Machine learning and the physical sciences

Abstract

Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. We review in a selective way the recent research on the interface between machine learning and physical sciences. This includes conceptual developments in machine learning (ML) motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross-fertilization between the two fields. After giving basic notion of machine learning methods and principles, we describe examples of how statistical physics is used to understand methods in ML. We then move to describe applications of ML methods in particle physics and cosmology, quantum many body physics, quantum computing, and chemical and material physics. We also highlight research and development into novel computing architectures aimed at accelerating ML. In each of the sections we describe recent successes as well as domain-specific methodology and challenges.

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• Classification without labels: Learning from mixed samples in high energy physics
• Machine Learning Topological States
• Neural-Network Approach to Dissipative Quantum Many-Body Dynamics
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• The Zurich Extragalactic Bayesian Redshift Analyzer (ZEBRA) and its first application: COSMOS
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• Machine Learning Topological Invariants with Neural Networks
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• Deep autoregressive models for the efficient variational simulation of many-body quantum systems
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• Symmetries and many-body excited states with neural-network quantum states
• Jet Substructure Classification in High-Energy Physics with Deep Neural Networks
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• Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines
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• A Convolutional Neural Network Neutrino Event Classifier
• Automated Search for new Quantum Experiments
• Permutationally invariant quantum tomography
• Identifying Quantum Phase Transitions using Artificial Neural Networks on Experimental Data
• Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems
• Variational neural network ansatz for steady states in open quantum systems
• Reinforcement Learning with Neural Networks for Quantum Feedback
• Parameterized Machine Learning for High-Energy Physics
• Solving Statistical Mechanics Using Variational Autoregressive Networks
• Flow-based generative models for Markov chain Monte Carlo in lattice field theory
• Learning to Predict the Cosmological Structure Formation
• Solving Many-Electron Schrödinger Equation Using Deep Neural Networks
• Approximating quantum many-body wave-functions using artificial neural networks
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• Optimal Errors and Phase Transitions in High-Dimensional Generalized Linear Models
• Localization and Glassy Dynamics Of Many-Body Quantum Systems
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• Estimating photometric redshifts with artificial neural networks
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• Neural Network Parametrization of Deep-Inelastic Structure Functions
• CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding
• Many-body localization and delocalization in large quantum chains
• Constructing neural stationary states for open quantum many-body systems
• Jet Flavor Classification in High-Energy Physics with Deep Neural Networks
• Application of Quantum Annealing to Training of Deep Neural Networks
• Backflow Transformations via Neural Networks for Quantum Many-Body Wave-Functions
• Gauge Equivariant Convolutional Networks and the Icosahedral CNN
• Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
• Sample-optimal tomography of quantum states
• Machine learning vortices at the Kosterlitz-Thouless transition
• Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
• Detection of phase transition via convolutional neural network
• Constraining the thick disc formation scenario of the Milky Way
• Machine Learning in Electronic Quantum Matter Imaging Experiments
• Mining gold from implicit models to improve likelihood-free inference
• Constraining Effective Field Theories with Machine Learning
• Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology
• Constructing exact representations of quantum many-body systems with deep neural networks
• Spherical CNNs
• Redshift distributions of galaxies in the DES Science Verification shear catalogue and implications for weak lensing
• Experimental quantum compressed sensing for a seven-qubit system
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• A Guide to Constraining Effective Field Theories with Machine Learning
• An exact mapping between the Variational Renormalization Group and Deep Learning
• Neural Network Renormalization Group
• Probing many-body localization with neural networks
• Integrating Neural Networks with a Quantum Simulator for State Reconstruction
• Latent Space Purification via Neural Density Operators
• Machine learning out-of-equilibrium phases of matter
• Machine-learning-assisted correction of correlated qubit errors in a topological code
• Entropy and mutual information in models of deep neural networks
• Energy flow polynomials: A complete linear basis for jet substructure
• Classification and Geometry of General Perceptual Manifolds
• Unsupervised Learning of Frustrated Classical Spin Models I: Principle Component Analysis
• Machine Learning Assisted Many-Body Entanglement Measurement
• Strings, Projected Entangled Pair States, and variational Monte Carlo methods
• Solving the Bose-Hubbard model with machine learning
• Emergence of Compositional Representations in Restricted Boltzmann Machines
• Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
• Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
• Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems
• The Relationship Between Local Structure and Relaxation in Out-of-Equilibrium Glassy Systems
• Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses
• Tree Tensor Networks for Generative Modeling
• Identifying Quantum Phase Transitions with Adversarial Neural Networks
• Machine Learning ${\mathbb{Z}}_2$ Quantum Spin Liquids with Quasi-particle Statistics
• The redshift evolution of massive galaxy clusters in the MACSIS simulations
• Nuisance hardened data compression for fast likelihood-free inference
• Approximating strongly correlated spin and fermion wavefunctions with correlator product states
• Decoding Small Surface Codes with Feedforward Neural Networks
• Approximate Bayesian Computation for Astronomical Model Analysis: A Case Study in Galaxy Demographics and Morphological Transformation at High Redshift
• Automatic physical inference with information maximising neural networks
• Machine Learning in the String Landscape
• Phase transitions and sample complexity in Bayes-optimal matrix factorization
• Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
• Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber
• Machine learning technique to find quantum many-body ground states of bosons on a lattice
• Learning to Classify from Impure Samples with High-Dimensional Data
• Deep Neural Network Probabilistic Decoder for Stabilizer Codes
• Machine Learning techniques for state recognition and auto-tuning in quantum dots
• Solving frustrated quantum many-particle models with convolutional neural networks
• Mean-field message-passing equations in the Hopfield model and its generalizations
• Perspective: Energy Landscapes for Machine Learning
• Ground-State Properties of Quantum Many-Body Systems: Entangled-Plaquette States and Variational Monte Carlo
• Guest Editorial: Special Topic on Data-enabled Theoretical Chemistry
• Learning Relevant Features of Data with Multi-scale Tensor Networks
• LHCb Topological Trigger Reoptimization
• Artificial Neural Network Approach to the Analytic Continuation Problem
• Fast cosmic web simulations with generative adversarial networks
• Deep Learning and AdS/CFT
• Recurrent Inference Machines for Solving Inverse Problems
• Chiral Topological Phases from Artificial Neural Networks
• Information-theoretic thresholds from the cavity method
• Trochoidal motion and pair generation in skyrmion and antiskyrmion dynamics under spin-orbit torques
• Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties
• Training A Quantum Optimizer
• Quenches near Ising quantum criticality as a challenge for artificial neural networks
• Machine Learning Many-Body Localization: Search for the Elusive Nonergodic Metal
• Quantifying hidden order out of equilibrium
• PCA meets RG
• Automated detection of galaxy-scale gravitational lenses in high resolution imaging data
• Prediction and real-time compensation of qubit decoherence via machine-learning
• cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation
• Self-Learning Monte Carlo Method in Fermion Systems
• A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters
• Phase Diagram of Restricted Boltzmann Machines and Generalised Hopfield Networks with Arbitrary Priors
• Deep Learning Topological Invariants of Band Insulators
• Neural-network Kohn-Sham exchange-correlation potential and its out-of-training transferability
• Advantages of versatile neural-network decoding for topological codes
• Learning hard quantum distributions with variational autoencoders
• Deep Learning the Ising Model Near Criticality
• Probing hidden spin order with interpretable machine learning
• Analytic continuation via domain-knowledge free machine learning
• Deep Learning and Holographic QCD
• Unifying Neural-network Quantum States and Correlator Product States via Tensor Networks
• Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks
• Neural network representation of tensor network and chiral states
• Reinforcement learning for autonomous preparation of Floquet-engineered states: Inverting the quantum Kapitza oscillator
• Learning Free Energy Landscapes Using Artificial Neural Networks
• Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
• Deep Learning the Quantum Phase Transitions in Random Electron Systems: Applications to Three Dimensions
• Distinguishing standard and modified gravity cosmologies with machine learning
• Data-Driven Reconstruction of Gravitationally Lensed Galaxies using Recurrent Inference Machines
• Efficient Representation of Topologically Ordered States with Restricted Boltzmann Machines
• Machine learning action parameters in lattice quantum chromodynamics
• N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials
• Learning multiple order parameters with interpretable machines
• Self-Learning Monte Carlo Method: Continuous-Time Algorithm
• Learning phase transitions from dynamics
• Extensive deep neural networks for transferring small scale learning to large scale systems
• Adaptive quantum computation in changing environments using projective simulation
• Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning
• Supervised learning magnetic skyrmion phases
• Symmetry Enforced Self-Learning Monte Carlo Method Applied to the Holstein Model
• A Systematic Search for High Surface Brightness Giant Arcs in a Sloan Digital Sky Survey Cluster Sample
• Quantum dynamics in transverse-field Ising models from classical networks
• Quantum phase recognition via unsupervised machine learning
• Automated discovery of characteristic features of phase transitions in many-body localization
• Matrix Product Operators for Sequence to Sequence Learning
• uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers
• Pattern recognition techniques for Boson Sampling validation
• Machine Learning of Frustrated Classical Spin Models. II. Kernel Principal Component Analysis
• Spectral Dynamics of Learning Restricted Boltzmann Machines
• Machine learning assisted readout of trapped-ion qubits
• Investigating ultrafast quantum magnetism with machine learning
• Interpretable Machine Learning Study of Many-Body Localization Transition in Disordered Quantum Ising Spin Chains
• An application of machine learning techniques to galaxy cluster mass estimation using the MACSIS simulations
• From Dark Matter to Galaxies with Convolutional Networks
• Neural-network states for the classical simulation of quantum computing
• Novel deep learning methods for track reconstruction
• Method to solve quantum few-body problems with artificial neural networks
• Generalized convex hull construction for materials discovery
• Deep learning and the renormalization group
• Event generator tuning using Bayesian optimization
• Generalized Transfer Matrix States from Artificial Neural Networks
• Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network
• The Role of Machine Learning in the Next Decade of Cosmology
• PICS: Simulations of Strong Gravitational Lensing in Galaxy Clusters
• Machine learning quantum mechanics: solving quantum mechanics problems using radial basis function networks
• Strategy for quantum algorithm design assisted by machine learning
• Probabilistic Modeling with Matrix Product States
• Machine Learning in High Energy Physics Community White Paper
• Variational optimization in the AI era: Computational Graph States and Supervised Wave-function Optimization
• Latent Heat Calculation of the 3D q=3, 4, and 5 Potts models by Tensor Product Variational Approach
• Hierarchical modeling and statistical calibration for photometric redshifts
• Phase transition encoded in neural network
• Likelihood-free MCMC with Amortized Approximate Ratio Estimators
• Statistical Physics and Representations in Real and Artificial Neural Networks
• Approximating power of machine-learning ansatz for quantum many-body states
• Analyzing interferometric observations of strong gravitational lenses with recurrent and convolutional neural networks
• Statistical Mechanics of Dictionary Learning
• Towards reduction of autocorrelation in HMC by machine learning
• A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines
• Neural network state estimation for full quantum state tomography
• Adversarial Variational Optimization of Non-Differentiable Simulators
• Restricted Boltzmann Machines and Matrix Product States of 1D Translational Invariant Stabilizer Codes
• Ab-Initio Surface Hopping and Multiphoton Ionisation Study of the Photodissociation Dynamics of CS${}_2$
• Kernels on Sample Sets via Nonparametric Divergence Estimates
• Towards meaningful physics from generative models
• The Learnability of Quantum States
• Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes
• Graph Neural Networks for IceCube Signal Classification
• Inferring the quantum density matrix with machine learning
• A Solvable High-Dimensional Model of GAN
• From probabilistic graphical models to generalized tensor networks for supervised learning
• An equation-of-state-meter of QCD transition from deep learning
• Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition
• Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
• Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems
• Approximate Inference for Constructing Astronomical Catalogs from Images
• Unfolding with Gaussian Processes
• Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models
• Backdrop: Stochastic Backpropagation
• New approaches for boosting to uniformity

Cited by in corpus (863)

• Noisy intermediate-scale quantum (NISQ) algorithms
• The frontier of simulation-based inference
• Explainable Machine Learning for Scientific Insights and Discoveries
• Matrix Product States and Projected Entangled Pair States: Concepts, Symmetries, and Theorems
• Social physics
• Quantum Simulators: Architectures and Opportunities
• A rigorous and robust quantum speed-up in supervised machine learning
• Machine learning for electronically excited states of molecules
• QuSpin: a Python Package for Dynamics and Exact Diagonalisation of Quantum Many Body Systems. Part II: bosons, fermions and higher spins
• The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics
• Recurrent Neural Network Wave Functions
• Machine Learning in Nuclear Physics
• Classical dynamical density functional theory: from fundamentals to applications
• Data-Driven Collective Variables for Enhanced Sampling
• Provably efficient machine learning for quantum many-body problems
• Deep-Learning Density Functional Theory Hamiltonian for Efficient ab initio Electronic-Structure Calculation
• Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
• Bell nonlocality in networks
• Perspective on integrating machine learning into computational chemistry and materials science
• Tailoring Structure-borne Sound Through Bandgap Engineering in Phononic Crystals and Metamaterials: A Comprehensive Review
• Graph Neural Networks in Particle Physics
• Opportunities in Quantum Reservoir Computing and Extreme Learning Machines
• Unsupervised machine learning and band topology
• Artificial Intelligence and Machine Learning for Quantum Technologies
• Generalization in Quantum Machine Learning: a Quantum Information Perspective
• Learning Quantum Systems
• Quantum Machine Learning for Chemistry and Physics
• Exploring Phononic Properties of Two-Dimensional Materials using Machine Learning Interatomic Potentials
• Convolutional neural networks: a magic bullet for gravitational-wave detection?
• A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic
• NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems
• Machine Learning for Quantum Matter
• Operator learning for predicting multiscale bubble growth dynamics
• Neural Quantum States of frustrated magnets: generalization and sign structure
• Neural Network Potentials for Chemistry: Concepts, Applications and Prospects
• Dirac-type nodal spin liquid revealed by refined quantum many-body solver using neural-network wave function, correlation ratio, and level spectroscopy
• Roadmap on structured waves
• Ab-initio quantum chemistry with neural-network wavefunctions
• Enhanced Light-Matter Interactions in Dielectric Nanostructures via Machine Learning Approach
• Machine Learning on Neutron and X-Ray Scattering
• Machine learning the nuclear mass
• Learning Deep Implicit Fourier Neural Operators (IFNOs) with Applications to Heterogeneous Material Modeling
• Machine learning for long-distance quantum communication
• Quantum federated learning through blind quantum computing
• Unsupervised phase discovery with deep anomaly detection
• Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems
• Review: Deep Learning in Electron Microscopy
• Advances of Machine Learning in Materials Science: Ideas and Techniques
• Restricted Boltzmann Machines for Quantum States with Nonabelian or Anyonic Symmetries
• Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
• Unsupervised Manifold Clustering of Topological Phononics
• Machine learning non-Markovian quantum dynamics
• Topological quantum phase transitions retrieved through unsupervised machine learning
• Quantum Machine Learning: from physics to software engineering
• Topological Quantum Compiling with Reinforcement Learning
• Neural tensor contractions and the expressive power of deep neural quantum states
• Quantum Engines and Refrigerators
• Recent advances for quantum classifiers
• Variational Monte Carlo calculations of $\mathbf{A}\le 4$ nuclei with an artificial neural-network correlator ansatz
• Machine learning active-nematic hydrodynamics
• Machine Learning for Condensed Matter Physics
• Controlling Rayleigh-Bénard convection via Reinforcement Learning
• Ultra-High-Energy Cosmic Rays: The Intersection of the Cosmic and Energy Frontiers
• Computer-inspired Quantum Experiments
• Neural network wave functions and the sign problem
• Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
• 2022 Review of Data-Driven Plasma Science
• Measurement Based Feedback Quantum Control With Deep Reinforcement Learning for Double-well Non-linear Potential
• Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
• Unsupervised machine learning of topological phase transitions from experimental data
• Exploring QCD matter in extreme conditions with Machine Learning
• Artificial neural networks for nonlinear pulse shaping in optical fibers
• Neural network quantum state tomography in a two-qubit experiment
• Novel Bayesian neural network based approach for nuclear charge radii
• Thermodynamics and dielectric response of ${\text{BaTiO}}_3$ by data-driven modeling
• Applications and Techniques for Fast Machine Learning in Science
• Precision Studies of QCD in the Low Energy Domain of the EIC
• Understanding quantum machine learning also requires rethinking generalization
• Experimental identification of the second-order non-Hermitian skin effect with physics-graph-informed machine learning
• Learning physical properties of liquid crystals with deep convolutional neural networks
• Precise measurement of quantum observables with neural-network estimators
• Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems
• Persistence in Complex Systems
• Hidden Unit Specialization in Layered Neural Networks: ReLU vs. Sigmoidal Activation
• Deep learning the Hohenberg-Kohn maps of Density Functional Theory
• Variational Quantum Eigensolver for Frustrated Quantum Systems
• Non-Hermitian Topological Phases: Principles and Prospects
• Entanglement Classification via Neural Network Quantum States
• Learning the ground state of a non-stoquastic quantum Hamiltonian in a rugged neural network landscape
• Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks
• Nuclear charge radii in Bayesian neural networks revisited
• Helping restricted Boltzmann machines with quantum-state representation by restoring symmetry
• Learning models of quantum systems from experiments
• Machine learning and excited-state molecular dynamics
• The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials
• Probing out-of-distribution generalization in machine learning for materials
• Machine learning the deuteron
• Multi-task learning on nuclear masses and separation energies with the kernel ridge regression
• Geometry of learning neural quantum states
• A Tutorial on Optimal Control and Reinforcement Learning methods for Quantum Technologies
• Ranking the information content of distance measures
• Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum states
• Perspectives on adaptive dynamical systems
• A posteriori learning for quasi-geostrophic turbulence parametrization
• Machine Learning meets Quantum Foundations: A Brief Survey
• Measurement-induced criticality as a data-structure transition
• Learning entropy production via neural networks
• A deep learning approach for determining the chiral indices of carbon nanotubes from high-resolution transmission electron microscopy images
• Nuclear energy density functionals from machine learning
• Boosting Monte Carlo simulations of spin glasses using autoregressive neural networks
• A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC
• Unsupervised machine learning of quantum phase transitions using diffusion maps
• Topological photonics: fundamental concepts, recent developments, and future directions
• Introduction to Theoretical and Experimental aspects of Quantum Optimal Control
• Matrix Model simulations using Quantum Computing, Deep Learning, and Lattice Monte Carlo
• Transfer learning for scalability of neural-network quantum states
• Uncertainty Aware Learning for High Energy Physics
• Deep Learning and AdS/QCD
• Nonlinear interferometry beyond classical limit facilitated by cyclic dynamics
• The world as a neural network
• Tensor network based machine learning of non-Markovian quantum processes
• Markovian Quantum Neuroevolution for Machine Learning
• Spin-Dependent Graph Neural Network Potential for Magnetic Materials
• Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system
• The Presence and Absence of Barren Plateaus in Tensor-network Based Machine Learning
• The Hidden Geometry of Particle Collisions
• Quantum machine learning and quantum biomimetics: A perspective
• Realizing a quantum generative adversarial network using a programmable superconducting processor
• Statistical Mechanics of Deep Linear Neural Networks: The Back-Propagating Kernel Renormalization
• Solving Quasiparticle Band Spectra of Real Solids using Neural-Network Quantum States
• Supervised Hebbian Learning
• Full-sky Cosmic Microwave Background Foreground Cleaning Using Machine Learning
• Conceptual understanding through efficient inverse-design of quantum optical experiments
• Unsupervised learning universal critical behavior via the intrinsic dimension
• Unsupervised Learning of Non-Hermitian Topological Phases
• DropTrack -- automatic droplet tracking using deep learning for microfluidic applications
• Using deep learning to understand and mitigate the qubit noise environment
• A deep neural network for molecular wave functions in quasi-atomic minimal basis representation
• Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
• Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions
• Artificial intelligence discovery of a charging protocol in a micromaser quantum battery
• Semantic Segmentation with a Sparse Convolutional Neural Network for Event Reconstruction in MicroBooNE
• Riemannian geometry and automatic differentiation for optimization problems of quantum physics and quantum technologies
• Deep learning stochastic processes with QCD phase transition
• Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning
• Stochastic normalizing flows as non-equilibrium transformations
• Deep learning of quantum entanglement from incomplete measurements
• Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks
• Adaptable Hamiltonian neural networks
• Quantum Machine-Learning for Eigenstate Filtration in Two-Dimensional Materials
• Unsupervised learning of topological phase transitions using Calinski-Harabaz index
• Robust Prediction of Force Chains in Jammed Solids using Graph Neural Networks
• Mapping distinct phase transitions to a neural network
• Robust calibration of multiparameter sensors via machine learning at the single-photon level
• Hybrid Machine Learning for Scanning Near-field Optical Spectroscopy
• Unravelling physics beyond the standard model with classical and quantum anomaly detection
• Imaginary components of out-of-time correlators and information scrambling for navigating the learning landscape of a quantum machine learning model
• Determining liquid crystal properties with ordinal networks and machine learning
• Deep Learning the Morphology of Dark Matter Substructure
• Scalability of all-optical neural networks based on spatial light modulators
• DADApy: Distance-based Analysis of DAta-manifolds in Python
• Hybrid convolutional neural network and PEPS wave functions for quantum many-particle states
• Deep Learning Criminal Networks
• Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks
• Neural-network quantum states for many-body physics
• Supervised and unsupervised learning of directed percolation
• Improved machine learning algorithm for predicting ground state properties
• Machine learning topological invariants of non-Hermitian systems
• Setting up experimental Bell test with reinforcement learning
• Quantum field-theoretic machine learning
• Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials - A review
• Topological Persistence Machine of Phase Transitions
• Soft Metamaterials: Adaptation and Intelligence
• Exploring neural network training strategies to determine phase transitions in frustrated magnetic models
• Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks
• Purifying Deep Boltzmann Machines for Thermal Quantum States
• Variational Quantum Anomaly Detection: Unsupervised mapping of phase diagrams on a physical quantum computer
• U(1) symmetric recurrent neural networks for quantum state reconstruction
• The forgotten channels: charged Higgs boson decays to a $W^{\pm }$ and a non-SM-like Higgs boson
• Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
• Boosted decision trees in the era of new physics: a smuon analysis case study
• Machine-learning detection of the Berezinskii-Kosterlitz-Thouless transitions in the q-state clock models
• Topological data analysis and machine learning
• Mean-field inference methods for neural networks
• Differentiable Strong Lensing: Uniting Gravity and Neural Nets through Differentiable Probabilistic Programming
• Classifying global state preparation via deep reinforcement learning
• Machine learning-based inversion of nuclear responses
• Machine-learning physics from unphysics: Finding deconfinement temperature in lattice Yang-Mills theories from outside the scaling window
• Unsupervised and supervised learning of interacting topological phases from single-particle correlation functions
• Gauge equivariant neural networks for quantum lattice gauge theories
• Determinant-free fermionic wave function using feed-forward neural networks
• Neural networks for detecting multimode Wigner-negativity
• Role of stochastic noise and generalization error in the time propagation of neural-network quantum states
• Extending machine learning classification capabilities with histogram reweighting
• Convolutional Neural Networks for Direct Detection of Dark Matter
• Machine learning spectral indicators of topology
• New Trends in Quantum Machine Learning
• Determining the temperature in heavy-ion collisions with multiplicity distribution
• Exponential data encoding for quantum supervised learning
• Nuclei with up to $\mathbf{A}=6$ nucleons with artificial neural network wave functions
• A Cautionary Tale of Decorrelating Theory Uncertainties
• Symmetry meets AI
• Comparing recurrent and convolutional neural networks for predicting wave propagation
• Many-body localization in large systems: Matrix-product-state approach
• Absorbing phase transition with a continuously varying exponent in a quantum contact process: a neural network approach
• Machine learning for complete intersection Calabi-Yau manifolds: a methodological study
• Estimating the degree of non-Markovianity using machine learning
• Application of machine learning in the determination of impact parameter in the ${}^{132}$Sn+${}^{124}$Sn system
• Deep learning probability flows and entropy production rates in active matter
• Learning quantum dynamics with latent neural ODEs
• Scientific intuition inspired by machine learning generated hypotheses
• Intrinsic dimension of path integrals: data mining quantum criticality and emergent simplicity
• Deep-neural-network approach to solving the ab initio nuclear structure problem
• Investigating Topological Order using Recurrent Neural Networks
• Mapping of attention mechanisms to a generalized Potts model
• Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies
• An unsupervised deep learning algorithm for single-site reconstruction in quantum gas microscopes
• Flexible learning of quantum states with generative query neural networks
• A single $T$-gate makes distribution learning hard
• A perspective on machine learning and data science for strongly correlated electron problems
• Reconstructing the Kinematics of Deep Inelastic Scattering with Deep Learning
• Recent Advances in Deep Learning for Channel Coding: A Survey
• mlpack 4: a fast, header-only C++ machine learning library
• Revealing the Phase Diagram of Kitaev Materials by Machine Learning: Cooperation and Competition between Spin Liquids
• Neural ODE and Holographic QCD
• Fundamental limits to learning closed-form mathematical models from data
• Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence
• Predicting Critical Transitions in Multiscale Dynamical Systems Using Reservoir Computing
• Compressing deep neural networks by matrix product operators
• Generative models for sampling and phase transition indication in spin systems
• Adversarial Multi-task Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations
• Spatial Mode Correction of Single Photons using Machine Learning
• Single-exposure absorption imaging of ultracold atoms using deep learning
• Phase Detection with Neural Networks: Interpreting the Black Box
• XLB: A differentiable massively parallel lattice Boltzmann library in Python
• Self-learning Emulators and Eigenvector Continuation
• Machine Learning and Quantum Devices
• Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks
• Machine learning time-local generators of open quantum dynamics
• Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters
• Completeness of Atomic Structure Representations
• Lorentz group equivariant autoencoders
• Interpretable and unsupervised phase classification
• Quantum kernels to learn the phases of quantum matter
• Shared Data and Algorithms for Deep Learning in Fundamental Physics
• Quantum-tailored machine-learning characterization of a superconducting qubit
• Correlation length in random MPS and PEPS
• Deep Learning the Functional Renormalization Group
• Magnetic Anisotropy in Two-dimensional van der Waals Magnetic Materials and Their Heterostructures: Importance, Mechanisms, and Opportunities
• Protocol Discovery for the Quantum Control of Majoranas by Differentiable Programming and Natural Evolution Strategies
• Machine learning identifies scale-free properties in disordered materials
• Learning the constitutive relation of polymeric flows with memory
• Estimation of Impact Parameter and Transverse Spherocity in heavy-ion collisions at the LHC energies using Machine Learning
• Reproducibility and Replication of Experimental Particle Physics Results
• SUPPNet: Neural network for stellar spectrum normalisation
• Reconstructing the cosmological density and velocity fields from redshifted galaxy distributions using V-net
• Cataloging Accreted Stars within Gaia DR2 using Deep Learning
• Inverse molecular design from first principles: tailoring organic chromophore spectra for optoelectronic applications
• Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
• Classifying Pole of Amplitude Using Deep Neural Network
• Local Order Metrics for Two-Phase Media Across Length Scales
• Data driven soliton solution of the nonlinear Schrödinger equation with certain $\mathcal{P}\mathcal{T}$-symmetric potentials via deep learning
• Operationally meaningful representations of physical systems in neural networks
• Introduction to correlation networks: Interdisciplinary approaches beyond thresholding
• A scheme for automatic differentiation of complex loss functions
• Artificial Neural Network Based Computation for Out-of-Time-Ordered Correlators
• Model independent analysis of coupled-channel scattering: a deep learning approach
• HInet: Generating neutral hydrogen from dark matter with neural networks
• Machine learning of phase transitions in nonlinear polariton lattices
• Estimating Elliptic Flow Coefficient in Heavy Ion Collisions using Deep Learning
• Scalable neural networks for the efficient learning of disordered quantum systems
• Machine Learning Universal Bosonic Functionals
• A fast and efficient deep learning procedure for tracking droplet motion in dense microfluidic emulsions
• Ultrafast single-channel machine vision based on neuro-inspired photonic computing
• Classification of four-qubit entangled states via Machine Learning
• Efficient and Robust Entanglement Generation with Deep Reinforcement Learning for Quantum Metrology
• Quantum neural networks force fields generation
• On weak ergodicity breaking in mean-field spin glasses
• Model-free prediction of emergence of extreme events in a parametrically driven nonlinear dynamical system by Deep Learning
• A Neural Networks study of the phase transitions of Potts model
• Efficient sorting of orbital-angular-momentum states with large topological charges and their unknown superpositions via machine learning
• Deep learning assisted jet tomography for the study of Mach cones in QGP
• Deep Generative Models of Gravitational Waveforms via Conditional Autoencoder
• Purifying electron spectra from noisy pulses with machine learning using synthetic Hamilton matrices
• Cosmic Velocity Field Reconstruction Using AI
• Classification of Fermi-LAT sources with deep learning using energy and time spectra
• Machine-learning assisted quantum control in random environment
• Integrated Photonic Platforms for Quantum Technology: A Review
• Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests
• Predicting excited states from ground state wavefunction by supervised quantum machine learning
• Machine learning nonequilibrium electron forces for adiabatic spin dynamics
• Data-Driven Selection and Spectral Classification of White Dwarf Stars
• Efficient estimation of trainability for variational quantum circuits
• Deep learning analysis of polaritonic waves images
• Supervised learning for robust quantum control in composite-pulse systems
• Universal and optimal coin sequences for high entanglement generation in 1D discrete time quantum walks
• Measuring the Hubble Constant with cosmic chronometers: a machine learning approach
• Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks
• Relevance in the Renormalization Group and in Information Theory
• Learning quantum phase transitions through Topological Data Analysis
• Machine learning non-Hermitian topological phases
• Simulating quantum field theories on continuous-variable quantum computers
• Designing quantum many-body matter with conditional generative adversarial networks
• Mixed State Entanglement Classification using Artificial Neural Networks
• Deep Learning-Assisted Classification of Site-Resolved Quantum Gas Microscope Images
• A deep learning method for the trajectory reconstruction of cosmic rays with the DAMPE mission
• Principal components of nuclear mass models
• Learning the black hole metric from holographic conductivity
• Active Learning Algorithm for Computational Physics
• Machine-Learned Phase Diagrams of Generalized Kitaev Honeycomb Magnets
• Inverse Renormalization Group based on Image Super-Resolution using Deep Convolutional Networks
• Symmetries and phase diagrams with real-space mutual information neural estimation
• Recognizing three-dimensional phase images with deep learning
• Computational Infrared Spectroscopy of 958 Phosphorus-bearing Molecules
• Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural network
• Disentangling Boosted Higgs Boson Production Modes with Machine Learning
• Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural Networks
• Variational quantum dynamics of two-dimensional rotor models
• Role of coherence in many-body Quantum Reservoir Computing
• Predicting Beta Decay Energy with Machine Learning
• Unsupervised Learning of Topological Non-Abelian Braiding in Non-Hermitian Bands
• Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalization
• Detecting Chiral Magnetic Effect via Deep Learning
• Phase diagram of quantum generalized Potts-Hopfield neural networks
• Effective models and predictability of chaotic multiscale systems via machine learning
• Bayesian Neural Networks for Fast SUSY Predictions
• Estimating Quantum Mutual Information Through a Quantum Neural Network
• Energy landscape analysis based on the Ising model: Tutorial review
• Machine Learning Microscopic Form of Nematic Order in twisted double-bilayer graphene
• On the expressivity of embedding quantum kernels
• Covariantizing Phase Space
• A super-polynomial quantum-classical separation for density modelling
• Quantum associative memory with a single driven-dissipative nonlinear oscillator
• Networked Quantum Services
• SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning
• Machine learning for excitation energy transfer dynamics
• Deep Learning in Searching the Spectroscopic Redshift of Quasars
• Noise-Adaptive Intelligent Programmable Meta-Imager
• Harnessing Interpretable Machine Learning for Holistic Inverse Design of Origami
• Entanglement Features of Random Neural Network Quantum States
• Accurate phonon blockade detector composed of a quadratically coupled optomechanical system
• Machine learning aided atomic structure identification of interfacial ionic hydrates from atomic force microscopy images
• Neural Network flows of low q-state Potts and clock Models
• Transfer learning of phase transitions in percolation and directed percolation
• Persistent currents in ultracold gases
• Machine classification for probe based quantum thermometry
• Classifying near-threshold enhancement using deep neural network
• Machine-Learning Study using Improved Correlation Configuration and Application to Quantum Monte Carlo Simulation
• Decoding Dark Matter Substructure without Supervision
• Machine learning understands knotted polymers
• Boltzmann machines as two-dimensional tensor networks
• Continuous-mixture Autoregressive Networks for efficient variational calculation of many-body systems
• Benefits of Open Quantum Systems for Quantum Machine Learning
• Neural Monte Carlo Renormalization Group
• Conditional Born machine for Monte Carlo event generation
• A learning by confusion approach to characterize phase transitions
• Correlation-pattern-based Continuous-variable Entanglement Detection through Neural Networks
• The Neutron Star Outer Crust Equation of State: A Machine Learning approach
• Predicting the Electronic Density Response of Condensed-Phase Systems to Electric Field Perturbations
• Benchmarks and Explanations for Deep Learning Estimates of X-ray Galaxy Cluster Masses
• Quantum State Tomography for Matrix Product Density Operators
• Quantum Continual Learning Overcoming Catastrophic Forgetting
• Machine learning predictions for local electronic properties of disordered correlated electron systems
• Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
• $2^{1296}$ Exponentially Complex Quantum Many-Body Simulation via Scalable Deep Learning Method
• Quantum Vision Transformers for Quark-Gluon Classification
• Neural activity in quarks language: Lattice Field Theory for a network of real neurons
• Frequentist Parameter Estimation with Supervised Learning
• Deep learning the hierarchy of steering measurement settings of qubit-pair states
• Solving and visualizing fractional quantum Hall wavefunctions with neural network
• Generation and storage of spin squeezing via learning-assisted optimal control
• Replacing neural networks by optimal analytical predictors for the detection of phase transitions
• Unsupervised machine learning approaches to the $q$-state Potts model
• Machine-learning detection of the Berezinskii-Kosterlitz-Thouless transition and the second-order phase transition in the XXZ models
• Mapping out phase diagrams with generative classifiers
• Random matrix analysis of deep neural network weight matrices
• Constraining the Woods-Saxon potential in fusion reactions based on the neural network
• Ground state search by local and sequential updates of neural network quantum states
• Extracting electronic many-body correlations from local measurements with artificial neural networks
• Statistical Complexity of Quantum Learning
• Taming quantum systems: A tutorial for using shortcuts-to-adiabaticity, quantum optimal control, and reinforcement learning
• Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows
• Using machine learning to compress the matter transfer function $T(k)$
• Variational optimization of the amplitude of neural-network quantum many-body ground states
• p-Adic Statistical Field Theory and Deep Belief Networks
• From ionic surfactants to Nafion through convolutional neural networks
• Deep neural networks for inverse problems in mesoscopic physics: Characterization of the disorder configuration from quantum transport properties
• Dynamical reciprocity in interacting games: numerical results and mechanism analysis
• Learning about learning by many-body systems
• Neural quantum kernels: training quantum kernels with quantum neural networks
• Deep neural networks have an inbuilt Occam's razor
• Can a CNN trained on the Ising model detect the phase transition of the $q$-state Potts model?
• Neural-network-based parameter estimation for quantum detection
• Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics
• Quantum space-time marginal problem: global causal structure from local causal information
• Machine learning for percolation utilizing auxiliary Ising variables
• Tomography of time-dependent quantum spin networks with machine learning
• Matrix Product States with Backflow correlations
• Eigenvalue distribution of nonlinear models of random matrices
• Machine Learning-Assisted Manipulation and Readout of Molecular Spin Qubits
• Adding machine learning within Hamiltonians: Renormalization group transformations, symmetry breaking and restoration
• Data-Centric Machine Learning in Quantum Information Science
• Optical lattice experiments at unobserved conditions and scales through generative adversarial deep learning
• Machine learning-accelerated chemistry modeling of protoplanetary disks
• Machine learning phases and criticalities without using real data for training
• Density functionals and Kohn-Sham potentials with minimal wavefunction preparations on a quantum computer
• Persistent homology analysis of a generalized Aubry-André-Harper model
• Automated design of quantum optical experiments for device-independent quantum key distribution
• Three Learning Stages and Accuracy-Efficiency Tradeoff of Restricted Boltzmann Machines
• Supervised learning of few dirty bosons with variable particle number
• A method for approximating optimal statistical significances with machine-learned likelihoods
• Physics-Informed Machine Learning for Modeling Turbulence in Supernovae
• Generalized hetero-associative neural networks
• Bulk reconstruction of metrics inside black holes by complexity
• Transferable empirical pseudopotenials from machine learning
• Real-time Dynamics of the Schwinger Model as an Open Quantum System with Neural Density Operators
• Learning minimal representations of stochastic processes with variational autoencoders
• Deep learning the astrometric signature of dark matter substructure
• On the generalizability of artificial neural networks in spin models
• Probing the effects of broken symmetries in machine learning
• Robust inversion of time-resolved data via forward-optimisation in a trajectory basis
• General time-reversal equivariant neural network potential for magnetic materials
• Uncertainty Aware ML-based surrogate models for particle accelerators: A Study at the Fermilab Booster Accelerator Complex
• Neural Network-Based Design of Approximate Gottesman-Kitaev-Preskill Code
• Neural-network quantum states for a two-leg Bose-Hubbard ladder under magnetic flux
• Supernova search with active learning in ZTF DR3
• Machine learning light hypernuclei
• Introduction to the dynamics of disordered systems: equilibrium and gradient descent
• Estimating the Euclidean quantum propagator with deep generative modeling of Feynman paths
• Exploring super-additivity of coherent information of noisy quantum channels through Genetic algorithms
• Scattering with Neural Operators
• Experimental demonstration of adversarial examples in learning topological phases
• Machine learning spectral functions in lattice QCD
• On generative models of T-cell receptor sequences
• From complex to simple : hierarchical free-energy landscape renormalized in deep neural networks
• Tracking droplets in soft granular flows with deep learning techniques
• Classification and reconstruction of optical quantum states with deep neural networks
• Reconstruction of nanoscale particles from single-shot wide-angle FEL diffractions patterns with physics-informed neural networks
• Deep learning of deformation-dependent conductance in thin films: nanobubbles in graphene
• Forecasting high-impact research topics via machine learning on evolving knowledge graphs
• Machine Learning for Estimation and Control of Quantum Systems
• Non-parametric learning critical behavior in Ising partition functions: PCA entropy and intrinsic dimension
• Adaptive projected variational quantum dynamics
• Confinement in non-Abelian lattice gauge theory via persistent homology
• Reduced basis emulation of pairing in finite systems
• A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction
• Identification of high order closure terms from fully kinetic simulations using machine learning
• Efficient learning of mixed-state tomography for photonic quantum walk
• Unsupervised mapping of phase diagrams of 2D systems from infinite projected entangled-pair states via deep anomaly detection
• Kernel-based quantum regressor models learn non-Markovianity
• Optimizing persistent currents in a ring-shaped Bose-Einstein condensate using machine learning
• Identifying nearby sources of ultra-high-energy cosmic rays with deep learning
• Entanglement-induced provable and robust quantum learning advantages
• Neural networks in quantum many-body physics: a hands-on tutorial
• Machine learning depinning of dislocation pileups
• Fully solvable finite simplex lattices with open boundaries in arbitrary dimensions
• Quantum error reduction with deep neural network applied at the post-processing stage
• A Physical Quantum Agent
• Machine learning in interpretation of electronic core-level spectra
• On the descriptive power of Neural-Networks as constrained Tensor Networks with exponentially large bond dimension
• Predicting elastic and plastic properties of small iron polycrystals by machine learning
• Signal detection in nearly continuous spectra and symmetry breaking
• Ultrametric Fitting by Gradient Descent
• Generalization Error Bound for Quantum Machine Learning in NISQ Era -- A Survey
• Preparing Schrödinger cat states in a microwave cavity using a neural network
• Training-free hyperparameter optimization of neural networks for electronic structures in matter
• Machine learning for structure-property relationships: Scalability and limitations
• Steering-induced phase transition in measurement-only quantum circuits
• Scalable Imaginary Time Evolution with Neural Network Quantum States
• Topological defects and confinement with machine learning: the case of monopoles in compact electrodynamics
• Addressing the Non-perturbative Regime of the Quantum Anharmonic Oscillator by Physics-Informed Neural Networks
• Satellite image classification with neural quantum kernels
• Compact Neural-network Quantum State representations of Jastrow and Stabilizer states
• Parallel Learning by Multitasking Neural Networks
• Certificates of quantum many-body properties assisted by machine learning
• Qudit Machine Learning
• Recurrent Localization Networks applied to the Lippmann-Schwinger Equation
• Detection of Berezinskii-Kosterlitz-Thouless transition via Generative Adversarial Networks
• Extending the reach of quantum computing for materials science with machine learning potentials
• Morphology of three-body quantum states from machine learning
• Complexity of spin configurations dynamics due to unitary evolution and periodic projective measurements
• Deep reinforcement learning for preparation of thermal and prethermal quantum states
• Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models
• Noisy atomic magnetometry with Kalman filtering and measurement-based feedback
• Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine Learning Algorithms
• Towards recognizing the light facet of the Higgs Boson
• Experimental Learning of Pure Quantum States using Sequential Single-Shot Measurement Outcomes
• Exponentially improved efficient machine learning for quantum many-body states with provable guarantees
• Study of phase transition of Potts model with Domain Adversarial Neural Network
• Learning the Universe: Cosmological and Astrophysical Parameter Inference with Galaxy Luminosity Functions and Colours
• Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning
• Accelerated Magnonic Motional Cooling with Deep Reinforcement Learning
• Virtual distillation with noise dilution
• Predicting Solid State Material Platforms for Quantum Technologies
• Deep Neural Network application: Higgs boson CP state mixing angle in H to tau tau decay and at LHC
• Data-driven detection of multi-messenger transients
• Enhanced quantum state preparation via stochastic prediction of neural network
• Single-Shot Matrix-Matrix Multiplication Optical Tensor Processor for Deep Learning
• Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks
• Quantum Reservoir Computing for Speckle-Disorder Potentials
• Assessing the foundation and applicability of some dark energy fluid models in the Dirac-Born-Infeld framework
• Gradient domain machine learning with composite kernels: improving the accuracy of PES and force fields for large molecules
• Machine Learning the Operator Content of the Critical Self-Dual Ising-Higgs Gauge Model
• Machine learning phase transitions of the three-dimensional Ising universality class
• The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems
• Neural-network-supported basis optimizer for the configuration interaction problem in quantum many-body clusters: Feasibility study and numerical proof
• Modeling Systems with Machine Learning based Differential Equations
• Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities
• Machine learning method to determine concentrations of structural defects in irradiated materials
• Learning phase transitions from regression uncertainty: A new regression-based machine learning approach for automated detection of phases of matter
• Shear induced ordering in systems with competing interactions: A machine learning study
• The Coming Decades of Quantum Simulation
• Efficient Solutions of Fermionic Systems using Artificial Neural Networks
• Emergence of hierarchical modes from deep learning
• Unsupervised Machine Learning of Quenched Gauge Symmetries: A Proof-of-Concept Demonstration
• Machine learning approach to analyze heavy quark diffusion coefficient in relativistic heavy-ion collisions
• Machine learning phases of active matter
• Unveiling quantum phase transitions from traps in variational quantum algorithms
• Probing Criticality in Quantum Spin Chains with Neural Networks
• Social hierarchy promotes the cooperation prevalence
• Using Machine Learning to Improve Neutron Identification in Water Cherenkov Detectors
• From predicting to learning dissipation from pair correlations of active liquids
• Hybrid quantum-classical unsupervised data clustering based on the self-organizing feature map
• Detecting composite orders in layered models via machine learning
• Quantum reservoir computing on random regular graphs
• Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks
• Gradient-descent methods for fast quantum state tomography
• Deciphering the Scattering of Mechanically Driven Polymers using Deep Learning
• Cavity-Heisenberg spin-$j$ chain quantum battery and reinforcement learning optimization
• Renormalized Mutual Information for Artificial Scientific Discovery
• Privacy-preserving machine learning with tensor networks
• Phase determination with and without deep learning
• Machine learning of phases and structures for model systems in physics
• Spatially heterogeneous learning by a deep student machine
• Quantum Transport in Open Spin Chains using Neural-Network Quantum States
• Meta-learning and data augmentation for mass-generalised jet taggers
• Machine learning classification: case of Higgs boson CP state in H to tau tau decay at LHC
• Casimir effect with machine learning
• Bounding free energy difference with flow matching
• Accurate generation of stochastic dynamics based on multi-model Generative Adversarial Networks
• Direct detection of plasticity onset through total-strain profile evolution
• Principal component analysis of absorbing state phase transitions
• Dark soliton detection using persistent homology
• Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay Line Detectors
• Message Passing Variational Autoregressive Network for Solving Intractable Ising Models
• Deep Learning for estimating parameters of Gravitational Waves
• Error Approximation and Bias Correction in Dynamic Problems using a Recurrent Neural Network/Finite Element Hybrid Model
• Predicting the Onset of Quantum Synchronization Using Machine Learning
• Neural network enhanced hybrid quantum many-body dynamical distributions
• Feature space approximation for kernel-based supervised learning
• Variational Autoencoder Analysis of Ising Model Statistical Distributions and Phase Transitions
• BKT transitions of the XY and six-state clock models on the various two-dimensional lattices
• FNet II: Spectral Classification of Quasars, Galaxies, Stars, and broad absorption line (BAL) Quasars
• A machine learning-based methodology for pulse classification in dual-phase xenon time projection chambers
• Inferring Markovian quantum master equations of few-body observables in interacting spin chains
• Non-Classical Kernels in Continuous Variable Systems
• Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe
• A deep neural network approach to solve the Dirac equation
• Efficient and quantum-adaptive machine learning with fermion neural networks
• A simple framework for contrastive learning phases of matter
• Influence of anisotropy on the study of critical behavior of spin models by machine learning methods
• Conservation Laws and Spin System Modeling through Principal Component Analysis
• Emergence of a finite-size-scaling function in the supervised learning of the Ising phase transition
• Roadmap on Integrated Quantum Photonics
• Framework for Learning and Control in the Classical and Quantum Domains
• Identifying topology of leaky photonic lattices with machine learning
• Deep learning density functionals for gradient descent optimization
• Migration of self-propelling agent in a turbulent environment with minimal energy consumption
• A learning algorithm with emergent scaling behavior for classifying phase transitions
• Neural Network Quantum States analysis of the Shastry-Sutherland model
• Machine Learning approach to muon spectroscopy analysis
• Superband: an Electronic-band and Fermi surface structure database of superconductors
• Quantum Kernel Evaluation via Hong-Ou-Mandel Interference
• Sampling asymmetric open quantum systems for artificial neural networks
• Learning Potentials of Quantum Systems using Deep Neural Networks
• Network-Initialized Monte Carlo Based on Generative Neural Networks
• Optical Levitation of Arrays of Microspheres
• Insights into one-body density matrices using deep learning
• Machine learning the dynamics of quantum kicked rotor
• Minimization of ion micromotion with artificial neural network
• A Machine Learning Approach to Correct for Mass Resolution Effects in Simulated Halo Clustering Statistics
• Thermodynamics of the Ising model encoded in restricted Boltzmann machines
• All you need is spin: SU(2) equivariant variational quantum circuits based on spin networks
• Machine learning one-dimensional spinless trapped fermionic systems with neural-network quantum states
• Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations
• A rotation-equivariant graph neural network for learning hadronic SMEFT effects
• Rapid detection of phase transitions from Monte Carlo samples before equilibrium
• Comprehensive studies on the universality of BKT transitions -- Machine-learning study, Monte Carlo simulation, and Level-spectroscopy method
• Neural network assisted quantum state and process tomography using limited data sets
• Classification of magnetic order from electronic structure by using machine learning
• Deep learning nonlocal and scalable energy functionals for quantum Ising models
• Supervised Training of Neural-Network Quantum States for the Next Nearest Neighbor Ising model
• Transfer learning from Hermitian to non-Hermitian quantum many-body physics
• Liouville Space Neural Network Representation of Density Matrices
• Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters
• Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster
• Quantifying the magnetic interactions governing chiral spin textures using deep neural networks
• Rise and Fall of Anderson Localization by Lattice Vibrations: A Time-Dependent Machine Learning Approach
• Deciphering quantum fingerprints in electric conductance
• Neural Network Approach to Scaling Analysis of Critical Phenomena
• Using machine learning to find exact analytic solutions to analytically posed physics problems
• Deep learning-based holography for T-linear resistivity
• Blending Optimal Control and Biologically Plausible Learning for Noise-Robust Physical Neural Networks
• Dark Matter Subhalos, Strong Lensing and Machine Learning
• Quantum System Compression: A Hamiltonian Guided Walk Through Hilbert Space
• Development of a resource-efficient FPGA-based neural network regression model for the ATLAS muon trigger upgrades
• How to set up your first machine learning project in astronomy
• Ray-based classification framework for high-dimensional data
• Machine-Learning Performance on Higgs-Pair Production Associated with Dark Matter at the LHC
• Automatic Learning of Topological Phase Boundaries
• Hybrid neural network method of a multilayer perceptron and autoencoder for the α-particle preformation factor in α-decay theory
• Controlling quantum effects in enhanced strong-field ionisation with machine-learning techniques
• ClasSOMfier: A neural network for cluster analysis and detection of lattice defects
• Robust Quantum Reservoir Computing for Molecular Property Prediction
• Characterization of a driven two-level quantum system by Supervised Learning
• Deep Learning Hamiltonians from Disordered Image Data in Quantum Materials
• Exact Quantum Algorithms for Quantum Phase Recognition: Renormalization Group and Error Correction
• Towards Arbitrary QUBO Optimization: Analysis of Classical and Quantum-Activated Feedforward Neural Networks
• DeepQuark: A Deep-Neural-Network Approach to Multiquark Bound States
• Finding Quantum Critical Points with Neural-Network Quantum States
• Deep reinforcement learning for feedback control in a collective flashing ratchet
• State Classification via a Random-Walk-Based Quantum Neural Network
• Reconstruction of Fast Neutron Direction in Segmented Organic Detectors using Deep Learning
• Machine Learning-Informed Scattering Correlation Analysis of Sheared Colloids
• Persistent Homology for Structural Characterization in Disordered Systems
• Deep Reinforcement Learning for Radiative Heat Transfer Optimization Problems
• Insights into Dark Matter Direct Detection Experiments: Decision Trees versus Deep Learning
• Predicting electronic screening for fast Koopmans spectral functional calculations
• Synergy between noisy quantum computers and scalable classical deep learning
• Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning
• Machine learning stochastic differential equations for the evolution of order parameters of classical many-body systems in and out of equilibrium
• Ambitions for theory in the physics of life
• Reconstructing axion-like particles from beam dumps with simulation-based inference
• Black holes and the loss landscape in machine learning
• Analysis and design of transition radiation in layered uniaxial crystals using Tandem neural networks
• Artificial intelligence for improved fitting of trajectories of elementary particles in inhomogeneous dense materials immersed in a magnetic field
• Exploring explicit coarse-grained structure in artificial neural networks
• Deep learning extraction of band structure parameters from density of states: a case study on trilayer graphene
• The Physics of Learning
• Extracting Off-Diagonal Order from Diagonal Basis Measurements
• Topical Review: Extracting Molecular Frame Photoionization Dynamics from Experimental Data
• Berezinskii-Kosterlitz-Thouless transition from Neural Network Flows
• Joint machine learning analysis of muon spectroscopy data from different materials
• The role of coherence theory in attractor quantum neural networks
• Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows
• Machine learning classification of two-dimensional vortex configurations
• Machine Learning S-Wave Scattering Phase Shifts Bypassing the Radial Schrödinger Equation
• Soft Mode in the Dynamics of Over-realizable On-line Learning for Soft Committee Machines
• Tensor networks and efficient descriptions of classical data
• Distributed Training and Optimization Of Neural Networks
• Second Harmonic Imaging Enhanced by Deep Learning Decipher
• Data-driven effective model shows a liquid-like deep learning
• Inferring Hidden Symmetries of Exotic Magnets from Detecting Explicit Order Parameters
• Towards a theory of machine learning
• Unveiling the nonclassicality within quasi-distribution representations through deep learning
• Neural-network quantum state study of the long-range antiferromagnetic Ising chain
• Vicsek Model Meets DBSCAN: Cluster Phases in the Vicsek Model
• Self-Supervised Ensemble Learning: A Universal Method for Phase Transition Classification of Many-Body Systems
• Machine learning a time-local fluctuation theorem for nonequilibrium steady states
• Phase classification in the long-range Harper model using machine learning
• Fluctuation based interpretable analysis scheme for quantum many-body snapshots
• Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony
• Electron-boson spectral density functions of cuprates obtained from optical spectra via machine learning
• Adaptive Random Quantum Eigensolver
• A convolutional-neural-network estimator of CMB constraints on dark matter energy injection
• Stochastic gradient descent with random learning rate
• Deep quantum Monte Carlo approach for polaritonic chemistry
• First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models
• Dirac-Equation Signal Processing: Physics Boosts Topological Machine Learning
• Scale-invariant representation of machine learning
• Deep learning for the modeling and inverse design of radiative heat transfer
• Effective classical correspondence of the Mott transition
• Hamiltonian-learning quantum magnets with non-local impurity tomography
• Machine learning the Ising transition: A comparison between discriminative and generative approaches
• Machine Learning Domain Adaptation in Spin Models with Continuous Phase Transitions
• Variational quantum algorithm for Gaussian discrete solitons and their boson sampling
• Unsupervised Machine Learning Phase Classification for Falicov-Kimball Model
• Gaussian boson sampling and multi-particle event optimization by machine learning in the quantum phase space
• Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC
• Observing how deep neural networks understand physics through the energy spectrum of one-dimensional quantum mechanics
• Unitary Evolutions Sourced By Interacting Quantum Memories: Closed Quantum Systems Directing Themselves Using Their State Histories
• Renormalization-group-inspired neural networks for computing topological invariants
• Deep Learning the Forecast of Galactic Cosmic-Ray Spectra
• Fast multilabel classification of HEP constraints with deep learning
• Probabilistic Linear Solvers for Machine Learning
• Magnetism trends in doped Ce-Cu intermetallics in the vicinity of quantum criticality: realistic Kondo lattice models based on dynamical mean-field theory
• Machine learning for predicting control landscape maps of quantum molecular dynamics: Laser-induced three-dimensional alignment of asymmetric top molecules
• Stochastic weight matrix dynamics during learning and Dyson Brownian motion
• Family of Quantum Mutual Information in Multiparty Quantum Systems
• Transforming a rare event search into a not-so-rare event search in real-time with deep learning-based object detection
• Estimation of the geometric measure of entanglement with Wehrl Moments through Artificial Neural Networks
• The impact of memory on learning sequence-to-sequence tasks
• Machine learning the deuteron: new architectures and uncertainty quantification
• A Theoretical Connection Between Statistical Physics and Reinforcement Learning
• Optimized numerical gradient and Hessian estimation for variational quantum algorithms
• Boundary between noise and information applied to filtering neural network weight matrices
• Stochastic Resetting Mitigates Latent Gradient Bias of SGD from Label Noise
• Unsupervised Machine Learning Discovery of Chemical and Physical Transformation Pathways from Imaging Data
• Machine learning identification of symmetrized base states of Rydberg atoms
• Enhancing lattice kinetic schemes for fluid dynamics with Lattice-Equivariant Neural Networks
• Stable many-body resonances in open quantum systems
• Search for glitches of gamma-ray pulsars with deep learning
• Building imaginary-time thermal field theory with artificial neural networks
• Deep learning in bifurcations of particle trajectories
• Foundations of automatic feature extraction at LHC--point clouds and graphs
• kSZ Pairwise Velocity Reconstruction with Machine Learning
• Comparative study on compact quantum circuits of hybrid quantum-classical algorithms for quantum impurity models
• Neural Unfolding of the Chiral Magnetic Effect in Heavy-Ion Collisions
• Neural Network Approach to Construction of Classical Integrable Systems
• The viewing angle in AGN SED models, a data-driven analysis
• Learning crystal field parameters using convolutional neural networks
• Optimizing measurement-based cooling by reinforcement learning
• Stochastic Gradient Descent and Anomaly of Variance-flatness Relation in Artificial Neural Networks
• Optimization of convolutional neural networks for background suppression in the PandaX-III experiment
• Classifying topological neural network quantum states via diffusion maps
• Localization of quantum walk with classical randomness: Comparison between manual methods and supervised machine learning
• Inferring nonlinear fractional diffusion processes from single trajectories
• Learning DFT
• Supervised learning of an interacting 2D hard-core boson model of a weak topological insulator using correlation functions
• The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions
• Reduced Simulations for High-Energy Physics, a Middle Ground for Data-Driven Physics Research
• Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments
• Exploring percolation phase transition in the three-dimensional Ising model with machine learning
• Many-body mobility edges in 1D and 2D revealed by convolutional neural networks
• AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning
• Improving Demand Forecasting in Open Systems with Cartogram-Enhanced Deep Learning
• Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics
• Diagnosing quantum transport from wave function snapshots
• Data-driven model reconstruction for nonlinear wave dynamics
• Comments on the minimal training set for CNN: a case study of the frustrated $J_1$-$J_2$ Ising model on the square lattice
• Predicting fermionic densities using a Projected Quantum Kernel method
• Quantum circuit complexity and unsupervised machine learning of topological order
• Research progress of artificial intelligence empowered quantum communication and quantum sensing systems
• Deciphering the Small-Angle Scattering of Polydisperse Hard Spheres using Deep Learning
• From basic science to technological development: the case for two avenues
• Subaging in underparametrized Deep Neural Networks
• Exploring Fresnel diffraction at a straight edge with a neural network
• Machine Learning approach to the Floquet--Lindbladian problem
• Quantum Compiling by Deep Reinforcement Learning
• Predicting the spread of COVID-19 in Delhi, India using Deep Residual Recurrent Neural Networks
• A deep learning-based ODE solver for chemical kinetics
• Simulating quantum dynamics: Evolution of algorithms in the HPC context
• Boosting on the shoulders of giants in quantum device calibration
• A Use of Even Activation Functions in Neural Networks
• Phase probabilities in first-order transitions using machine learning
• Phase diagram and eigenvalue dynamics of stochastic gradient descent in multilayer neural networks
• Universal Performance Gap of Neural Quantum States Applied to the Hofstadter-Bose-Hubbard Model
• Moiré spintronics: Emergent phenomena, material realization and machine learning accelerating discovery
• Neural Scaling Laws for Deep Regression
• Systematically Constructing the Likelihood for Boosted $H\to gg$ Decays
• Artificial intelligence for representing and characterizing quantum systems
• Hybrid Fourier Neural Operator-Plasma Fluid Model for Fast and Accurate Multiscale Simulations of High Power Microwave Breakdown
• Interpretable machine-learning identification of the crossover from subradiance to superradiance in an atomic array
• Machine learning of the well known things
• Gradient dynamics in reinforcement learning
• Simplified derivations for high-dimensional convex learning problems
• Rediscovery of Numerical Lüscher's Formula from the Neural Network
• Randomized-gauge test for machine learning of Ising model order parameter
• Machine learning topological energy braiding of non-Bloch bands
• Deep Learning Transient Detection with VERITAS
• Double descent in quantum kernel methods
• Spin-polarized scanning tunneling microscopy measurement scheme for determining the quantum geometric tensor
• Appearance of Random Matrix Theory in Deep Learning
• Learning topological defects formation with neural networks in a quantum phase transition
• Variational Transformer Ansatz for the Density Operator of Steady States in Dissipative Quantum Many-Body Systems
• Semileptonic decays of heavy mesons with artificial neural networks
• A method for quantifying the generalization capabilities of generative models for solving Ising models
• Predicting Many Crystal Properties via an Adaptive Transformer-based Framework
• Beyond holography: the entropic quantum gravity foundations of image processing
• Machine learning that predicts well may not learn the correct physical descriptions of glassy systems
• Electrostatic Discovery Atomic Force Microscopy
• Programming with Neural Surrogates of Programs
• Learning Insulin-Glucose Dynamics in the Wild
• Discovering quasiorder parameters in the Potts model: A bridge between machine learning and critical phenomena
• Largest Eigenvalues of the Conjugate Kernel of Single-Layered Neural Networks
• Relevant Analytic Spontaneous Magnetization Relation for the Face-Centered-Cubic Ising Lattice
• Femtosecond pulse parameter estimation from photoelectron momenta using machine learning
• Global exploration of phase behavior in frustrated Ising models using unsupervised learning techniques
• On the capacity of a quantum perceptron for storing biased patterns
• Inferring Local Structure from Pairwise Correlations
• Predicting Quantum Potentials by Deep Neural Network and Metropolis Sampling
• Nonequilibrium thermodynamics of self-supervised learning
• Physical meaning of principal component analysis for classical lattice systems with translational invariance
• Fast estimation of the look-elsewhere effect using Gaussian random fields
• Seeding neural network quantum states with tensor network states
• Suppression of accidental backgrounds with deep neural networks in the PandaX-II experiment
• Efficiency of neural-network state representations of one-dimensional quantum spin systems
• Analysis of Kohn-Sham Eigenfunctions Using a Convolutional Neural Network in Simulations of the Metal-insulator Transition in Doped Semiconductors
• Noise Balance and Stationary Distribution of Stochastic Gradient Descent
• Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning
• Machine-learning semi-local density functional theory for many-body lattice models at zero and finite temperature
• Robustness of optimized numerical estimation schemes for noisy variational quantum algorithms
• Using Slisemap to interpret physical data
• Many-Body Neural Network Wavefunction for a Non-Hermitian Ising Chain
• Dreaming up scale invariance via inverse renormalization group
• Dimensional Reduction of Dynamical Systems by Machine Learning: Automatic Generation of the Optimum Extensive Variables and Their Time-Evolution Map
• Learning the dynamics of Markovian open quantum systems from experimental data
• Building bulk from Wilson loops
• Cosmological parameter estimation from large-scale structure deep learning
• A phase microscope for quantum gases
• Mismatching as a tool to enhance algorithmic performances of Monte Carlo methods for the planted clique model
• Gaussian Processes for Finite Size Extrapolation of Many-Body Simulations
• Machine Learning for Discovering Effective Interaction Kernels between Celestial Bodies from Ephemerides
• Challenges and opportunities in the supervised learning of quantum circuit outputs
• Quantum Atomic Matter Near Two-Dimensional Materials in Microgravity
• A deep learning approach to the texture optimization problem for friction control in lubricated contacts
• The statistical mechanics and machine learning of the $\alpha$-Rényi ensemble
• Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning
• Learning phase transitions in ferrimagnetic GdFeCo alloys
• Photonic quantum Hall effects
• Snake net and balloon force with a neural network for detecting multiple phases
• Casimir energy for $N$ constant conductivity $\delta$-plates with a neural network perception
• A study of first-passage time minimization via Q-learning in heated gridworlds
• Improving the performance of fermionic neural networks with the Slater exponential Ansatz
• Machine Learning out of equilibrium correlations in the Bose-Hubbard model
• Dynamical symmetry breaking through AI: The dimer self-trapping transition
• Analysis of Neural Network Predictions for Entanglement Self-Catalysis
• How to use Machine Learning to improve the discrimination between signal and background at particle colliders
• Probabilistic Autoencoder using Fisher Information
• Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure
• Machine learnt approximations to the bridge function yield improved closures for the Ornstein-Zernike equation
• Searching for a signature of turnaround in galaxy clusters with convolutional neural networks
• Learning Langevin dynamics with QCD phase transition
• Energy Reconstruction of Non-fiducial Electron-Positron Events in the DAMPE Experiment Using Convolutional Neural Networks
• Data-driven prediction of reversal of large-scale circulation in turbulent convection
• Semi-supervised physics guided deep learning framework for predicting the I-V characteristics of GAN HEMT
• Phase classification using neural networks: application to supercooled, polymorphic core-softened mixtures
• Uncertainty in AI-driven Monte Carlo simulations
• Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection
• Hamiltonian parameter inference from resonant inelastic x-ray scattering with active learning
• Dipolar optimal control of entangled current states
• Projection-based multifidelity linear regression for data-scarce applications
• Binary Classification of Light and Dark Time Traces of a Transition Edge Sensor Using Convolutional Neural Networks
• Shedding Light on Dark Matter at the LHC with Machine Learning
• Learning Minimal Representations of Fermionic Ground States
• Determining crucial factors for the popularity of scientific articles
• Extraction of the color dipole amplitude with physics-informed neural networks
• Quantum Phase Recognition via Quantum Attention Mechanism
• Prediction of the Hubbard U parameter from scanning tunneling microscopy images of moire systems using image recognition
• Bosonic and fermionic mutual information of N-partite systems in dilaton black hole background
• Neural Networks and Schramm-Loewner Evolutions
• Machine Learning of Quantum Entanglement from Noisy Measurements
• Interpretable liquid crystal phase classification via two-by-two ordinal patterns
• Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
• Channel-noise tracking for sub-shot-noise-limited receivers with neural networks
• An Incremental MaxSAT-based Model to Learn Interpretable and Balanced Classification Rules
• Ab-initio Quantum Monte Carlo study of ultracold atomic mixtures
• Experimental implementation of universal holonomic quantum computation on solid-state spins with optimal control
• Efficient bit encoding of neural networks for Fock states
• New Metric Formulas that Include Measurement Errors in Machine Learning for Natural Sciences
• Neural-Network Quantum States for Periodic Systems in Continuous Space
• Towards Exascale Design of Soft Mesoscale Materials
• Data-Driven Continuum Dynamics via Transport-Teleport Duality
• Toward Building Science Discovery Machines
• Tradeoff of generalization error in unsupervised learning
• A deep learning approach to resonant light transmission through single subwavelength apertures
• Emulating ultrafast dissipative quantum dynamics with deep neural networks
• Another Unorthodox Introduction to QCD and now Machine Learning
• Principle of learning sign rules by neural networks in qubit lattice models
• An almost deterministic cooling by measurements
• Teaching Machine Learning for the Physical Sciences: A summary of lessons learned and challenges
• Global sampling of Feynman's diagrams through Normalizing Flow
• Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive Bias