Solving the quantum many-body problem with artificial neural networks

Cited by (1,000)

Items 401–500 of 1,000

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3. Machine learning \& artificial intelligence in the quantum domain
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4. Learning Entropy Production via Neural Networks
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9. Detecting multipartite entanglement structure with minimal resources
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10. Machine learning assisted readout of trapped-ion qubits
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11. Quantum-Enhanced Data Classification with a Variational Entangled Sensor Network
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12. Combining dynamical quantum typicality and numerical linked cluster expansions
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13. Investigating ultrafast quantum magnetism with machine learning
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14. Deep reinforcement learning for quantum multiparameter estimation
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17. Simulations meet machine learning in structural biology
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20. The 2021 quantum materials roadmap
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21. Principal component analysis for fermionic critical points
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22. Nonlinear Network Description for Many-Body Quantum Systems in Continuous Space
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23. Quantum machine learning
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24. Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions
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25. Transfer learning for scalability of neural-network quantum states
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26. Matrix product operators for sequence-to-sequence learning
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27. Presence and Absence of Barren Plateaus in Tensor-Network Based Machine Learning
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28. Deep learning of quantum entanglement from incomplete measurements
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29. Artificial intelligence for quantum computing
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30. Exciton Mott transition revisited
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31. Learnability scaling of quantum states: Restricted Boltzmann machines
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32. Interpreting machine learning of topological quantum phase transitions
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33. Breaking adiabatic quantum control with deep learning
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34. Markovian Quantum Neuroevolution for Machine Learning
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35. Hidden-nucleons neural-network quantum states for the nuclear many-body problem
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36. Holographic local quench and effective complexity
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37. Thermodynamics and feature extraction by machine learning
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38. Improving the dynamics of quantum sensors with reinforcement learning
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39. Quantum-inspired machine learning on high-energy physics data
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40. Simulating Hydrodynamics on Noisy Intermediate-Scale Quantum Devices with Random Circuits
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41. Holography as deep learning
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42. Transfer-Learning-Based Coarse-Graining Method for Simple Fluids: Toward Deep Inverse Liquid-State Theory
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43. All-optical diffractive neural networked terahertz hologram
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44. Photonic Inverse Design with Neural Networks: The Case of Invisibility in the Visible
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45. Riemannian geometry and automatic differentiation for optimization problems of quantum physics and quantum technologies
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46. Branching quantum convolutional neural networks
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47. Adaptable Hamiltonian neural networks
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48. Reinforcement Learning and Physics
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49. Optimizing design choices for neural quantum states
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50. Walled Buildings, Sustainability, and Housing Prices: An Artificial Neural Network Approach
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51. Solving quasiparticle band spectra of real solids using neural-network quantum states
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52. Two-Layer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory
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53. Quantum codes from neural networks
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54. Scalability of All-Optical Neural Networks Based on Spatial Light Modulators
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55. Neural-network quantum states for many-body physics
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56. Machine learning density functional theory for the Hubbard model
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57. Deep learning-enhanced variational Monte Carlo method for quantum many-body physics
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58. Predicting quantum advantage by quantum walk with convolutional neural networks
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59. Autonomous Tuning and Charge-State Detection of Gate-Defined Quantum Dots
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60. Nanophotonic particle simulation and inverse design using artificial neural networks
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61. Machine learning assisted vector atomic magnetometry
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62. A Quantum Many-body Wave Function Inspired Language Modeling Approach
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63. Towards novel insights in lattice field theory with explainable machine learning
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64. Machine-learning models for Raman spectra analysis of twisted bilayer graphene
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65. Machine learned Hückel theory: Interfacing physics and deep neural networks
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66. Vector field divergence of predictive model output as indication of phase transitions
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67. Scalable neural quantum states architecture for quantum chemistry
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68. Exploring cluster Monte Carlo updates with Boltzmann machines
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69. Method to Solve Quantum Few-Body Problems with Artificial Neural Networks
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70. Automated assignment of rotational spectra using artificial neural networks
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71. Learning the Ising model with generative neural networks
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72. Ultrafast laser ablation simulator using deep neural networks
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73. Pseudo-fermion functional renormalization group for spin models
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74. Improved machine learning algorithm for predicting ground state properties
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75. Interpretable machine learning study of the many-body localization transition in disordered quantum Ising spin chains
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76. Variational quantum algorithms for dimensionality reduction and classification
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77. Nelder–Mead Optimization of Elastic Metamaterials via Machine-Learning-Aided Surrogate Modeling
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78. Gauge Equivariant Neural Networks for Quantum Lattice Gauge Theories
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79. Scaling of temporal entanglement in proximity to integrability
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81. Imaginary components of out-of-time-order correlator and information scrambling for navigating the learning landscape of a quantum machine learning model
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84. Application of artificial neural network model based on GIS in geological hazard zoning
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85. Mapping distinct phase transitions to a neural network
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86. Machine Learning Magnetic Parameters from Spin Configurations
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87. Quantum computing for chemistry and physics applications from a Monte Carlo perspective
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88. Large-scale quantum approximate optimization on nonplanar graphs with machine learning noise mitigation
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89. Neural-network-designed pulse sequences for robust control of singlet-triplet qubits
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90. Applications of neural networks to the simulation of dynamics of open quantum systems
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92. Machine learning approach to dynamical properties of quantum many-body systems
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98. Prediction of nuclear charge density distribution with feedback neural network
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99. A computational framework for neural network-based variational Monte Carlo with Forward Laplacian
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