Solving the quantum many-body problem with artificial neural networks

Cited by (1,000)

Items 601–700 of 1,000

1. Efficient machine-learning representations of a surface code with boundaries, defects, domain walls, and twists
   Zhian Jia, Yuanhang Zhang, Yu-Chun Wu, Liang Kong, Guang‐Can Guo, Guo‐Ping Guo · 2019 · Physical Review A
2. Quantum neural networks to simulate many-body quantum systems
   Bartłomiej Gardas, Marek M. Rams, Jacek Dziarmaga · 2018 · Physical review. B./Physical review. B
3. Exact Real-Time Longitudinal Correlation Functions of the Massive XXZ Chain
   Constantin Babenko, Frank Göhmann, Karol Kajetan Kozlowski, Jesko Sirker, Junji Suzuki · 2021 · Physical Review Letters
4. Neural-network-based multistate solver for a static Schrödinger equation
   Hong Li, Qilong Zhai, Jeff Z. Y. Chen · 2021 · Physical Review A
5. MemComputing
   Massimiliano Di Ventra · 2022
6. Simulating excited states of the Lipkin model on a quantum computer
   Manqoba Q. Hlatshwayo, Yinu Zhang, Herlik Wibowo, Ryan LaRose, Denis Lacroix A, Елена Литвинова · 2022 · Physical Review C
7. Pinch-points to half-moons and up in the stars: The kagome skymap
   Dominik Kiese, Francesco Ferrari, Nikita Astrakhantsev, Nils Niggemann, Pratyay Ghosh, Tobias Müller, Ronny Thomale, Titus Neupert, Johannes Reuther, Michel J. P. Gingras et al. · 2023 · Physical Review Research
8. Machine Learning Quantum States — Extensions to Fermion–Boson Coupled Systems and Excited-State Calculations
   Yusuke Nomura · 2020 · Journal of the Physical Society of Japan
9. The Promise and Challenges of Quantum Computing for Energy Storage
   Alan Ho, Jarrod R. McClean, Shyue Ping Ong · 2018 · Joule
10. A complex junction recognition method based on GoogLeNet model
    Chengming Li, Honggang Zhang, Pengda Wu, Yong Yin, Sichao Liu · 2020 · Transactions in GIS
11. QGOpt: Riemannian optimization for quantum technologies
    I. A. Luchnikov, Alexander Ryzhov, Sergey N. Filippov, Henni Ouerdane · 2021 · SciPost Physics
12. Unitary Dynamics of Strongly Interacting Bose Gases with the Time-Dependent Variational Monte Carlo Method in Continuous Space
    Giuseppe Carleo, Lorenzo Cevolani, Laurent Sanchez-Palencia, Markus Holzmann · 2017 · Repository for Publications and Research Data (ETH Zurich)
13. Machine learning and serving of discrete field theories
    Hong Qin · 2020 · Scientific Reports
14. Machine-learning-assisted electron-spin readout of nitrogen-vacancy center in diamond
    Peng Qian, Xue Lin, Feifei Zhou, Runchuan Ye, Yunlan Ji, Bing Chen, Guangjun Xie, Nanyang Xu · 2021 · Applied Physics Letters
15. Scaling of Neural‐Network Quantum States for Time Evolution
    Sheng-Hsuan Lin, Frank Pollmann · 2021 · physica status solidi (b)
16. Role of stochastic noise and generalization error in the time propagation of neural-network quantum states
    Damian Hofmann, Giammarco Fabiani, Johan H. Mentink, Giuseppe Carleo, Michael A. Sentef · 2022 · SciPost Physics
17. Neural networks enforcing physical symmetries in nonlinear dynamical lattices: The case example of the Ablowitz–Ladik model
    Wei Zhu, Wesley Khademi, E. G. Charalampidis, P. G. Kevrekidis · 2022 · Physica D Nonlinear Phenomena
18. Determinant-free fermionic wave function using feed-forward neural networks
    Koji Inui, Yasuyuki Kato, Yukitoshi Motome · 2021 · Physical Review Research
19. Towards a transferable fermionic neural wavefunction for molecules
    Michael Scherbela, Leon Gerard, Philipp Grohs · 2024 · Nature Communications
20. Phase Diagrams of Three-Dimensional Anderson and Quantum Percolation Models Using Deep Three-Dimensional Convolutional Neural Network
    Tomohiro Mano, Tomi Ohtsuki · 2017 · Journal of the Physical Society of Japan
21. Extracting critical exponents by finite-size scaling with convolutional neural networks
    Zhenyu Li, Mingxing Luo, Xin Wan · 2019 · Physical review. B./Physical review. B
22. Stochastic approach to non-equilibrium quantum spin systems
    Stefano De Nicola, Benjamin Doyon, M. J. Bhaseen · 2018 · Journal of Physics A Mathematical and Theoretical
23. Self-learning Monte Carlo method with Behler-Parrinello neural networks
    Yuki Nagai, Masahiko Okumura, Akinori Tanaka · 2020 · Physical review. B./Physical review. B
24. Searching for Hidden Descriptors in the Metal–Ligand Bond through Statistical Analysis of Density Functional Theory (DFT) Results
    Oier Lakuntza, María Besora, Feliu Maseras · 2018 · Inorganic Chemistry
25. Searching magnetic states using an unsupervised machine learning algorithm with the Heisenberg model
    Hee Young Kwon, N. J. Kim, C K Lee, C. Won · 2019 · Physical review. B./Physical review. B
26. Hidden self-energies as origin of cuprate superconductivity revealed by machine learning
    Youhei Yamaji, T. Yoshida, Atsushi Fujimori, Masatoshi Imada · 2021 · Physical Review Research
27. Self-learning projective quantum Monte Carlo simulations guided by restricted Boltzmann machines
    Sebastiano Pilati, Estelle M. Inack, P. Pieri · 2019 · Physical review. E
28. Optimal Renormalization Group Transformation from Information Theory
    Patrick M. Lenggenhager, Doruk Efe Gökmen, Zohar Ringel, Sebastian D. Huber, Maciej Koch-Janusz · 2020 · Repository for Publications and Research Data (ETH Zurich)
29. Machine learning time-local generators of open quantum dynamics
    Paolo P. Mazza, Dominik Zietlow, Federico Carollo, Sabine Andergassen, Georg Martius, Igor Lesanovsky · 2021 · Physical Review Research
30. Mixed state entanglement classification using artificial neural networks
    Cillian Harney, Mauro Paternostro, Stefano Pirandola · 2021 · New Journal of Physics
31. Predicting quantum dynamical cost landscapes with deep learning
    Mogens Dalgaard, Felix Motzoi, Jacob Friis Sherson · 2022 · Physical Review A
32. Functional Electrolytes: Game Changers for Smart Electrochemical Energy Storage Devices
    Faxing Wang, Panpan Zhang, Gang Wang, Ali Shaygan Nia, Minghao Yu, Xinliang Feng · 2021 · Small Science
33. A perspective on machine learning and data science for strongly correlated electron problems
    Steven Johnston, Ehsan Khatami, Richard T. Scalettar · 2022 · Carbon Trends
34. Unsupervised and supervised learning of interacting topological phases from single-particle correlation functions
    Simone Tibaldi, Giuseppe Magnifico, Davide Vodola, Elisa Ercolessi · 2023 · SciPost Physics
35. Supervised learning of time-independent Hamiltonians for gate design
    Luca Innocenti, Leonardo Banchi, Alessandro Ferraro, Sougato Bose, Mauro Paternostro · 2020 · New Journal of Physics
36. Group Convolutional Neural Networks Improve Quantum State Accuracy
    Roth, Christopher, Allan H. MacDonald · 2021 · arXiv (Cornell University)
37. Learning nonequilibrium statistical mechanics and dynamical phase transitions
    Ying Tang, Jing Liu, Jiang Zhang, Pan Zhang · 2024 · Nature Communications
38. Neural-network Density Functional Theory Based on Variational Energy Minimization
    Yang Li, Zechen Tang, Zezhou Chen, Minghui Sun, Boheng Zhao, He Li, Honggeng Tao, Zilong Yuan, Wenhui Duan, Yong Xu · 2024 · Physical Review Letters
39. Unitary Dynamics of Strongly Interacting Bose Gases with the Time-Dependent Variational Monte Carlo Method in Continuous Space
    Giuseppe Carleo, Lorenzo Cevolani, Laurent Sanchez-Palencia, Markus Holzmann · 2017 · Physical Review X
40. Recognition of polymer configurations by unsupervised learning
    Xin Xu, Qianshi Wei, Huaping Li, Yuzhang Wang, Yuguo Chen, Ying Lan Jiang · 2019 · Physical review. E
41. Quantum quench dynamics in the transverse-field Ising model: A numerical expansion in linked rectangular clusters
    Jonas Richter, Tjark Heitmann, Robin Steinigeweg · 2020 · SciPost Physics
42. Conditional generative models for sampling and phase transition indication in spin systems
    Japneet Singh, Mathias S. Scheurer, Vipul Arora · 2021 · SciPost Physics
43. Protocol Discovery for the Quantum Control of Majoranas by Differentiable Programming and Natural Evolution Strategies
    Luuk Coopmans, Di Luo, Graham Kells, Bryan K. Clark, Juan Carrasquilla · 2021 · PRX Quantum
44. Generalized quantum master equation: A tutorial review and recent advances
    Dominikus Brian, Xiang Ying Sun · 2021 · Chinese Journal of Chemical Physics
45. Machine learning of two-dimensional spectroscopic data
    Mirta Rodrı́guez, Tobias Kramer · 2019 · Chemical Physics
46. Mitigating the Sign Problem through Basis Rotations
    Ryan Levy, Bryan K. Clark · 2021 · Physical Review Letters
47. Machine learning-based microarray analyses indicate low-expression genes might collectively influence PAH disease
    Song Cui, Qiang Wu, James D. West, Jiangping Bai · 2019 · PLoS Computational Biology
48. Solving quantum master equations with deep quantum neural networks
    Zidu Liu, Luming Duan, Dong-Ling Deng · 2022 · Physical Review Research
49. Investigating topological order using recurrent neural networks
    Hibat-Allah, Mohamed, Melko, Roger G., Carrasquilla, Juan · 2023 · Physical Review B
50. A fatigue crack growth prediction method on small datasets based on optimized deep neural network and Delaunay data augmentation
    Weixing Liang, Min Lou, Yu Wang, Chen Zhang, Shengwen Chen, Chengwei Cui · 2023 · Theoretical and Applied Fracture Mechanics
51. Few-shot machine learning in the three-dimensional Ising model
    Rui Zhang, Bin Wei, Dong Zhang, Jia-Ji Zhu, Kai Chang · 2019 · Physical review. B./Physical review. B
52. Rapid Flipping of Parametric Phase States
    Martin Frimmer, Toni Louis Heugel, Žiga Nosan, Felix Tebbenjohanns, David Hälg, Abdulkadir Akın, Christian L. Degen, Lukáš Novotný, R. Chitra, Oded Zilberberg et al. · 2019 · Physical Review Letters
53. Machine Prediction of Topological Transitions in Photonic Crystals
    Bei Wu, Kun Ding, Che Ting Chan, Yuntian Chen · 2020 · Physical Review Applied
54. Exact representations of many-body interactions with restricted-Boltzmann-machine neural networks
    Ermal Rrapaj, Alessandro Roggero · 2021 · Physical review. E
55. Estimating the degree of non-Markovianity using machine learning
    F. F. Fanchini, Göktuğ Karpat, Daniel Z. Rossatto, Ariel Norambuena, Raúl Coto · 2021 · Physical Review A
56. Many-body quantum states with exact conservation of non-Abelian and lattice symmetries through variational Monte Carlo
    Tom Vieijra, Jannes Nys · 2021 · Physical review. B./Physical review. B
57. Ab-Initio Study of Interacting Fermions at Finite Temperature with Neural Canonical Transformation
    Hao Xie, Linfeng Zhang, Lei Wang · 2022 · Journal of Machine Learning
58. Combined unitary and symmetric group approach applied to low-dimensional Heisenberg spin systems
    Werner Dobrautz, Vamshi M. Katukuri, Nikolay A. Bogdanov, Daniel Kats, Giovanni Li Manni, Ali Alavi · 2022 · Physical review. B./Physical review. B
59. Deep Reinforcement Learning With Reward Design for Quantum Control
    Haixu Yu, Xudong Zhao · 2022 · IEEE Transactions on Artificial Intelligence
60. Gauge covariant neural network for quarks and gluons
    Yuki Nagai, Akio Tomiya · 2025 · Physical review. D/Physical review. D.
61. Educational data mining for students' performance based on fuzzy C‐means clustering
    Yu Li, Jin Gou, Zongwen Fan · 2019 · The Journal of Engineering
62. Applications of neural networks to dynamics simulation of Landau-Zener transitions
    Bianjiang Yang, Baizhe He, Jiajun Wan, Sharvaj Kubal, Yang Zhao · 2019 · Chemical Physics
63. Integrating Machine Learning with Physics-Based Modeling
    E Weinan, Jiequn Han, Linfeng Zhang · 2020 · arXiv (Cornell University)
64. Nuclear density functional theory
    2018 · Physics Subject Headings (PhySH)
65. Hierarchical quantum circuit representations for neural architecture search
    Matt Lourens, Ilya Sinayskiy, Daniel K. Park, Carsten Blank, Francesco Petruccione · 2023 · npj Quantum Information
66. Deep reinforcement learning in chemistry: A review
    Bhuvanesh Sridharan, Animesh Sinha, Jai Bardhan, Rohit Modee, Masahiro Ehara, U. Deva Priyakumar · 2024 · Journal of Computational Chemistry
67. Analyzing the effects of streetscape and land use on urban accidents and predicting future accidents by using machine learning algorithms (case study: Mashhad)
    Seyed Amir Mohammad Bagheri, Barat Mojaradi, Neda Kamboozia, Mohsen Faizi · 2024 · Heliyon
68. Scalable quantum tomography with fidelity estimation
    Jun Wang, Zhaoyu Han, Songbo Wang, Zeyang Li, Liang-Zhu Mu, Heng Fan, Lei Wang · 2020 · Physical Review A
69. Error Thresholds for Arbitrary Pauli Noise
    Johannes Bausch, Felix Leditzky · 2021 · SIAM Journal on Computing
70. Neural-network quantum states at finite temperature
    Naoki Irikura, Hiroki Saito · 2020 · Physical Review Research
71. Storage capacity and learning capability of quantum neural networks
    Maciej Lewenstein, Aikaterini Gratsea, Andreu Riera-Campeny, Albert Aloy, Valentin Kasper, Anna Sanpera · 2021 · Quantum Science and Technology
72. Recurrent neural networks for stochastic control problems with delay
    Jiequn Han, Ruimeng Hu · 2021 · Mathematics of Control Signals and Systems
73. Correlation-enhanced neural networks as interpretable variational quantum states
    Agnes Valenti, Eliška Greplová, Netanel H. Lindner, Sebastian D. Huber · 2022 · Physical Review Research
74. Expressive power of complex-valued restricted Boltzmann machines for solving nonstoquastic Hamiltonians
    Chae-Yeun Park, Michael J. Kastoryano · 2022 · Physical review. B./Physical review. B
75. Quantum Neural Networks and Topological Quantum Field Theories
    Antonino Marcianò, De’en Chen, Filippo Fabrocini, Chris Fields, Enrico Greco, Niels G. Gresnigt, Krid Jinklub, Matteo Lulli, Kostas Terzidis, Emanuele Zappala · 2022 · Neural Networks
76. Quantum-Inspired Multi-Parameter Adaptive Bayesian Estimation for Sensing and Imaging
    Kwan K. Lee, Christos N. Gagatsos, Saikat Guha, Amit Ashok · 2022 · IEEE Journal of Selected Topics in Signal Processing
77. Solving the Schrödinger Equation in the Configuration Space with Generative Machine Learning
    Basile Herzog, Bastien Casier, Sébastien Lebègue∥, Dario Rocca · 2023 · Journal of Chemical Theory and Computation
78. Revealing the nature of hidden charm pentaquarks with machine learning.
    Zhen-Yu Zhang, Jiahao Liu, Ji-Feng Hu, Qian Wang, U. Meissner · 2023 · Science Bulletin
79. Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning
    Yi-Lun Du, Kai Zhou, Jan Steinheimer, Long-Gang Pang, Anton Motornenko, Hong-Shi Zong, Xin-Nian Wang, Horst Stöcker B · 2020 · The European Physical Journal C
80. Enhancing AI Responses in Chemistry: Integrating Text Generation, Image Creation, and Image Interpretation through Different Levels of Prompts
    Wilton José Diolindo do Nascimento Júnior, Carla Susana Lopes Morais, Gildo Girotto Júnior · 2024 · Journal of Chemical Education
81. Unified Variational Approach Description of Ground-State Phases of the Two-Dimensional Electron Gas
    Conor Smith, Yixiao Chen, Ryan Levy, Yubo Yang, Miguel A. Morales, Shiwei Zhang · 2024 · Physical Review Letters
82. Fast and flexible long-range models for atomistic machine learning
    Philip Loche, Kevin K. Huguenin-Dumittan, Melika Honarmand, Qianjun Xu, Egor Rumiantsev, Wei Bin How, Marcel F. Langer, Michele Ceriotti · 2025 · The Journal of Chemical Physics
83. Self-organizing maps as a method for detecting phase transitions and phase identification
    Albert A. Shirinyan, Valerii K. Kozin, Johan Hellsvik, Manuel Pereiro, Olle Eriksson, Dmitry I. Yudin · 2019 · Physical review. B./Physical review. B
84. Operationally meaningful representations of physical systems in neural networks
    Hendrik Poulsen Nautrup, Tony Metger, Raban Iten, Sofiène Jerbi, Lea M. Trenkwalder, Henrik Wilming, Hans J. Briegel, Renato Renner · 2022 · Machine Learning Science and Technology
85. Machine learning corrected quantum dynamics calculations
    A. Jasinski, J. Montaner, R. C. Forrey, B. H. Yang, P. C. Stancil, N. Balakrishnan, J. Dai, R. A. Vargas-Hernández, Roman V. Krems · 2020 · Physical Review Research
86. A priori generalization error analysis of two-layer neural networks for solving high dimensional Schrödinger eigenvalue problems
    Jianfeng Lu, Yulong Lu · 2022 · Communications of the American Mathematical Society
87. Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression
    Zhaozhi Qian, William R. Zame, Lucas M. Fleuren, Paul Elbers, Mihaela van der Schaar · 2021 · Pure Amsterdam UMC
88. Quantum kernels to learn the phases of quantum matter
    Teresa Sancho-Lorente, Juan Román-Roche, David Zueco · 2022 · Physical Review A
89. Building separable approximations for quantum states via neural networks
    Antoine Girardin, Nicolas Brunner, Tamás Kriváchy · 2022 · Physical Review Research
90. Conditional normalizing flow for Markov chain Monte Carlo sampling in the critical region of lattice field theory
    Ankur Singha, Dipankar Chakrabarti, Vipul Arora · 2023 · Physical review. D/Physical review. D.
91. Gradient-Descent Quantum Process Tomography by Learning Kraus Operators
    Shahnawaz Ahmed, Fernando Quijandría, Anton Frisk Kockum · 2023 · Physical Review Letters
92. DeepQMC: An open-source software suite for variational optimization of deep-learning molecular wave functions
    Zeno Schätzle, P. Bernát Szabó, Matěj Mezera, Jan Hermann, Frank Noé · 2023 · The Journal of Chemical Physics
93. Efficient Large-Scale Many-Body Quantum Dynamics via Local-Information Time Evolution
    Claudia Artiaco, Christoph Fleckenstein, David Aceituno Chávez, Thomas Klein Kvorning, Jens Hjorleifur Bardarson · 2024 · PRX Quantum
94. Complementary first and second derivative methods for ansatz optimization in variational Monte Carlo
    Leon Otis, Eric Neuscamman · 2019 · Physical Chemistry Chemical Physics
95. Solving inverse wave scattering with deep learning
    Yuwei Fan, Lexing Ying · 2022 · Annals of Mathematical Sciences and Applications
96. Scheme for automatic differentiation of complex loss functions with applications in quantum physics
    Chu Guo, Dario Poletti · 2021 · Physical review. E
97. Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models
    Kyle Mills, Isaac Tamblyn · 2018 · Physical review. E
98. Identifying product order with restricted Boltzmann machines
    Wen-Jia Rao, Zhenyu Li, Qiong Zhu, Mingxing Luo, Xin Wan · 2018 · Physical review. B./Physical review. B
99. Optimal Renormalization Group Transformation from Information Theory
    Patrick M. Lenggenhager, Doruk Efe Gökmen, Zohar Ringel, Sebastian D. Huber, Maciej Koch-Janusz · 2020 · Physical Review X
100. Approximating power of machine-learning ansatz for quantum many-body states
     Artem Borin, Dmitry A. Abanin · 2020 · Physical review. B./Physical review. B