- Artificial Intelligence
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- CS3243 Foundations Of Artificial Intelligence
- CMSC471/671 Artificial Intelligence
- Introduction to Neural Networks and Deep Learning
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- Mathematics for Machine Learning
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- CS725: Foundations of Machine learning - Lecture Notes
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- Introduction to Artificial Intelligence
- Machine Learning and Data Mining Lecture Notes
- Artificial Intelligence - IIT
- Statistical Machine Learning
- Lecture Notes in Machine Learning
- Introduction to AI and Intelligent Systems
- Machine Learning by Andrew NG Lecture Notes
- Computing Machinery And Intelligence
- Concise Machine Learning
- Artificial Intelligence - MIT
- Artificial Intelligence Lecture Notes
- Lecture Notes in Artificial Intelligence: Contemporary Knowledge Engineering and Cognition
- Artificial Intelligence (Künstliche Intelligenz) Lecture Notes
- 15-381: Artificial Intelligence - Introduction and Overview
- Python code for Artificial Intelligence: Foundations of Computational Agents
- Introduction to Computer Vision
- Deep Learning
- The Robot and I: How New Digital Technologies Are Making Smart People and Businesses Smarter by Automating Rote Work
- Artificial Intelligence And Life In 2030
- Research Priorities for Robust and Beneficial Artificial Intelligence
- Artificial Intelligence as a Positive and Negative Factor in Global Risk
- Artificial Intelligence, Robotics, Privacy and Data Protection
- Machine Learning Lectures
- Artificial Intelligence and Human Thinking
- Artificial Intelligence and its Role in Near Future
- Future Progress in Artificial Intelligence: A Survey of Expert Opinion
- AI | Professor John McCarthy
- Artificial Intelligence: Adversarial Search
- Artificial Intelligence: Constraint Satisfaction Problems
- Artificial Intelligence: Intelligent Agents
- Artificial Intelligence: Logical Agents
- Artificial Intelligence: Machine Learning Association Rules
- Classification
- Artificial Intelligence: Machine Learning Ensemble Methods
- Machine Learning: Linear Models
- Artificial Intelligence: Machine Learning Logistic Regression
- Artificial Intelligence: Machine Learning Naive Bayes
- Artificial Intelligence: Machine Learning Neural Networks
- Artificial Intelligence: Machine Learning Tree classifiers
- Artificial Intelligence: Machine Learning Unsupervised learning
- Artificial Intelligence: Natural Language Processing
- Artificial Intelligence: Search Agents
- Artificial Intelligence: Search Agents Uninformed search
- Artificial Intelligence: Search Agents Informed search
- Artificial Intelligence: Ethics, governance and policy challenges
- Artificial Intelligence and Robotics and Their Impact on the Workplace
- Artificial Intelligence and the Future of Defense
- Artificial intelligence (AI) in healthcare and research
- Artificial intelligence in India – hype or reality Impact of artificial intelligence across industries and user groups
- Core Python: Creating Beautiful Code with an Interpreted, Dynamically Typed Language
- Data Mining and Statistics: What's the Connection?
- Essay: Artificial Intelligence: Boon or Bane?
- Machine Learning that Matters
- Robot Path Planning
- Preparing for the Future of Artificial Intelligence
- The Quest for Artificial Intelligence: A History of Ideas and Achievements
- Regulation of Artificial Intelligence in Selected Jurisdictions
- Reinforcement Learning
- The Artificial Intelligence Black Box and the Failure of Intent and Causation
- The economics of artificial intelligence: Implications for the future of work
- Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation
- Network Dissection: Quantifying Interpretability of Deep Visual Representations
- Interpretable Explanations of Black Boxes by Meaningful Perturbation
- A brief survey of visualization methods for deep learning models from the perspective of Explainable AI.
- Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks
- Neural network inversion beyond gradient descent
- NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models
- Towards Robust Interpretability with Self-Explaining Neural Networks
- Axiomatic Attribution for Deep Networks
- Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks
- Explainable Artificial Intelligence via Bayesian Teaching
- Understanding Neural Networks Through Deep Visualization
- Visualizing and Understanding Convolutional Networks
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
- Striving for Simplicity: The All Convolutional Net
- "Why Should I Trust You?": Explaining the Predictions of Any Classifier
- Not Just A Black Box: Learning Important Features Through Propagating Activation Differences
- A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks
- Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
- Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations
- Towards A Rigorous Science of Interpretable Machine Learning
- Methods for Interpreting and Understanding Deep Neural Networks
- Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
- Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks
- Understanding Deep Architectures by Visual Summaries
- A Survey Of Methods: For Explaining Black Box Models
- Neural Network Interpretation via Fine-Grained Textual Summarization
- How Important Is a Neuron?
- Explaining Explanations: An Overview of Interpretability of Machine Learning
- RISE: Randomized Input Sampling for Explanation of Black-box Models
- Explaining Image Classifiers by Counterfactual Generation
- Diverse feature visualizations reveal invariances in early layers of deep neural networks
- Regional Multi-scale Approach for Visually Pleasing Explanations of Deep Neural Networks
- Improving the Interpretability of Deep Neural Networks with Knowledge Distillation
- Interpretable machine learning: definitions, methods, and applications
- On the (In)fidelity and Sensitivity of Explanations
- Interpreting Black Box Models via Hypothesis Testing
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- Understanding Neural Networks via Feature Visualization: A survey
- Visualizing Deep Networks by Optimizing with Integrated Gradients
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- Leibniz: Explanation of Binary Arithmetic (1703)
- The Mathematical Analysis of Logic
- On formally undecidable propositions of Principia Mathematica and related systems I
- On Computable Numbers, with an Application to the Entscheidungsproblem
- A Logical Calculus of the Ideas Immanent in Nervous Activity
- The First Draft of a Report on the EDVAC
- Computing Machinery and Intelligence
- Statistical and inductive probability
- Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I
- Steps Toward Artificial Intelligence
- The Working Set Model for Program
- Human problem solving: The state of the theory in 1970
- Computer Science as Empirical Inquiry: Symbols and Search
- The Knowledge Level
- Parallel Distributed Processing, Explorations in the microstructure of Cognition, Volume 1: Foundations
- Intelligence without representation
- Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization
- Gradient-Based Learning Applied to Document Recognition
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- A Neural Probabilistic Language Model
- Deep Learning of Representations: Looking Forward
- Learning Deep Architectures for AI
- Wikipedia-based Semantic Interpretation for Natural Language Processing
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- Playing Atari with Deep Reinforcement Learning
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- Human-level control through deep reinforcement learning
- A (Very) Brief History of Artificial Intelligence
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- Machine Learning In Self-driving Tesla Automobiles
- Human Side of Tesla Autopilot: Exploration of Functional Vigilance in Real-World Human-Machine Collaboration
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- Common LISP: A Gentle Introduction to Symbolic Computation
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- A Course in Machine Learning
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- Deep Learning with R
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- From Bricks to Brains: The Embodied Cognitive Science of LEGO Robots
- Logic For Computer Science: Foundations of Automatic Theorem Proving
- Life 3.0: Being Human in the Age of Artificial Intelligence
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- Artificial Intelligence: A Modern Approach
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- The Quest for Artificial Intelligence: A History of Ideas and Achievements
- Simply Logical: Intelligent Reasoning by Example
- Superintelligence
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- Quantum algorithms for supervised and unsupervised machine learning
- Understanding Machine-learned Density Functionals
- Tensor Networks for Big Data Analytics and Large-Scale Optimization Problems
- An exact mapping between the Variational Renormalization Group and Deep Learning
- Automated Search for new Quantum Experiments
- Quantum gate learning in qubit networks: Toffoli gate without time dependent control
- Quantum Boltzmann Machine
- Machine learning phases of matter
- Exponential Machines
- Supervised Learning With Quantum-Inspired Tensor Networks
- Discovering Phase Transitions with Unsupervised Learning
- Solving the Quantum Many-Body Problem with Artificial Neural Networks
- Learning Thermodynamics with Boltzmann Machines
- Machine learning quantum phases of matter beyond the fermion sign problem
- Why does deep and cheap learning work so well?
- Low-Rank Tensor Networks for Dimensionality Reduction and Large-Scale Optimization Problems: Perspectives and Challenges PART 1
- Machine Learning Phases of Strongly Correlated Fermions
- By-passing the Kohn-Sham equations with machine learning
- Pure density functional for strong correlations and the thermodynamic limit from machine learning
- Machine learning topological states
- Learning phase transitions by confusion
- Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines
- Self-Learning Monte Carlo Method
- A Neural Decoder for Topological Codes
- Quantum Loop Topography for Machine Learning
- Sampling algorithms for validation of supervised learning models for Ising-like systems
- Quantum Machine Learning
- Self-Learning Monte Carlo Method in Fermion Systems
- Tomography and Generative Data Modeling via Quantum Boltzmann Training
- Reinforcement Learning Using Quantum Boltzmann Machines
- Restricted Boltzmann Machines for the Long Range Ising Models
- Equivalence of restricted Boltzmann machines and tensor network states
- Quantum Entanglement in Neural Network States
- Efficient Representation of Quantum Many-body States with Deep Neural Networks
- Neural network representation of tensor network and chiral states
- Opening the black box of Deep Neural Networks via Information
- Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders
- Neural-network quantum state tomography for many-body systems
- Experimental Quantum Hamiltonian Learning
- Discovering Phases, Phase Transitions and Crossovers through Unsupervised Machine Learning: A critical examination
- Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design
- Probing many-body localization with neural networks
- Approximating quantum many-body wave-functions using artificial neural networks
- Deterministic Quantum Annealing Expectation-Maximization Algorithm
- Kernel methods for interpretable machine learning of order parameters
- Mutual Information, Neural Networks and the Renormalization Group
- Nonequilibrium Thermodynamics of Restricted Boltzmann Machines
- Reinforcement Learning in Different Phases of Quantum Control
- Decoding Small Surface Codes with Feedforward Neural Networks
- A Separability-Entanglement Classifier via Machine Learning
- Machine Learning Z2 Quantum Spin Liquids with Quasi-particle Statistics
- Construction of Hamiltonians by supervised learning of energy and entanglement spectra
- Machine Learning of Explicit Order Parameters: From the Ising Model to SU(2) Lattice Gauge Theory
- Self-Learning Monte Carlo Method: Continuous-Time Algorithm
- Machine-learning-assisted correction of correlated qubit errors in a topological code
- Criticality and Deep Learning II: Momentum Renormalisation Group
- Active learning machine learns to create new quantum experiments
- Unsupervised Learning of Frustrated Classical Spin Models I: Principle Component Analysis
- Self-Learning Phase Boundaries by Active Contours
- Inverse Ising inference by combining Ornstein-Zernike theory with deep learning
- Deep neural networks for direct, featureless learning through observation: the case of 2d spin models
- Quantum phase recognition via unsupervised machine learning
- Learning the Einstein-Podolsky-Rosen correlations on a Restricted Boltzmann Machine
- Quantum dynamics in transverse-field Ising models from classical networks
- Quantum machine learning: a classical perspective
- Solving the Bose-Hubbard model with machine learning
- Spectral Dynamics of Learning Restricted Boltzmann Machines
- Deep Learning the Ising Model Near Criticality
- Learning Fermionic Critical Points
- Extensive deep neural networks
- Machine Learning Topological Invariants with Neural Networks
- Phase Diagrams of Three-Dimensional Anderson and Quantum Percolation Models using Deep Three-Dimensional Convolutional Neural Network
- Machine Learning Spatial Geometry from Entanglement Features
- Unsupervised Generative Modeling Using Matrix Product States
- Identifying Product Order with Restricted Boltzmann Machines
- Machine learning and artificial intelligence in the quantum domain
- Learning Disordered Topological Phases by Statistical Recovery of Symmetry
- Restricted-Boltzmann-Machine Learning for Solving Strongly Correlated Quantum Systems
- Quantum Autoencoders via Quantum Adders with Genetic Algorithms
- Generalized Quantum Reinforcement Learning with Quantum Technologies
- Combining Machine Learning and Physics to Understand Glassy Systems
- Enhanced Quantum Synchronization via Quantum Machine Learning
- Learning hard quantum distributions with variational autoencoders
- Mean-field theory of input dimensionality reduction in unsupervised deep neural networks
- Neural Networks Quantum States, String-Bond States and chiral topological states
- Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures
- Entanglement Entropy of Target Functions for Image Classification and Convolutional Neural Network
- A Correspondence Between Random Neural Networks and Statistical Field Theory
- Learning Hidden Quantum Markov Models
- Machine learning vortices at the Kosterlitz-Thouless transition
- Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
- Machine learning out-of-equilibrium phases of matter
- An efficient quantum algorithm for generative machine learning
- Hardening Quantum Machine Learning Against Adversaries
- Experimental learning of quantum states
- Deep Neural Network Detects Quantum Phase Transition
- Towards reduction of autocorrelation in HMC by machine learning
- Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines
- A Quantum Extension of Variational Bayes Inference
- A quantum algorithm to train neural networks using low-depth circuits
- Pattern recognition techniques for Boson Sampling validation
- Learning Relevant Features of Data with Multi-scale Tensor Networks
- A relativistic extension of Hopfield neural networks via the mechanical analogy
- Generative Models for Stochastic Processes Using Convolutional Neural Networks
- Scale-invariant Feature Extraction of Neural Network and Renormalization Group Flow
- Experimentally Detecting a Quantum Change Point via Bayesian Inference
- Leveraging Adiabatic Quantum Computation for Election Forecasting
- Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification
- Critical Percolation as a Framework to Analyze the Training of Deep Networks
- Neural Network Renormalization Group
- Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control
- Reinforcement Learning with Neural Networks for Quantum Feedback
- Learning DNFs under product distributions via μ–biased quantum Fourier sampling
- Inferring relevant features: from QFT to PCA
- Quantum Variational Autoencoder
- The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity
- Deep neural decoders for near term fault-tolerant experiments
- Advantages of versatile neural-network decoding for topological codes
- Online Learning of Quantum States
- Parameter diagnostics of phases and phase transition learning by neural networks
- Universal Quantum Control through Deep Reinforcement Learning
- Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning
- Thermodynamics of Restricted Boltzmann Machines and Related Learning Dynamics
- Measurement-based adaptation protocol with quantum reinforcement learning
- Vulnerability of Deep Learning
- Comparing Dynamics: Deep Neural Networks versus Glassy Systems
- Extrapolating quantum observables with machine learning: Inferring multiple phase transitions from properties of a single phase
- A high-bias, low-variance introduction to Machine Learning for physicists
- Deep Learning Phase Segregation
- Learning architectures based on quantum entanglement: a simple matrix product state algorithm for image recognition
- Bridging Many-Body Quantum Physics and Deep Learning via Tensor Networks
- Quantum algorithms for training Gaussian Processes
- Matrix Product Operators for Sequence to Sequence Learning
- Protection against Cloning for Deep Learning
- Barren plateaus in quantum neural network training landscapes
- Learning quantum models from quantum or classical data
- Towards Quantum Machine Learning with Tensor Networks
- A note on state preparation for quantum machine learning
- Circuit-centric quantum classifiers
- Classical Verification of Quantum Computations
- Smallest Neural Network to Learn the Ising Criticality
- Quantum Machine Learning Matrix Product States
- The Loss Surface of XOR Artificial Neural Networks
- Complex energy landscapes in spiked-tensor and simple glassy models: ruggedness, arrangements of local minima and phase transitions
- Machine learning of phase transitions in the percolation and XY models
- Neural network decoder for topological color codes with circuit level noise
- Variational quantum simulation of imaginary time evolution with applications in chemistry and beyond
- Strawberry Fields: A Software Platform for Photonic Quantum Computing
- Supervised machine learning algorithms based on generalized Gibbs ensembles
- Hierarchical quantum classifiers
- Differentiable Learning of Quantum Circuit Born Machine
- Optimizing a Polynomial Function on a Quantum Simulator
- Classicalization Clearly: Quantum Transition into States of Maximal Memory Storage Capacity
- Method to solve quantum few-body problems with artificial neural networks
- A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems
- Learning non-Higgsable gauge groups in 4D F-theory
- A Simple Quantum Neural Net with a Periodic Activation Function
- Quantum codes from classical graphical models
- Machine learning assisted readout of trapped-ion qubits
- Probing Hidden Spin Order with Interpretable Machine Learning
- Quantum generative adversarial networks
- Quantum generative adversarial learning
- Supervised learning with quantum enhanced feature spaces
- Multiparameter optimisation of a magneto-optical trap using deep learning
- Neural networks as Interacting Particle Systems: Asymptotic convexity of the Loss Landscape and Universal Scaling of the Approximation Error
- Optimal universal learning machines for quantum state discrimination
- Quantum artificial intelligence to simulate many body quantum systems
- Identifying topological order via unsupervised machine learning
- Physically optimizing inference
- Universal discriminative quantum neural networks
- Quantum classification of the MNIST dataset via Slow Feature Analysis
- Entropy and mutual information in models of deep neural networks
- Deep Learning Topological Invariants of Band Insulators
- Bayesian Quantum Circuit
- Machine learning many-body localization: Search for the elusive nonergodic metal
- Automated discovery of characteristic features of phase transitions in many-body localization
- Adversarial quantum circuit learning for pure state approximation
- Machine learning of quantum phase transitions
- Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach
- Machine-learning Skyrmions
- Analytic continuation via 'domain-knowledge free' machine learning
- Supervised learning with generalized tensor networks
- Continuous-variable quantum neural networks
- Decoherence in a quantum neural network
- Quantum Kitchen Sinks: An algorithm for machine learning on near-term quantum computers
- Quantum Codes from Neural Networks
- Artificial Quantum Neural Network: quantum neurons, logical elements and tests of convolutional nets
- A Universal Training Algorithm for Quantum Deep Learning
- Optimization of neural networks via finite-value quantum fluctuations
- Adversarial training of quantum Born machine
- Interpretable Machine Learning for Inferring the Phase Boundaries in a Non-equilibrium System
- Benchmarking Neural Networks For Quantum Computations
- Symmetries and many-body excited states with neural-network quantum states
- Neural networks as "hidden" variable models for quantum systems
- Recurrent neural networks running on quantum spins: memory accuracy and capacity
- TherML: Thermodynamics of Machine Learning
- The fundamentals of quantum machine learning
- An introductory example of machine learning enhanced global optimization
- Self-learning Monte Carlo method with Behler-Parrinello neural networks
- Many-body (de)localization in large quantum chains
- irbasis: Open-source database and software for intermediate-representation basis functions of imaginary-time Green's function
- Solving Many-Electron Schrodinger Equation Using Deep Neural Networks
- Local-measurement-based quantum state tomography via neural networks
- Machine learning study of the relationship between the geometric and entropy discord
- Solving frustrated quantum many-particle models with convolutional neural networks
- Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers
- Discovering physical concepts with neural networks
- Experimental Implementation of a Quantum Autoencoder via Quantum Adders
- Quantized Hodgkin-Huxley Model for Quantum Neurons
- Backflow Transformations via Neural Networks for Quantum Many-Body Wave-Functions
- Machine learning method for state preparation and gate synthesis on photonic quantum computers
- Unsupervised machine learning for detection of phase transitions in off-lattice systems I. Foundations
- Geometry of energy landscapes and the optimizability of deep neural networks
- From Bloch Oscillations to Many Body Localization in Clean Interacting Systems
- Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks
- Machine Learning Phase Transition: An Iterative Methodology
- Generalized Transfer Matrix States from Artificial Neural Networks
- Quantum Lyapunov control with machine learning
- Quantum generative adversarial learning in a superconducting quantum circuit
- Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks
- Learning and Inference on Generative Adversarial Quantum Circuits
- Model Reduction with Memory and the Machine Learning of Dynamical Systems
- A machine-learning solver for modified diffusion equations
- Geometry and symmetry in quantum Boltzmann machine
- CosmoFlow: Using Deep Learning to Learn the Universe at Scale
- Neural-network states for the classical simulation of quantum computing
- Machine Learning Configuration Interaction
- Artificial Neural Networks in Fluid Dynamics: A Novel Approach to the Navier-Stokes Equations
- Smart energy models for atomistic simulations using a DFT-driven multifidelity approach
- Machine learning non-local correlations
- Statistical Neurodynamics of Deep Networks: Geometry of Signal Spaces
- The effect of retardation in the random networks of excitable nodes embeddable in the Euclidean space
- Realizing quantum linear regression with auxiliary qumodes
- Quantum enhanced cross-validation for near-optimal neural networks architecture selection
- Policy Guided Monte Carlo: Reinforcement Learning Markov Chain Dynamics
- Reconstruction of a Photonic Qubit State with Quantum Reinforcement Learning
- Quantum optical neural networks
- Deep learning, quantum chaos, and pseudorandom evolution
- A Quantum Model for Multilayer Perceptron
- Quantum Neural Network States
- Simple coarse graining and sampling strategies for image recognition
- GANs for generating EFT models
- Approaching the adiabatic timescale with machine-learning
- Projective quantum Monte Carlo simulations guided by unrestricted neural network states
- Supervised machine learning of ultracold atoms with speckle disorder
- Identifying Quantum Phase Transitions using Artificial Neural Networks on Experimental Data
- Implementable Quantum Classifier for Nonlinear Data
- Neural Network Decoders for Large-Distance 2D Toric Codes
- The jamming transition as a paradigm to understand the loss landscape of deep neural networks
- Phase Diagram of Disordered Higher Order Topological Insulator: a Machine Learning Study
- A machine learning approach to the Berezinskii-Kosterlitz-Thouless transition in classical and quantum models
- Monge-Ampère Flow for Generative Modeling
- Self-organizing maps as a method for detecting phase transitions and phase identification
- Solving Statistical Mechanics using Variational Autoregressive Networks
- Deep learning systems as complex networks
- Extracting many-particle entanglement entropy from observables using supervised machine learning
- An Artificial Neural Network Approach to the Analytic Continuation Problem
- A quantum autoencoder: the compression of qutrits via machine learning
- Efficient Representation of Topologically Ordered States with Restricted Boltzmann Machines
- Super-resolving the Ising model with convolutional neural networks
- Quantum Convolutional Neural Networks
- Quantum Neural Network and Soft Quantum Computing
- Learning multiple order parameters with interpretable machines
- Nonequilibrium fluctuations of a driven quantum heat engine via machine learning
- Topographic Representation for Quantum Machine Learning
- Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation
- Thermodynamics and Feature Extraction by Machine Learning
- A jamming transition from under- to over-parametrization affects loss landscape and generalization
- Emulating quantum computation with arti cial neural networks
- The Role of Data in Model Building and Prediction: A Survey Through Examples
- Toward an AI Physicist for Unsupervised Learning
- Reconstructing quantum states with generative models
- Quantum data compression by principal component analysis
- Free energies of Boltzmann Machines: self-averaging, annealed and replica symmetric approximations in the thermodynamic limit
- Machine learning density functional theory for the Hubbard model
- Quantum advantage in training binary neural networks
- Quantum-inspired classical algorithms for principal component analysis and supervised clustering
- Deep Learning of Robust and High-Precision Quantum Controls
- Machine learning for molecular dynamics with strongly correlated electrons
- An Artificial Neuron Implemented on an Actual Quantum Processor
- Artificial neural networks for density-functional optimizations in fermionic systems
- Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
- Efficient prediction of 3D electron densities using machine learning
- Neural network state estimation for full quantum state tomography
- Experimental Simultaneous Learning of Multiple Non-Classical Correlations
- Neural Belief-Propagation Decoders for Quantum Error-Correcting Codes
- Advances in Quantum Reinforcement Learning
- Energy Levels of One Dimensional Anharmonic Oscillator via Neural Networks
- Deep Learning and Density Functional Theory
- fPINNs: Fractional Physics-Informed Neural Networks
- Physics-aware Deep Generative Models for Creating Synthetic Microstructures
- Developing a Bubble Chamber Particle Discriminator Using Semi-Supervised Learning
- Estimating of the inertial manifold dimension for a chaotic attractor of complex Ginzburg-Landau equation using a neural network
- Quantum error correction for the toric code using deep reinforcement learning
- Using a Recurrent Neural Network to Reconstruct Quantum Dynamics of a Superconducting Qubit from Physical Observations
- Variational optimization in the AI era: Computational Graph States and Supervised Wave-function Optimization
- Classifying Snapshots of the Doped Hubbard Model with Machine Learning
- Designing neural network based decoders for surface codes
- Quantum topology identi cation with deep neural networks and quantum walks
- Divergence of predictive model output as indication of phase transitions
- Designing quantum experiments with a genetic algorithm
- Phase transition encoded in neural network
- Approximating the solution to wave propagation using deep neural networks
- Boltzmann Generators – Sampling Equilibrium States of Many-Body Systems with Deep Learning
- Enhancing the efficiency of quantum annealing via reinforcement: A path-integral Monte Carlo simulation of the quantum reinforcement algorithm
- Quantum algorithms for feedforward neural networks
- A hybrid machine-learning algorithm for designing quantum experiments
- q-means: A quantum algorithm for unsupervised machine learning
- Adaptive Quantum State Tomography with Neural Networks
- Machine Learning as a universal tool for quantitative investigations of phase transitions
- Machine Learning for Optimal Parameter Prediction in Quantum Key Distribution
- Parameters optimization and real-time calibration of Measurement-Device-Independent Quantum Key Distribution Network based on BackPropagation Artificial Neural Network
- Optimizing Quantum Error Correction Codes with Reinforcement Learning
- Deep ToC: A New Method for Estimating the Solutions of PDEs
- Variational quantum simulation of general processes
- QuCumber: wavefunction reconstruction with neural networks
- Quantum algorithm and quantum circuit for A-Optimal Projection: dimensionality reduction
- Extrapolation of quantum observables with Gaussian processes
- The geometry of quantum learning
- Improved Bounds on Quantum Learning Algorithms