Papers Variational Monte Carlo
“Variational Monte Carlo” 태그가 달린 논문 55편 · 필터 해제
DeepQuark: deep-neural-network approach to multiquark bound states
For the first time, we implement the deep-neural-network-based variational Monte Carlo approach for the multiquark bound states, whose complexity surpasses that of electron or nucleon systems due to strong SU(3) color in…
Variational Monte CarloHyperbolic recurrent neural network as the first type of non-Euclidean neural quantum state ansatz
In this work, we introduce the first type of non-Euclidean neural quantum state (NQS) ansatz, in the form of the hyperbolic GRU (a variant of recurrent neural networks (RNNs)), to be used in the Variational Monte Carlo m…
Variational Monte CarloImproving Energy Natural Gradient Descent through Woodbury, Momentum, and Randomization
Natural gradient methods significantly accelerate the training of Physics-Informed Neural Networks (PINNs), but are often prohibitively costly. We introduce a suite of techniques to improve the accuracy and efficiency of…
Variational Monte CarloAccurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems
We present finite-range embeddings (FiRE), a novel wave function ansatz for accurate large-scale ab-initio electronic structure calculations. Compared to contemporary neural-network wave functions, FiRE reduces the asymp…
Variational Monte CarloIs attention all you need to solve the correlated electron problem?
The attention mechanism has transformed artificial intelligence research by its ability to learn relations between objects. In this work, we explore how a many-body wavefunction ansatz constructed from a large-parameter …
AllVariational Monte CarloDiagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation
Incorporating group symmetries into neural networks has been a cornerstone of success in many AI-for-science applications. Diagonal groups of isometries, which describe the invariance under a simultaneous movement of mul…
Data AugmentationVariational Monte CarloGeneralized Lanczos method for systematic optimization of neural-network quantum states
Recently, artificial intelligence for science has made significant inroads into various fields of natural science research. In the field of quantum many-body computation, researchers have developed numerous ground state …
reinforcement-learningReinforcement LearningVariational Monte CarloRetentive Neural Quantum States: Efficient Ansätze for Ab Initio Quantum Chemistry
Neural-network quantum states (NQS) has emerged as a powerful application of quantum-inspired deep learning for variational Monte Carlo methods, offering a competitive alternative to existing techniques for identifying g…
Variational Monte CarloA Theoretical Framework for an Efficient Normalizing Flow-Based Solution to the Electronic Schrodinger Equation
A central problem in quantum mechanics involves solving the Electronic Schrodinger Equation for a molecule or material. The Variational Monte Carlo approach to this problem approximates a particular variational objective…
Point ProcessesVariational Monte CarloTransferable Neural Wavefunctions for Solids
Deep-Learning-based Variational Monte Carlo (DL-VMC) has recently emerged as a highly accurate approach for finding approximate solutions to the many-electron Schr\"odinger equation. Despite its favorable scaling with th…
Variational Monte CarloAb-initio variational wave functions for the time-dependent many-electron Schrödinger equation
Understanding the real-time evolution of many-electron quantum systems is essential for studying dynamical properties in condensed matter, quantum chemistry, and complex materials, yet it poses a significant theoretical …
Variational Monte CarloA Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions
Neural network wavefunctions optimized using the variational Monte Carlo method have been shown to produce highly accurate results for the electronic structure of atoms and small molecules, but the high cost of optimizin…
Variational Monte CarloOpen-Source Fermionic Neural Networks with Ionic Charge Initialization
Finding accurate solutions to the electronic Schr\"odinger equation plays an important role in discovering important molecular and material energies and characteristics. Consequently, solving systems with large numbers o…
Variational Monte CarloPairing-based graph neural network for simulating quantum materials
We develop a pairing-based graph neural network for simulating quantum many-body systems. Our architecture augments a BCS-type geminal wavefunction with a generalized pair amplitude parameterized by a graph neural networ…
Graph Neural NetworkVariational Monte CarloAccurate Computation of Quantum Excited States with Neural Networks
We present a variational Monte Carlo algorithm for estimating the lowest excited states of a quantum system which is a natural generalization of the estimation of ground states. The method has no free parameters and requ…
Variational Monte CarloNeural-network quantum state study of the long-range antiferromagnetic Ising chain
We investigate quantum phase transitions in the transverse field Ising chain with algebraically decaying long-range (LR) antiferromagnetic interactions using the variational Monte Carlo method with the restricted Boltzma…
Variational Monte CarloVariational optimization of the amplitude of neural-network quantum many-body ground states
Neural-network quantum states (NQSs), variationally optimized by combining traditional methods and deep learning techniques, is a new way to find quantum many-body ground states and gradually becomes a competitor of trad…
Variational Monte CarloForward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo
Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry. However, the high computational cost of existing approaches hinders their applicatio…
Efficient Neural NetworkVariational Monte CarloVariational Monte Carlo on a Budget -- Fine-tuning pre-trained Neural Wavefunctions
Obtaining accurate solutions to the Schr\"odinger equation is the key challenge in computational quantum chemistry. Deep-learning-based Variational Monte Carlo (DL-VMC) has recently outperformed conventional approaches i…
Variational Monte CarloNNQS-Transformer: an Efficient and Scalable Neural Network Quantum States Approach for Ab initio Quantum Chemistry
Neural network quantum state (NNQS) has emerged as a promising candidate for quantum many-body problems, but its practical applications are often hindered by the high cost of sampling and local energy calculation. We dev…
Variational Monte Carlo