Solving the Quantum Many-Body Problem with Artificial Neural Networks

Abstract

The challenge posed by the many-body problem in quantum physics originates from the difficulty of describing the non-trivial correlations encoded in the exponential complexity of the many-body wave function. Here we demonstrate that systematic machine learning of the wave function can reduce this complexity to a tractable computational form, for some notable cases of physical interest. We introduce a variational representation of quantum states based on artificial neural networks with variable number of hidden neurons. A reinforcement-learning scheme is then demonstrated, capable of either finding the ground-state or describing the unitary time evolution of complex interacting quantum systems. We show that this approach achieves very high accuracy in the description of equilibrium and dynamical properties of prototypical interacting spins models in both one and two dimensions, thus offering a new powerful tool to solve the quantum many-body problem.

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• Machine prediction of topological transitions in photonic crystals
• On dissipative symplectic integration with applications to gradient-based optimization
• Simulating disordered quantum systems via dense and sparse restricted Boltzmann machines
• Lee-Yang theory of criticality in interacting quantum many-body systems
• Calculating Renyi Entropies with Neural Autoregressive Quantum States
• Supermagnonic propagation in two-dimensional antiferromagnets
• Machine-learning assisted quantum control in random environment
• Long-Range Entangled-Plaquette States for Critical and Frustrated Quantum Systems on a Lattice
• Machine learning nonequilibrium electron forces for adiabatic spin dynamics
• Deep learning analysis of polaritonic waves images
• Real-time calibration of coherent-state receivers: learning by trial and error
• Simulating prethermalization using near-term quantum computers
• Neural Network Evolution Strategy for Solving Quantum Sign Structures
• A scalable hardware and software control apparatus for experiments with hybrid quantum systems
• A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Equations
• Adaptive Quantum State Tomography with Active Learning
• Ground state phases of the two-dimension electron gas with a unified variational approach
• On the stability of the infinite Projected Entangled Pair Operator ansatz for driven-dissipative 2D lattices
• Unconventional dual 1D-2D quantum spin liquid revealed by $ab$ $initio$ studies on organic solids family
• Applications of deep learning to relativistic hydrodynamics
• A duplication-free quantum neural network for universal approximation
• Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation
• Neural network state estimation for full quantum state tomography
• Phase diagram of the $J$-$J_d$ Heisenberg Model on the Maple-Leaf Lattice: Neural networks and density matrix renormalization group
• Hierarchy of energy scales in an O(3) symmetric antiferromagnetic quantum critical metal: a Monte Carlo study
• A machine learning approach to the Berezinskii-Kosterlitz-Thouless transition in classical and quantum models
• Variational classical networks for dynamics in interacting quantum matter
• Enhanced Quantum Synchronization via Quantum Machine Learning
• Designing quantum many-body matter with conditional generative adversarial networks
• MIM: A deep mixed residual method for solving high-order partial differential equations
• Mixed State Entanglement Classification using Artificial Neural Networks
• Quantum speed limit for complex dynamics
• Neural network approach to quasiparticle dispersions in doped antiferromagnets
• Active Learning Algorithm for Computational Physics
• Variational quantum dynamics of two-dimensional rotor models
• Many-body calculations for periodic materials via quantum machine learning
• Machine-Learned Phase Diagrams of Generalized Kitaev Honeycomb Magnets
• Restricted Boltzmann Machines and Matrix Product States of 1D Translational Invariant Stabilizer Codes
• Superconductivity studied by solving ab initio low-energy effective Hamiltonians for carrier doped CaCuO${}_2$, Bi${}_2$Sr${}_2$CuO${}_6$, Bi${}_2$Sr${}_2$CaCu${}_2$O${}_8$, and HgBa${}_2$CuO${}_4$
• Highly resolved spectral functions of two-dimensional systems with neural quantum states
• Certification of quantum states with hidden structure of their bitstrings
• A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions
• Machine learning on quantum experimental data toward solving quantum many-body problems
• Quantum quench dynamics in the transverse-field Ising model: A numerical expansion in linked rectangular clusters
• Optimal Thermometers with Spin Networks
• Lee-Yang theory of the two-dimensional quantum Ising model
• Neural-network approach for identifying nonclassicality from click-counting data
• Tensor Networks for Lattice Gauge Theories beyond one dimension: a Roadmap
• Phase diagram of quantum generalized Potts-Hopfield neural networks
• Fault-tolerant quantum algorithms for quantum molecular systems: A survey
• Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression
• Electronic Structure Calculations using Quantum Computing
• Chebyshev expansion of spectral functions using restricted Boltzmann machines
• Machine learning effective models for quantum systems
• A semigroup method for high dimensional elliptic PDEs and eigenvalue problems based on neural networks
• Variational Neural-Network Ansatz for Continuum Quantum Field Theory
• Variational solutions to fermion-to-qubit mappings in two spatial dimensions
• DiracSolver: a tool for solving the Dirac Equation
• Entanglement Features of Random Neural Network Quantum States
• Machine learning for excitation energy transfer dynamics
• Neural Network Operations and Susuki-Trotter evolution of Neural Network States
• Natural evolution strategies and variational Monte Carlo
• Efficient representation of long-range interactions in tensor network algorithms
• Entangled Quantum Dynamics of Many-Body Systems using Bohmian Trajectories
• QuantumToolbox.jl: An efficient Julia framework for simulating open quantum systems
• Foundation Neural-Networks Quantum States as a Unified Ansatz for Multiple Hamiltonians
• Can neural quantum states learn volume-law ground states?
• Gaussian Process States: A data-driven representation of quantum many-body physics
• Distinguishing an Anderson Insulator from a Many-Body Localized phase through space-time snapshots with Neural Networks
• jVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration
• Transforming Generalized Ising Model into Boltzmann Machine
• Doped stabilizer states in many-body physics and where to find them
• Spectral Density Classification For Environment Spectroscopy
• Ground state phase diagram of the one-dimensional Bose-Hubbard model from restricted Boltzmann machines
• Improved Optimization for the Neural-network Quantum States and Tests on the Chromium Dimer
• Atomic Quantum Technologies for Quantum Matter and Fundamental Physics Applications
• Speeding up the ab initio diffusion Monte Carlo by a smart lattice regularization
• Local minima in quantum systems
• Neural Network flows of low q-state Potts and clock Models
• Neural network backflow for ab-initio quantum chemistry
• Ground and Excited States from Ensemble Variational Principles
• The Quantum House Of Cards
• Learning epidemic threshold in complex networks by Convolutional Neural Network
• Data-Driven Dynamical Mean-Field Theory: an error-correction approach to solve the quantum many-body problem using machine learning
• Neural Monte Carlo Renormalization Group
• Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
• Physics-Coupled Neural Network Magnetic Resonance Electrical Property Tomography (MREPT) for Conductivity Reconstruction
• Avoiding local minima in Variational Quantum Algorithms with Neural Networks
• Variational Quantum-Neural Hybrid Error Mitigation
• Continuous-mixture Autoregressive Networks for efficient variational calculation of many-body systems
• Quantum State Tomography with Conditional Generative Adversarial Networks
• A learning by confusion approach to characterize phase transitions
• Towards Perturbation Theory Methods on a Quantum Computer
• Boltzmann machines as two-dimensional tensor networks
• Correlation-pattern-based Continuous-variable Entanglement Detection through Neural Networks
• Neural Projected Quantum Dynamics: a systematic study
• Quantum Gradient Algorithm for General Polynomials
• A Non-stochastic Optimization Algorithm for Neural-network Quantum States
• Unifying view of fermionic neural network quantum states: From neural network backflow to hidden fermion determinant states
• Magnetic gradient free two axis control of a valley spin qubit in SiGe
• Neural network enhanced measurement efficiency for molecular groundstates
• Classifying surface probe images in strongly correlated electronic systems via machine learning
• Mapping out phase diagrams with generative classifiers
• $2^{1296}$ Exponentially Complex Quantum Many-Body Simulation via Scalable Deep Learning Method
• Time-dependent atomic magnetometry with a recurrent neural network
• The sounds of science: a symphony for many instruments and voices
• Machine-learning detection of the Berezinskii-Kosterlitz-Thouless transition and the second-order phase transition in the XXZ models
• Solving the nuclear pairing model with neural network quantum states
• Reducing the Quantum Many-electron Problem to Two Electrons with Machine Learning
• Ground state search by local and sequential updates of neural network quantum states
• Extracting many-particle entanglement entropy from observables using supervised machine learning
• Matrix Product States with Backflow correlations
• Convolutional restricted Boltzmann machine aided Monte Carlo: An application to Ising and Kitaev models
• Quantum computing with classical bits
• Mean-Field Density Matrix Decompositions
• Neural Wave Functions for Superfluids
• Is attention all you need to solve the correlated electron problem?
• Reconstruction of classical skyrmions from Anderson towers: quantum Darwinism in action
• Tensor-network-assisted variational quantum algorithm
• Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics
• Machine learning generated configurations in presence of a conserved quantity: a cautionary tale
• Data-driven methods for diffusivity prediction in nuclear fuels
• Unsupervised Learning Eigenstate Phases of Matter
• Variational optimization of the amplitude of neural-network quantum many-body ground states
• Forecasting long-time dynamics in quantum many-body systems by dynamic mode decomposition
• Supplementing Recurrent Neural Networks with Annealing to Solve Combinatorial Optimization Problems
• Spin-1/2 kagome Heisenberg antiferromagnet: Machine learning discovery of the spinon pair density wave ground state
• $\require{mhchem}$Quantum paramagnetism in the decorated square-kagome antiferromagnet $\ce{Na6Cu7BiO4(PO4)4Cl3}$
• The quantum Gaussian process state: A kernel-inspired state with quantum support data
• Cavity engineering of Hubbard $U$ via phonon polaritons
• Three Learning Stages and Accuracy-Efficiency Tradeoff of Restricted Boltzmann Machines
• Computation of forces and stresses in solids: Towards accurate structural optimization with auxiliary-field quantum Monte Carlo
• Experimental sample-efficient quantum state tomography via parallel measurements
• Certifying ground-state properties of quantum many-body systems
• Comment on "Can Neural Quantum States Learn Volume-Law Ground States?"
• Neural-Shadow Quantum State Tomography
• Kondo QED: The Kondo effect and photon trapping in a two-impurity Anderson model ultra-strongly coupled to light
• Wave function network description and Kolmogorov complexity of quantum many-body systems
• Neural networks for quantum state tomography with constrained measurements
• Operatorial formulation of the ghost rotationally-invariant slave-Boson theory
• Learning Effective Spin Hamiltonian of Quantum Magnet
• Adding machine learning within Hamiltonians: Renormalization group transformations, symmetry breaking and restoration
• Machine learning phases and criticalities without using real data for training
• Optical lattice experiments at unobserved conditions and scales through generative adversarial deep learning
• Multi-faceted machine learning of competing orders in disordered interacting systems
• Stochastic Replica Voting Machine Prediction of Stable Cubic and Double Perovskite Materials and Binary Alloys
• Accelerated Continuous time quantum Monte Carlo method with Machine Learning
• Learning Order Parameters from Videos of Dynamical Phases for Skyrmions with Neural Networks
• Real-time Dynamics of the Schwinger Model as an Open Quantum System with Neural Density Operators
• Deep recurrent networks predicting the gap evolution in adiabatic quantum computing
• Neural-network quantum states for a two-leg Bose-Hubbard ladder under magnetic flux
• A framework for efficient ab initio electronic structure with Gaussian Process States
• Learning ground states of gapped quantum Hamiltonians with Kernel Methods
• Quantum Process Identification: A Method for Characterizing Non-Markovian Quantum Dynamics
• Artificial-intelligence-based surrogate solution of dissipative quantum dynamics: physics-informed reconstruction of the universal propagator
• Estimating the Euclidean quantum propagator with deep generative modeling of Feynman paths
• Spiking neuromorphic chip learns entangled quantum states
• Quantum Next Generation Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics
• Bayesian Optimization of Bose-Einstein Condensates
• Transformer neural networks and quantum simulators: a hybrid approach for simulating strongly correlated systems
• Deep learning of many-body observables and quantum information scrambling
• Sample-efficient estimation of entanglement entropy through supervised learning
• Machine learning of XY model on a spherical Fibonacci lattice
• Classification and reconstruction of optical quantum states with deep neural networks
• Confinement in non-Abelian lattice gauge theory via persistent homology
• Autoregressive neural Slater-Jastrow ansatz for variational Monte Carlo simulation
• Entanglement-induced provable and robust quantum learning advantages
• Prospects of reinforcement learning for the simultaneous damping of many mechanical modes
• On the descriptive power of Neural-Networks as constrained Tensor Networks with exponentially large bond dimension
• Entanglement Area Law for Shallow and Deep Quantum Neural Network States
• Adaptive variational low-rank dynamics for open quantum systems
• Machine Learning for Estimation and Control of Quantum Systems
• Pruning a restricted Boltzmann machine for quantum state reconstruction
• Neural networks in quantum many-body physics: a hands-on tutorial
• Scalable variational Monte Carlo with graph neural ansatz
• Adaptive projected variational quantum dynamics
• Implementation of quantum stochastic walks for function approximation, two-dimensional data classification, and sequence classification
• Determination of impact parameter in high-energy heavy-ion collisions via deep learning
• Taming Landau level mixing in fractional quantum Hall states with deep learning
• Deep learning Local Reduced Density Matrices for Many-body Hamiltonian Estimation
• Learning entanglement breakdown as a phase transition by confusion
• The percolating cluster is invisible to image recognition with deep learning
• Highly Accurate Real-space Electron Densities with Neural Networks
• A Bayesian Inference Framework for Compression and Prediction of Quantum States
• Thermometry of one-dimensional Bose gases with neural networks
• Scalable Imaginary Time Evolution with Neural Network Quantum States
• Circuit Complexity through phase transitions: consequences in quantum state preparation
• Machine-learning approach to finite-size effects in systems with strongly interacting fermions
• Statistical learning of engineered topological phases in the kagome superlattice of AV${}_3$Sb${}_5$
• Machine Learning Enabled Lineshape Analysis in Optical Two-Dimensional Coherent Spectroscopy
• A Heavy-Fermion Zn-deficient CaBe2Ge2-Type Phase with Rare Ce-based Ferromagnetism and Large Magnetoresistance
• Continuous-time dynamics and error scaling of noisy highly-entangling quantum circuits
• A structural optimization algorithm with stochastic forces and stresses
• Many-Qudit representation for the Travelling Salesman Problem Optimisation
• Certificates of quantum many-body properties assisted by machine learning
• Hybrid Tree Tensor Networks for quantum simulation
• Density-Matrix Renormalization Group for Continuous Quantum Systems
• Compact Neural-network Quantum State representations of Jastrow and Stabilizer states
• Sampling scheme for neuromorphic simulation of entangled quantum systems
• Tunneling in projective quantum Monte Carlo simulations with guiding wave functions
• The unbearable lightness of Restricted Boltzmann Machines: Theoretical Insights and Biological Applications
• Solving Optical Tomography with Deep Learning
• Deep reinforcement learning for preparation of thermal and prethermal quantum states
• Extending the reach of quantum computing for materials science with machine learning potentials
• Fine-tuning Neural Network Quantum States
• Reduced basis surrogates for quantum spin systems based on tensor networks
• Observing a topological phase transition with deep neural networks from experimental images of ultracold atoms
• Ground State Properties of Quantum Skyrmions described by Neural Network Quantum States
• Generation of Bose-Einstein Condensates' Ground State Through Machine Learning
• Deconstructing experimental decay energy spectra: the ${}^{26}$O case
• Accurate neural quantum states for interacting lattice bosons
• Learning the Fuzzy Phases of Small Photonic Condensates
• Autoregressive neural quantum states of Fermi Hubbard models
• Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks
• Nonequilibrium fluctuations of a driven quantum heat engine via machine learning
• Electric Polarization from Many-Body Neural Network Ansatz
• Enhanced quantum state preparation via stochastic prediction of neural network
• Improving the performance of quantum approximate optimization for preparing non-trivial quantum states without translational symmetry
• Gaussian-state Ansatz for the non-equilibrium dynamics of quantum spin lattices
• Empirical Sample Complexity of Neural Network Mixed State Reconstruction
• Efficient neural-network based variational Monte Carlo scheme for direct optimization of excited energy states in frustrated quantum systems
• Impact of conditional modelling for a universal autoregressive quantum state
• Explainable Natural Language Processing with Matrix Product States
• Are queries and keys always relevant? A case study on Transformer wave functions
• Study of phase transition of Potts model with Domain Adversarial Neural Network
• Error-rate-agnostic decoding of topological stabilizer codes
• Policy-guided Monte Carlo on general state spaces: Application to glass-forming mixtures
• Exponentially improved efficient machine learning for quantum many-body states with provable guarantees
• Training Quantum Boltzmann Machines with the $\beta$-Variational Quantum Eigensolver
• Emulating Quantum Interference with Generalized Ising Machines
• Deep learning lattice gauge theories
• Phase diagram reconstruction of the Bose-Hubbard model with a Restricted Boltzmann Machine wavefunction
• The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems
• Identifying nonclassicality from experimental data using artificial neural networks
• Using a Feedback-Based Quantum Algorithm to Analyze the Critical Properties of the ANNNI Model Without Classical Optimization
• Deep Neural Networks as Variational Solutions for Correlated Open Quantum Systems
• Bayesian machine scientist to compare data collapses for the Nikuradse dataset
• Learning Dynamic Boltzmann Distributions as Reduced Models of Spatial Chemical Kinetics
• Adversarial Machine Learning Phases of Matter
• Machine learning algorithms based on generalized Gibbs ensembles
• Partially Unitary Learning
• Neural-network Quantum States for Spin-1 systems: spin-basis and parameterization effects on compactness of representations
• Random Sampling Neural Network for Quantum Many-Body Problems
• Statistical-mechanical study of deep Boltzmann machine given weight parameters after training by singular value decomposition
• Many-body quantum sign structures as non-glassy Ising models
• Stabilizer ground states for simulating quantum many-body physics: theory, algorithms, and applications
• Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging
• Machine-learning-inspired quantum optimal control of nonadiabatic geometric quantum computation via reverse engineering
• An RNN-policy gradient approach for quantum architecture search
• Infinite Neural Network Quantum States: Entanglement and Training Dynamics
• Machine learning as an improved estimator for magnetization curve and spin gap
• Generative learning for the problem of critical slowing down in lattice Gross Neveu model
• Probing Criticality in Quantum Spin Chains with Neural Networks
• Invariant Neural Network Ansatz for weakly symmetric Open Quantum Lattices
• Approximation Theory of Tree Tensor Networks: Tensorized Univariate Functions -- Part I
• Approximation Theory of Tree Tensor Networks: Tensorized Univariate Functions -- Part II
• Neural-network-supported basis optimizer for the configuration interaction problem in quantum many-body clusters: Feasibility study and numerical proof
• Efficient Solutions of Fermionic Systems using Artificial Neural Networks
• Machine learning phases of active matter
• Solving Schrodinger equations using physically constrained neural network
• Machine learning method to determine concentrations of structural defects in irradiated materials
• Controlling magnetic frustration in 1T-TaS${}_2$ via Coulomb engineered long-range interactions
• Transformer Wave Function for Quantum Long-Range models
• Machine learning of phases and structures for model systems in physics
• The Discrete Langevin Machine: Bridging the Gap Between Thermodynamic and Neuromorphic Systems
• On the Representation of Solutions to Elliptic PDEs in Barron Spaces
• Amplitude Ratios and Neural Network Quantum States
• Noise prediction and reduction of single electron spin by deep-learning-enhanced feedforward control
• Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks
• Quantum Transport in Open Spin Chains using Neural-Network Quantum States
• Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning
• Evolving Quantum Circuits
• Neural network enhanced hybrid quantum many-body dynamical distributions
• Lee-Yang theory of quantum phase transitions with neural network quantum states
• Thresholds of descending algorithms in inference problems
• Sampling U(1) gauge theory using a re-trainable conditional flow-based model
• Automatic Order Detection and Restoration Through Systematically Improvable Variational Wave Functions
• Generative Neural Samplers for the Quantum Heisenberg Chain
• Gradient-descent methods for fast quantum state tomography
• Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water-methane dimer
• Equivariant Variational Quantum Eigensolver to detect Phase Transitions through Energy Level Crossings
• Rayleigh-Gauss-Newton optimization with enhanced sampling for variational Monte Carlo
• Computational prediction of RNA tertiary structures using machine learning methods
• Unsupervised interpretable learning of topological indices invariant under permutations of atomic bands
• Free Energy Evaluation Using Marginalized Annealed Importance Sampling
• Neural Network Quantum States analysis of the Shastry-Sutherland model
• Quantum algorithms for Schrieffer-Wolff transformation
• Investigating Network Parameters in Neural-Network Quantum States
• Efficient and quantum-adaptive machine learning with fermion neural networks
• Two-dopant origin of competing stripe and pair formation in Hubbard and $t$-$J$ models
• Inferring Markovian quantum master equations of few-body observables in interacting spin chains
• Multiparameter estimation of continuous-time Quantum Walk Hamiltonians through Machine Learning
• Predicting quantum many-body dynamics with transferable neural networks
• Quantum Rényi entropy by optimal thermodynamic integration paths
• A deep neural network approach to solve the Dirac equation
• Sampling asymmetric open quantum systems for artificial neural networks
• Noise-induced network topologies
• New trial wave function for nuclear cluster structure of nuclei
• Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice
• Simplicity of mean-field theories in neural quantum states
• Classical Quantum Optimization with Neural Network Quantum States
• Reinforcement Learning Approach to Shortcuts between Thermodynamic States with Extra Constraints
• Active quantum flocks
• Second-order optimisation strategies for neural network quantum states
• Machine Learning approach to muon spectroscopy analysis
• Performance of the rigorous renormalization group for first order phase transitions and topological phases
• Learning by Confusion: The Phase Diagram of the Holstein Model
• Network-Initialized Monte Carlo Based on Generative Neural Networks
• Efficiency of neural quantum states in light of the quantum geometric tensor
• Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe
• A simple framework for contrastive learning phases of matter
• Approximately-symmetric neural networks for quantum spin liquids
• Entropy, Free Energy, and Work of Restricted Boltzmann Machines
• Maximising Quantum-Computing Expressive Power through Randomised Circuits
• Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks
• Restricted Boltzmann machine representation for the groundstate and excited states of Kitaev Honeycomb model
• Towards quantum gravity with neural networks: Solving the quantum Hamilton constraint of U(1) BF theory
• Minimization of ion micromotion with artificial neural network
• Predicting Topological Entanglement Entropy in a Rydberg analog simulator
• Self-Supervised Learning of Generative Spin-Glasses with Normalizing Flows
• Supervised and unsupervised learning of the many-body critical phase, phase transitions, and critical exponents in disordered quantum systems
• Deep Learning Hamiltonians from Disordered Image Data in Quantum Materials
• Ground-state properties via machine learning quantum constraints
• Characterization of a driven two-level quantum system by Supervised Learning
• Spin-qubit noise spectroscopy from randomized benchmarking by supervised learning
• Quantum many-body solver using artificial neural networks and its applications to strongly correlated electron systems
• Quantum computing quantum Monte Carlo algorithm
• Quantum System Compression: A Hamiltonian Guided Walk Through Hilbert Space
• Simulation of Charge Stability Diagrams for Automated Tuning Solutions (SimCATS)
• Trade-off between Gradient Measurement Efficiency and Expressivity in Deep Quantum Neural Networks
• Observation of non-Fermi liquid physics in a quantum critical metal via quantum loop topography
• Learning quantum dissipation by the neural ordinary differential equation
• Machine learning one-dimensional spinless trapped fermionic systems with neural-network quantum states
• Thermodynamics of the Ising model encoded in restricted Boltzmann machines
• Experimentally detecting a quantum change point via Bayesian inference
• Neural network assisted quantum state and process tomography using limited data sets
• Supervised Training of Neural-Network Quantum States for the Next Nearest Neighbor Ising model
• Liouville Space Neural Network Representation of Density Matrices
• Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters
• Boltzmann machines and quantum many-body problems
• Data-driven discovery of self-similarity using neural networks
• Typicality at quantum-critical points
• Dual-Capability Machine Learning Models for Quantum Hamiltonian Parameter Estimation and Dynamics Prediction
• DeepQuark: A Deep-Neural-Network Approach to Multiquark Bound States
• A Neural Network Perturbation Theory Based on the Born Series
• Real-time dynamics of 1D and 2D bosonic quantum matter deep in the many-body localized phase
• Analysis of the buildup of spatiotemporal correlations and their bounds outside of the light cone
• Time-dependent Neural Galerkin Method for Quantum Dynamics
• Learning Moment Closure in Reaction-Diffusion Systems with Spatial Dynamic Boltzmann Distributions
• All you need is spin: SU(2) equivariant variational quantum circuits based on spin networks
• Transfer learning from Hermitian to non-Hermitian quantum many-body physics
• Energy minimization of paired composite fermion wave functions in the spherical geometry
• Automatic Learning of Topological Phase Boundaries
• Non-stabilizerness of Neural Quantum States
• High-dimentional Multipartite Entanglement Structure Detection with Low Cost
• Beyond single-reference fixed-node approximation in ab initio Diffusion Monte Carlo using antisymmetrized geminal power applied to systems with hundreds of electrons
• Correlated states in super-moiré materials with a kernel polynomial quantics tensor cross interpolation algorithm
• Fast reconstruction of single-shot wide-angle diffraction images through deep learning
• Specialising Neural-network Quantum States for the Bose Hubbard Model
• Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations
• Neural-network Quantum State of Transverse-field Ising Model
• Neural network representation of quantum systems
• Exact block encoding of imaginary time evolution with universal quantum neural networks
• Hamiltonian Learning using Machine Learning Models Trained with Continuous Measurements
• Variational Optimization for Quantum Problems using Deep Generative Networks
• Exact Quantum Algorithms for Quantum Phase Recognition: Renormalization Group and Error Correction
• Towards quantum gravity with neural networks: Solving quantum Hamilton constraints of 3d Euclidean gravity in the weak coupling limit
• How machine learning conquers the unitary limit
• Machine-learning-based methods for output only structural modal identification
• Neural Network Approach to Scaling Analysis of Critical Phenomena
• $\beta$-Variational Autoencoder as an Entanglement Classifier
• Emulating quantum computation with artificial neural networks
• SOLAX: A Python solver for fermionic quantum systems with neural network support
• Variational Monte Carlo Approach to Partial Differential Equations with Neural Networks
• Probing transport in quantum many-fermion simulations via quantum loop topography
• Variational Monte Carlo with Neural Network Quantum States for Yang-Mills Matrix Model
• Machine Learning Quantum Systems with Magnetic p-bits
• Berezinskii-Kosterlitz-Thouless transition from Neural Network Flows
• Shifting sands of hardware and software in exascale quantum mechanical simulations
• Deep learning extraction of band structure parameters from density of states: a case study on trilayer graphene
• Deep learning of topological phase transitions from entanglement aspects for two-dimensional chiral p-wave superconductors
• A sampling-guided unsupervised learning method to capture percolation in complex networks
• Robopheus: A Virtual-Physical Interactive Mobile Robotic Testbed
• The Future of the Correlated Electron Problem
• State Classification via a Random-Walk-Based Quantum Neural Network
• Machine learning classification of two-dimensional vortex configurations
• Variational Neural and Tensor Network Approximations of Thermal States
• Entanglement transitions from restricted Boltzmann machines
• Performance of machine-learning-assisted Monte Carlo in sampling from simple statistical physics models
• Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets
• Quantum-assisted variational Monte Carlo
• Multifunctional Meta-Optic Systems: Inversely Designed with Artificial Intelligence
• Relationship between the ground-state wave function of a magnet and its static structure factor
• Inferring Hidden Symmetries of Exotic Magnets from Detecting Explicit Order Parameters
• Coarse-grained spectral projection (CGSP): a deep learning-assisted approach to quantum unitary dynamics
• Broken-Symmetry Ground States of the Heisenberg model on the Pyrochlore Lattice
• Machine Learning Wavefunction
• Exploring explicit coarse-grained structure in artificial neural networks
• Quantum information criteria for model selection in quantum state estimation
• Persistent Homology for Structural Characterization in Disordered Systems
• Simulating quantum circuits using the multi-scale entanglement renormalization ansatz
• Time-dependent variational principle for open quantum systems with artificial neural networks
• Principal Component Analysis of Diffuse Magnetic Scattering: a Theoretical Study
• Renormalization-group-inspired neural networks for computing topological invariants
• Investigation of bi-particle states in gate-array-controlled quantum-dot systems aided by machine learning techniques
• Multiqubit state learning with entangling quantum generative adversarial networks
• Estimation of the geometric measure of entanglement with Wehrl Moments through Artificial Neural Networks
• Machine learning the deuteron: new architectures and uncertainty quantification
• Learning of error statistics for the detection of quantum phases
• Sample generation for the spin-fermion model using neural networks
• Efficient Characterization of Quantum Evolutions via a Recommender System
• Effects of electron-electron interactions in the Yu-Shiba-Rusinov lattice model
• Preparing Quantum States by Measurement-feedback Control with Bayesian Optimization
• Synergy between deep neural networks and the variational Monte Carlo method for small ${}^{4}H{e}_N$ clusters
• Fluctuation based interpretable analysis scheme for quantum many-body snapshots
• Phase classification in the long-range Harper model using machine learning
• Neural-network quantum state study of the long-range antiferromagnetic Ising chain
• Resource-efficient Generalized Quantum Subspace Expansion
• A Data-Driven Machine Learning Approach for Electron-Molecule Ionization Cross Sections
• Quantum state tomography with disentanglement algorithm
• Noncoplanar and chiral spin states on the way towards Néel ordering in fullerene Heisenberg models
• Comparative study on compact quantum circuits of hybrid quantum-classical algorithms for quantum impurity models
• Building imaginary-time thermal field theory with artificial neural networks
• Hidden self-energies as origin of cuprate superconductivity revealed by machine learning
• Neural Quantum State Study of Fracton Models
• Zero-temperature Monte Carlo simulations of two-dimensional quantum spin glasses guided by neural network states
• A new strategy for directly calculating the minimum eigenvector of matrices without diagonalization
• Machine learning for predicting control landscape maps of quantum molecular dynamics: Laser-induced three-dimensional alignment of asymmetric top molecules
• Efficient Optimization of Variational Autoregressive Networks with Natural Gradient
• Quantum Machine Learning of Molecular Energies with Hybrid Quantum-Neural Wavefunction
• First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models
• Deep quantum Monte Carlo approach for polaritonic chemistry
• Hamiltonian Learning of Triplon Excitations in an Artificial Nanoscale Molecular Quantum Magnet
• Machine learning, quantum chaos, and pseudorandom evolution
• Design principles of deep translationally-symmetric neural quantum states for frustrated magnets
• Spin-glass quantum phase transition in amorphous arrays of Rydberg atoms
• Efficient optimization and conceptual barriers in variational finite Projected Entangled-Pair States
• Variational Quantum Imaginary Time Evolution for Matrix Product State Ansatz with Tests on Transcorrelated Hamiltonians
• Sampling Problems on a Quantum Computer
• Deep Reinforcement Learning with Quantum-inspired Experience Replay
• Bidirectional information flow quantum state tomography
• Random Batch Algorithms for Quantum Monte Carlo simulations
• Effective classical correspondence of the Mott transition
• Weighted Quantum Channel Compiling through Proximal Policy Optimization
• Simulating methylamine using symmetry adapted qubit-excitation-based variational quantum eigensolver
• Many-body mobility edges in 1D and 2D revealed by convolutional neural networks
• Machine-learning-inspired quantum control in many-body dynamics
• Dimension truncation for open quantum systems in terms of tensor networks
• Quantum circuit complexity and unsupervised machine learning of topological order
• Experimental demonstration of reconstructing quantum states with generative models
• Simple Fermionic backflow states via a systematically improvable tensor decomposition
• Fermi Machine -- Quantum Many-Body Solver Derived from Correspondence between Noninteracting and Strongly Correlated Fermions
• Deep Learning Super-Diffusion in Multiplex Networks
• Efficient Learning of Long-Range and Equivariant Quantum Systems
• NNQS-AFQMC: Neural network quantum states enhanced fermionic quantum Monte Carlo
• Deep learning based inverse method for layout design
• Exploring entanglement in finite-size quantum systems with degenerate ground state
• Mott Transition and Volume Law Entanglement with Neural Quantum States
• Structure-Driven Prediction of Magnetic Order in Uranium Compounds
• Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy
• Generalized Probabilistic Approximate Optimization Algorithm
• Phase diagram and crystal melting of helium-4 in two dimensions
• Understanding and eliminating spurious modes in variational Monte Carlo using collective variables
• Quantifying High-Order Interdependencies in Entangled Quantum States
• Supervised learning of an interacting 2D hard-core boson model of a weak topological insulator using correlation functions
• Revisiting the dynamics of Bose-Einstein condensates in a double well by deep learning with a hybrid network
• Classifying topological neural network quantum states via diffusion maps
• Model-Driven Engineering for Quantum Programming: A Case Study on Ground State Energy Calculation
• Deep Neural Network-assisted improvement of quantum compressed sensing tomography
• Partial suppression of magnetism in the square lattice SU(3) Hubbard model
• Physics-informed Transformers for Electronic Quantum States
• Establishing simple relationship between eigenvector and matrix elements
• Efficiency of the hidden fermion determinant states Ansatz in the light of different complexity measures
• Solving Fermi-Hubbard-type Models by Tensor Representations of Backflow Corrections
• Effective temperature in approximate quantum many-body states
• Machine learning and serving of discrete field theories -- when artificial intelligence meets the discrete universe
• Locating quantum critical points with shallow quantum circuits
• Classical fracton spin liquid and Hilbert space fragmentation in a 2D spin-$1/2$ model
• Quantum control without quantum states
• Recoverability from direct quantum correlations
• Learning phase transitions by siamese neural network
• Learning a quantum computer's capability
• Single-shot quantum measurements sketch quantum many-body states
• Boltzmann machines as thermal models for quantum systems
• Simulating quantum dynamics: Evolution of algorithms in the HPC context
• Improving neural network performance for solving quantum sign structure
• The deep learning and statistical physics applications to the problems of combinatorial optimization
• Bound on entanglement in neural quantum states
• Universal Performance Gap of Neural Quantum States Applied to the Hofstadter-Bose-Hubbard Model
• Modal Backflow Neural Quantum States for Anharmonic Vibrational Calculations
• Physics-enhanced neural networks for equation-of-state calculations
• Simulating dynamics of the two-dimensional transverse-field Ising model: a comparative study of large-scale classical numerics
• Quantum Machine Learning For Classical Data
• Topological Order in Neural Wavefunctions
• Engineering large end-to-end correlations in finite fermionic chains
• Discovering quasiorder parameters in the Potts model: A bridge between machine learning and critical phenomena
• Expressivity of determinantal ansatzes for neural network wave functions
• Convergence of variational Monte Carlo simulation and scale-invariant pre-training
• Efficiency of neural-network state representations of one-dimensional quantum spin systems
• Seeding neural network quantum states with tensor network states
• Explaining the effects of non-convergent sampling in the training of Energy-Based Models
• Many-Body Neural Network Wavefunction for a Non-Hermitian Ising Chain
• Quantum-Inspired Tempering for Ground State Approximation using Artificial Neural Networks
• Finding the Dynamics of an Integrable Quantum Many-Body System via Machine Learning
• Entanglement Clustering for ground-stateable quantum many-body states
• An Empirical Study of Quantum Dynamics as a Ground State Problem with Neural Quantum States
• Learning quantum symmetries with interactive quantum-classical variational algorithms
• Variational Transformer Ansatz for the Density Operator of Steady States in Dissipative Quantum Many-Body Systems
• Learning topological defects formation with neural networks in a quantum phase transition
• Composite Spatial Monte Carlo Integration Based on Generalized Least Squares
• Neuralized Fermionic Tensor Networks for Quantum Many-Body Systems
• Investigating Stark many-body localization with continuous unitary transformation flows
• Accuracy of Restricted Boltzmann Machines for the one-dimensional $J_1-J_2$ Heisenberg model
• Predicting Quantum Potentials by Deep Neural Network and Metropolis Sampling
• Gaussian Processes for Finite Size Extrapolation of Many-Body Simulations
• Nonequilibrium thermodynamics of self-supervised learning
• The neural networks with tensor weights and emergent fermionic Wick rules in the large-width limit
• Unsupervised Learning of Symmetry Protected Topological Phase Transitions
• Mapping Phase Diagrams of Quantum Spin Systems through Semidefinite-Programming Relaxations
• Machine Learning for Discovering Effective Interaction Kernels between Celestial Bodies from Ephemerides
• An artificial neural network approximation for Cauchy inverse problems
• ORQVIZ: Visualizing High-Dimensional Landscapes in Variational Quantum Algorithms
• Meta Variational Monte Carlo
• Approximating Ground State Energies and Wave Functions of Physical Systems with Neural Networks
• Classical restrictions of generic matrix product states are quasi-locally Gibbsian
• Ground States of Quantum Many Body Lattice Models via Reinforcement Learning
• Local density matrices of many-body states in the constant weight subspaces
• Time-dependent Schwinger boson mean-field theory of supermagnonic propagation in 2D antiferromagnets
• A universal neural network for learning phases and criticalities
• Correcting and extending Trotterized quantum many-body dynamics
• Machine-learning semi-local density functional theory for many-body lattice models at zero and finite temperature
• Kinetic samplers for neural quantum states
• Self-regularizing restricted Boltzmann machines
• Generalization Error Estimates of Machine Learning Methods for Solving High Dimensional Schrödinger Eigenvalue Problems
• Phase diagram of the J1-J2 Heisenberg second-order topological quantum magnet
• Model-Independent Quantum Phases Classifier
• Emergence of global receptive fields capturing multipartite quantum correlations
• Nearest-Neighbours Neural Network architecture for efficient sampling of statistical physics models
• Machine learning the single-$\Lambda$ hypernuclei with neural-network quantum states
• Representing arbitrary ground states of toric code by a restricted Boltzmann machine
• Artificial intelligence for representing and characterizing quantum systems
• A Jastrow wave function for the spin-1 Heisenberg chain: the string order revealed by the mapping to the classical Coulomb gas
• Machine learning of the Ising model on a spherical Fibonacci lattice
• Fitness landscape for quantum state tomography from neutron scattering
• Quantum dynamics evolution predicted by the long short-term memory network in the photosystem II reaction center
• Artificial versus Natural Atoms: The uncanny capability of the many-body Schrödinger equation to produce emergent behavior
• The statistical mechanics and machine learning of the $\alpha$-Rényi ensemble
• High Harmonic Generation in Two-Dimensional Mott Insulators
• Challenges and opportunities in the supervised learning of quantum circuit outputs
• Learning agent-based approach to the characterization of open quantum systems
• Ultrafast neural sampling with spiking nanolasers
• Tensorization of neural networks for improved privacy and interpretability
• Global sampling of Feynman's diagrams through Normalizing Flow
• Worldline algorithm by oracle-guided variational autoregressive network
• Generalized Lanczos method for systematic optimization of neural-network quantum states
• Variational decision diagrams for quantum-inspired machine learning applications
• Large-Angle Convergent-Beam Electron Diffraction Patterns via Conditional Generative Adversarial Networks
• Bridging the Gap between Deep Learning and Frustrated Quantum Spin System for Extreme-scale Simulations on New Generation of Sunway Supercomputer
• Learning Full Configuration Interaction Electron Correlations with Deep Learning
• Predicting sampling advantage of stochastic Ising Machines for Quantum Simulations
• Effective Method for Inverse Ising Problem under Missing Observations in Restricted Boltzmann Machines
• Exact training of Restricted Boltzmann machines on intrinsically low dimensional data
• Hyperbolic recurrent neural network as the first type of non-Euclidean neural quantum state ansatz
• Group Convolutional Neural Network for the Low-Energy Spectrum in the Quantum Dimer Model
• Comparison of D-Wave Quantum Annealing and Classical Simulated Annealing for Local Minima Determination
• Adaptive quantum dynamics with the time-dependent variational Monte Carlo method
• Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders
• Bootstrapping Flat-band Superconductors: Rigorous Lower Bounds on Superfluid Stiffness
• Hybrid between biologically and quantum-inspired many-body states
• Magnetic correlations in the $SU(3)$ triangular-lattice $t$-$J$ model at finite doping
• Quantum Machine Learning for State Tomography Using Classical Data
• Analog Circuit-QED Simulator of Quantum Spin Dynamics Through the Extended Bose-Hubbard Model
• Variational Neural Network Approach to QFT in the Field Basis
• Process Tensor Approaches to Non-Markovian Quantum Dynamics
• Comparing Symmetrized Determinant Neural Quantum States for the Hubbard Model
• Machine Learning Green's Functions of Strongly Correlated Hubbard Models
• Exploring the performance of superposition of product states: from 1D to 3D quantum spin systems
• Brute-force positivization of $J_1-J_2$ model ground states
• Basis dependence of Neural Quantum States for the Transverse Field Ising Model
• Neural quantum states for entanglement depth certification from randomized Pauli measurements
• Optimizing the dynamical preparation of quantum spin lakes on the ruby lattice
• Learning Minimal Representations of Fermionic Ground States
• High-precision ground state parameters of the two-dimensional spin-1/2 Heisenberg model on the square lattice
• Wilson loops with neural networks
• Energy gap of quantum spin glasses: a projection quantum Monte Carlo study
• The toric code under antiferromagnetic isotropic Heisenberg interactions
• Efficient emulation of nuclear ground states with neural-network variational Monte Carlo and eigenvector continuation
• Neural-network solution of subtracted three-body Faddeev integral equations near the Efimov limit
• Variational neural-network solution of the two-body ${}^{17}\mathrm{F}$ proton-halo problem with a Coulomb--Whittaker tail
• Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
• Principle of learning sign rules by neural networks in qubit lattice models
• Perturbational Decomposition Analysis for Quantum Ising Model with Weak Transverse Fields
• Efficient bit encoding of neural networks for Fock states
• Trajectory-Resolved Weiss Fields for Quantum Spin Dynamics
• Ab-initio Quantum Monte Carlo study of ultracold atomic mixtures
• Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks
• Electronic excited states in deep variational Monte Carlo
• Improving the performance of fermionic neural networks with the Slater exponential Ansatz
• Time-dependent variational Monte Carlo study of the dynamic response of bosons in an optical lattice
• On the direct diagonalization method for a few particles trapped in harmonic potentials
• Neural-Network Quantum States for Periodic Systems in Continuous Space
• Data-Driven Time Propagation of Quantum Systems with Neural Networks
• Geometry of backflow transformation ansatz for quantum many-body fermionic wavefunctions
• Overcoming barriers to scalability in variational quantum Monte Carlo
• Learning phase transitions in ferrimagnetic GdFeCo alloys
• Stoquastic ground states are classical thermal distributions
• Learning a compass spin model with neural network quantum states
• Optimized Observable Readout from Single-shot Images of Ultracold Atoms via Machine Learning
• Dynamically polarisable force-fields for surface simulations via multi-output classification Neural Networks
• Learning disentangled representation for classical models
• Fast evaluation of interaction integrals for confined systems with machine learning
• Application of Langevin Dynamics to Advance the Quantum Natural Gradient Optimization Algorithm
• Machine Learning out of equilibrium correlations in the Bose-Hubbard model
• Optimizing Temperature Distributions for Training Neural Quantum States using Parallel Tempering