for Ab Initio Quantum Chemistry—0
Certified variational quantum algorithms for eigenstate preparation—0
Toward Neural Network Simulation of Variational Quantum Algorithms—0
Towards Neural Variational Monte Carlo That Scales Linearly with System Size—0
Understanding and eliminating spurious modes in variational Monte Carlo using collective variables—0
Variational Policy Gradient Method for Reinforcement Learning with General Utilities—0
Neural Wave Functions for Superfluids—0
Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems—0
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization—0
An Empirical Study of Quantum Dynamics as a Ground State Problem with Neural Quantum States—0
A Theoretical Framework for an Efficient Normalizing Flow-Based Solution to the Electronic Schrodinger Equation—0
Convergence of variational Monte Carlo simulation and scale-invariant pre-training—0
DeepQuark: deep-neural-network approach to multiquark bound states—0
Determinant-free fermionic wave function using feed-forward neural networks—0
Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation—0
Electronic excited states in deep variational Monte Carlo—0
Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulation—0
Generalized Lanczos method for systematic optimization of neural-network quantum states—0
Improving Energy Natural Gradient Descent through Woodbury, Momentum, and Randomization—0
Is attention all you need to solve the correlated electron problem?—0
Machine learning predictions for local electronic properties of disordered correlated electron systems—0
Meta Variational Monte Carlo—0
Neural-network quantum state study of the long-range antiferromagnetic Ising chain—0
Neural network quantum state with proximal optimization: a ground-state searching scheme based on variational Monte Carlo—0
Neural network wave functions and the sign problem—0
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