Solving the quantum many-body problem with artificial neural networks

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Equation of State Calculations by Fast Computing Machines

Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928

Quantum Mechanics

Messiah, A. (1961) · Cited by 16,012

Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612

Density matrix formulation for quantum renormalization groups

White, S. R. (1992) · Physical Review Letters · doi:10.1103/physrevlett.69.2863 · Cited by 7,702

Approximation capabilities of multilayer feedforward networks

Hornik, K. (1991) · Neural Networks · doi:10.1016/0893-6080(91)90009-t · Cited by 6,091

The density-matrix renormalization group in the age of matrix product states

Schollwöck, U. (2011) · Annals of Physics · doi:10.1016/j.aop.2010.09.012 · Cited by 4,818

Note on Exchange Phenomena in the Thomas Atom

Dirac, P. A. M. (1930) · Mathematical Proceedings of the Cambridge Philosophical Society · doi:10.1017/s0305004100016108 · Cited by 3,302

Colloquium: Nonequilibrium dynamics of closed interacting quantum systems

Polkovnikov, A., Sengupta, K., Silva, A., & Vengalattore, M. (2011) · Reviews of Modern Physics · doi:10.1103/revmodphys.83.863 · Cited by 2,562

Variational quantum extreme learning machine

Wang, Y., Lin, K.-Y., Cheng, S., & Li, L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.068 · Cited by 20

Dynamic hierarchical quantum secret sharing based on the multiscale entanglement renormalization ansatz

Lai, H., Pieprzyk, J., & Pan, L. (2022) · Physical Review A · doi:10.1103/physreva.106.052403 · Cited by 19

Continuous-Mixture Autoregressive Networks Learning the Kosterlitz-Thouless Transition

Wang, L., Jiang, Y., He, L., & Zhou, K. (2022) · Chinese Physics Letters · doi:10.1088/0256-307x/39/12/120502 · Cited by 24

A promising intersection of excited‐state‐specific methods from quantum chemistry and quantum Monte Carlo

Otis, L., & Neuscamman, E. (2023) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1659 · Cited by 19

Simulating Prethermalization Using Near-Term Quantum Computers

Yang, Y., Christianen, A., Coll-Vinent, S., Smelyanskiy, V., Bañuls, M. C., O’Brien, T. E., Wild, D. S., & Cirac, J. I. (2023) · PRX Quantum · doi:10.1103/prxquantum.4.030320 · Cited by 23

Variational Quantum Dynamics of Two-Dimensional Rotor Models

Medvidović, M., & Sels, D. (2023) · PRX Quantum · doi:10.1103/prxquantum.4.040302 · Cited by 25

Quantum speed limit for complex dynamics

Mao, Z., Yu, H.-M., & Liu, J. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00768-8 · Cited by 19

Language models for quantum simulation

Melko, R. G., & Carrasquilla, J. (2024) · Nature Computational Science · doi:10.1038/s43588-023-00578-0 · Cited by 21

Atomic Quantum Technologies for Quantum Matter and Fundamental Physics Applications

Malo, J. Y., Lepori, L., Gentini, L., & Chiofalo, M. L. (2024) · Technologies · doi:10.3390/technologies12050064 · Cited by 22

Quantum embedding method with transformer neural network quantum states for strongly correlated materials

Ma, H., Shang, H., & Yang, J. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01406-3 · Cited by 20

How Sophisticated Are Neural Networks Needed to Predict Long-Term Nonadiabatic Dynamics?

Zeng, H., Kou, Y., & Sun, X. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01223 · Cited by 19

Doped stabilizer states in many-body physics and where to find them

Gu, A., Oliviero, S. F. E., & Leone, L. (2024) · Physical Review A · doi:10.1103/physreva.110.062427 · Cited by 26

Artificial neural networks and vector regression models for prediction of lattice constants of half-Heusler compounds

Ahmad, R., Gul, A., & Mehmood, N. (2018) · Materials Research Express · doi:10.1088/2053-1591/aafa9f · Cited by 19

Unitary-coupled restricted Boltzmann machine ansatz for quantum simulations

Hsieh, C., Sun, Q., Zhang, S., & Lee, C. (2021) · npj Quantum Information · doi:10.1038/s41534-020-00347-1 · Cited by 21

Neural-network approach for identifying nonclassicality from click-counting data

Gebhart, V., & Bohmann, M. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.023150 · Cited by 20

Flower and leaf recognition for plant identification using convolutional neural network

FatihahSahidan, N., Juha, A. K., Mohammad, N., & Ibrahim, Z. (2019) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v16.i2.pp737-743 · Cited by 20

QuantumToolbox.jl: An efficient Julia framework for simulating open quantum systems

Mercurio, A., Huang, Y.-T., Cai, L.-X., Chen, Y.-N., Savona, V., & Nori, F. (2025) · Quantum · doi:10.22331/q-2025-09-29-1866 · Cited by 27

Machine Learning Corrected Quantum Dynamics Calculations

Jasinski, A., Montaner, J., Forrey, R. C., Yang, B., Stancil, P. C., Balakrishnan, N., Dai, J. F., Vargas-Hernandez, A., & Krems, R. V. (2020) · Digital Scholarship - UNLV (University of Nevada Reno) · Cited by 18

Neural network flows of low q-state Potts and clock models

Giataganas, D., Huang, C.-Y., & Lin, F.-L. (2022) · New Journal of Physics · doi:10.1088/1367-2630/ac63da · Cited by 22

Quantum machine learning of graph-structured data

Beer, K., Khosla, M., Köhler, J., Osborne, T. J., & Zhao, T. (2023) · Physical Review A · doi:10.1103/physreva.108.012410 · Cited by 18

A semigroup method for high dimensional elliptic PDEs and eigenvalue problems based on neural networks

Li, H., & Ying, L. (2022) · Journal of Computational Physics · doi:10.1016/j.jcp.2022.110939 · Cited by 18

Unit Module-Based Convergence Acceleration for Topology Optimization Using the Spatiotemporal Deep Neural Network

Joo, Y., Yu, Y., & Jang, I. G. (2021) · IEEE Access · doi:10.1109/access.2021.3125014 · Cited by 22

Descriptive and Predictive Analysis on Heart Disease with Machine Learning and Deep Learning

Sutedja, I. (2021) · International Conference on Cybernetics and Intelligent System (ICORIS) · doi:10.1109/icoris52787.2021.9649585 · Cited by 18

Interpolating Between BSDEs and PINNs: Deep Learning for Elliptic and Parabolic Boundary Value Problems

Nüsken, N., & Richter, L. (2023) · Journal of Machine Learning · doi:10.4208/jml.220416 · Cited by 18

A genetic evolved machine learning approach for 3D DEM modelling of anisotropic materials with full consideration of particulate interactions

Gu, Z., Ding, X., Hou, X., & Ye, J. (2022) · Composites Part B Engineering · doi:10.1016/j.compositesb.2022.110432 · Cited by 18

Machine Learning the Phase Diagram of a Strongly Interacting Fermi Gas

Link, M., Gao, K., Kell, A., Breyer, M., Eberz, D., Rauf, B., & Köhl, M. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.203401 · Cited by 18

Highly Resolved Spectral Functions of Two-Dimensional Systems with Neural Quantum States

Mendes-Santos, T., Schmitt, M., & Heyl, M. (2023) · Physical Review Letters · doi:10.1103/physrevlett.131.046501 · Cited by 25

Variational Neural-Network Ansatz for Continuum Quantum Field Theory

Martyn, J. M., Najafi, K., & Luo, D. (2023) · Physical Review Letters · doi:10.1103/physrevlett.131.081601 · Cited by 33

Variational quantum eigensolvers in the era of distributed quantum computers

Khait, I., Tham, E., Segal, D., & Brodutch, A. (2023) · Physical Review A · doi:10.1103/physreva.108.l050401 · Cited by 22

Experimental investigation and phenomenological modeling of fatigue crack growth in X80 pipeline steel under random loading

Liang, W., Lou, M., Zhang, C., Zhao, D., Yang, D., & Wang, Y. (2024) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2024.108169 · Cited by 18

Spectral density classification for environment spectroscopy

Barr, J., Zicari, G., Ferraro, A., & Paternostro, M. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad2cf1 · Cited by 18

Neural Wave Functions for Superfluids

Lou, W., Sutterud, H., Cassella, G., Foulkes, W. M. C., Knolle, J., Pfau, D., & Spencer, J. S. (2024) · Physical Review X · doi:10.1103/physrevx.14.021030 · Cited by 29

Neural network approach to quasiparticle dispersions in doped antiferromagnets

Lange, H., Döschl, F., Carrasquilla, J., & Bohrdt, A. (2024) · Communications Physics · doi:10.1038/s42005-024-01678-7 · Cited by 18

Improved optimization for the neural-network quantum states and tests on the chromium dimer

Li, X., Huang, J.-C., Zhang, G.-Z., Li, H.-E., Shen, Z.-P., Zhao, C., Li, J., & Hu, H. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0214150 · Cited by 18

Transformer-Based Neural-Network Quantum State Method for Electronic Band Structures of Real Solids

Fu, L., Wu, Y., Shang, H., & Yang, J. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00567 · Cited by 18

Ground and Excited States from Ensemble Variational Principles

Ding, L., Hong, C.-L., & Schilling, C. (2024) · Quantum · doi:10.22331/q-2024-11-14-1525 · Cited by 18

DiracSolver: A tool for solving the Dirac equation

Tsoulos, I. G., Kosmas, O., & Stavrou, V. N. (2018) · Computer Physics Communications · doi:10.1016/j.cpc.2018.10.010 · Cited by 18

On the descriptive power of Neural-Networks as constrained Tensor Networks with exponentially large bond dimension

Collura, M., Dell’Anna, L., Felser, T., & Montangero, S. (2021) · SciPost Physics Core · doi:10.21468/scipostphyscore.4.1.001 · Cited by 21

On the stability of the infinite Projected Entangled Pair Operator ansatz for driven-dissipative 2D lattices

Kilda, D., Biella, A., Schirò, M., Fazio, R., & Keeling, J. (2021) · SciPost Physics Core · doi:10.21468/scipostphyscore.4.1.005 · Cited by 18

Machine-learned phase diagrams of generalized Kitaev honeycomb magnets

Rao, N., Liu, K., Machaczek, M., & Pollet, L. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.033223 · Cited by 20

Many-body calculations for periodic materials via restricted Boltzmann machine-based VQE

Kanno, S., & Tada, T. (2021) · Quantum Science and Technology · doi:10.1088/2058-9565/abe139 · Cited by 19

jVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration

Schmitt, M., & Reh, M. (2022) · SciPost Physics Codebases · doi:10.21468/scipostphyscodeb.2 · Cited by 23

Random Batch Algorithms for Quantum Monte Carlo Simulations

Jin, S., & Li, X. (2020) · Communications in Computational Physics · doi:10.4208/cicp.oa-2020-0168 · Cited by 18

Avoiding local minima in Variational Quantum Algorithms with Neural NetworksPreprint

Rivera-Dean, J., Huembeli, P., Acín, A., & Bowles, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.02955 · Cited by 34

Neural network operations and Susuki–Trotter evolution of neural network states

Freitas, N., Morigi, G., & Dunjko, V. (2018) · International Journal of Quantum Information · doi:10.1142/s0219749918400087 · Cited by 17

Spin-symmetry-enforced solution of the many-body Schrödinger equation with a deep neural network

Li, Z., Lu, Z., Li, R., Wen, X., Li, X., Wang, L., Chen, J., & Ren, W. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00730-4 · Cited by 23

Foundation neural-networks quantum states as a unified Ansatz for multiple hamiltonians

Rende, R., Viteritti, L. L., Becca, F., Scardicchio, A., Laio, A., & Carleo, G. (2025) · Nature Communications · doi:10.1038/s41467-025-62098-x · Cited by 46

Gaussian Process States: A Data-Driven Representation of Quantum Many-Body Physics

Glielmo, A., Rath, Y., Cśanyi, G., Vita, A. D., & Booth, G. H. (2020) · Physical Review X · doi:10.1103/physrevx.10.041026 · Cited by 20

Unsupervised machine learning of phase transition in percolation

Yu, W., & Lyu, P. (2020) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2020.125065 · Cited by 19

Physics-Coupled Neural Network Magnetic Resonance Electrical Property Tomography (MREPT) for Conductivity Reconstruction

Inda, A. J. G., Huang, S. Y., İmamoğlu, N., & Yu, W. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3172220 · Cited by 17

Artificial neural network approximations of Cauchy inverse problem for linear PDEs

Li, Y., & Hu, X. (2021) · Applied Mathematics and Computation · doi:10.1016/j.amc.2021.126678 · Cited by 17

Extending the reach of quantum computing for materials science with machine learning potentials

Schuhmacher, J., Mazzola, G., Tacchino, F., Dmitriyeva, O., Bui, T., Huang, S., & Tavernelli, I. (2022) · AIP Advances · doi:10.1063/5.0099469 · Cited by 18

Resonance for Analog Recurrent Neural Network

Qu, Y., Zhou, M., Khoram, E., Yu, N., & Yu, Z. (2022) · ACS Photonics · doi:10.1021/acsphotonics.1c02016 · Cited by 18

Designing quantum many-body matter with conditional generative adversarial networks

Koch, R., & Lado, J. L. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.033223 · Cited by 17

Variational solutions to fermion-to-qubit mappings in two spatial dimensions

Nys, J., & Carleo, G. (2022) · Quantum · doi:10.22331/q-2022-10-13-833 · Cited by 17

Machine learning of pair-contact process with diffusion

Shen, J., Li, W., Deng, S., Xu, D., Chen, S., & Liu, F. (2022) · Scientific Reports · doi:10.1038/s41598-022-23350-2 · Cited by 19

Accurate property prediction with interpretable machine learning model for small datasets via transformed atom vector

Chen, X., Lu, S., Wan, X.-Y., Chen, Q., Zhou, Q., & Wang, J. (2022) · Physical Review Materials · doi:10.1103/physrevmaterials.6.123803 · Cited by 17

Data-driven modeling for the dynamic behavior of nonlinear vibratory systems

Liu, H., Zhao, C., Huang, X., & Yao, G. (2023) · Nonlinear Dynamics · doi:10.1007/s11071-023-08404-y · Cited by 18

An artificial neural network approach to identify the parameter in a nonlinear subdiffusion model

Oulmelk, A., Srati, M., Afraites, L., & Hadri, A. (2023) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2023.107413 · Cited by 17

Can Neural Quantum States Learn Volume-Law Ground States?

Passetti, G., Hofmann, D., Neitemeier, P., Grunwald, L., Sentef, M. A., & Kennes, D. M. (2023) · Physical Review Letters · doi:10.1103/physrevlett.131.036502 · Cited by 33

Iterative quantum algorithm for combinatorial optimization based on quantum gradient descent

Yi, X., Huo, J., Gao, Y., Fan, L., Zhang, R., & Cao, C. (2023) · Results in Physics · doi:10.1016/j.rinp.2023.107204 · Cited by 17

Correlation-Pattern-Based Continuous Variable Entanglement Detection through Neural Networks

Gao, X., Isoard, M., Sun, F.-X., Lopetegui, C. E., Xiang, Y., Parigi, V., He, Q., & Walschaers, M. (2024) · Physical Review Letters · doi:10.1103/physrevlett.132.220202 · Cited by 22

Neural network backflow for ab initio quantum chemistry

Liu, A.-J., & Clark, B. K. (2024) · Physical review. B./Physical review. B · doi:10.1103/physrevb.110.115137 · Cited by 17

Transforming generalized Ising models into Boltzmann machines

Yoshioka, N., Akagi, Y., & Katsura, H. (2019) · Physical review. E · doi:10.1103/physreve.99.032113 · Cited by 17

Machine learning effective models for quantum systems

Rigo, J. B., & Mitchell, A. K. (2020) · Physical review. B./Physical review. B · doi:10.1103/physrevb.101.241105 · Cited by 17

Learning epidemic threshold in complex networks by Convolutional Neural Network

Ni, Q., Kang, J., Tang, M., Liu, Y., & Zou, Y. (2019) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5121401 · Cited by 17

Variational classical networks for dynamics in interacting quantum matter

Verdel, R., Schmitt, M., Huang, Y.-P., Karpov, P., & Heyl, M. (2021) · Physical review. B./Physical review. B · doi:10.1103/physrevb.103.165103 · Cited by 18

Predicting Kerr Soliton Combs in Microresonators via Deep Neural Networks

Tan, T., Peng, C., Yuan, Z., Xie, X., Liu, H., Xie, Z., Huang, S., Rao, Y., & Yao, B. (2020) · Journal of Lightwave Technology · doi:10.1109/jlt.2020.3015586 · Cited by 17

Optimized observable readout from single-shot images of ultracold atoms via machine learning

Lode, A. U. J., Lin, R., Büttner, M., Papariello, L., Lévêque, C., Chitra, R., Tsatsos, M. C., Jaksch, D., & Molignini, P. (2021) · Physical Review A · doi:10.1103/physreva.104.l041301 · Cited by 22

Emergent complex quantum networks in continuous-variables non-Gaussian states

Walschaers, M., Sundar, B., Treps, N., Carr, L. D., & Parigi, V. (2023) · Quantum Science and Technology · doi:10.1088/2058-9565/accdfd · Cited by 19

Chebyshev expansion of spectral functions using restricted Boltzmann machines

Hendry, D., Chen, H., Weinberg, P., & Feiguin, A. E. (2021) · Physical review. B./Physical review. B · doi:10.1103/physrevb.104.205130 · Cited by 17

Certification of quantum states with hidden structure of their bitstrings

Sotnikov, O. M., Iakovlev, I. A., Iliasov, A. A., Katsnelson, M. I., Bagrov, A. A., & Mazurenko, V. V. (2022) · npj Quantum Information · doi:10.1038/s41534-022-00559-7 · Cited by 16

Phase diagram of quantum generalized Potts-Hopfield neural networks

Fiorelli, E., Lesanovsky, I., & Müller, M. (2022) · New Journal of Physics · doi:10.1088/1367-2630/ac5490 · Cited by 19

Quantum Lyapunov control with machine learning

Hou, S. C., & Yi, X. X. (2019) · Quantum Information Processing · doi:10.1007/s11128-019-2470-8 · Cited by 17

Machine learning for estimation and control of quantum systems

Ma, H., Qi, B., Petersen, I. R., Wu, R.-B., RABITZ, H. A., & Dong, D. (2025) · National Science Review · doi:10.1093/nsr/nwaf269 · Cited by 24

Solving the many-electron Schrödinger equation with a transformer-based framework

Shang, H., Guo, C., Wu, Y., Li, Z., & Yang, J. (2025) · Nature Communications · doi:10.1038/s41467-025-63219-2 · Cited by 23

Kink Dynamics and Quantum Simulation of Supersymmetric Lattice Hamiltonians

Minář, J., van Voorden, B., & Schoutens, K. (2022) · Physical Review Letters · doi:10.1103/physrevlett.128.050504 · Cited by 16

Entanglement features of random neural network quantum states

Sun, X.-Q., Nebabu, T., Han, X., Flynn, M. O., & Qi, X.-L. (2022) · Physical review. B./Physical review. B · doi:10.1103/physrevb.106.115138 · Cited by 16

Time-dependent Dirac Equation with Physics-Informed Neural Networks: Computation and Properties

Lorin, E., & Yang, X. (2022) · Computer Physics Communications · doi:10.1016/j.cpc.2022.108474 · Cited by 16

A Real Neural Network State for Quantum Chemistry

Wu, Y., Xu, X., Poletti, D., Fan, Y., Guo, C., & Shang, H. (2023) · Mathematics · doi:10.3390/math11061417 · Cited by 16

Toward perturbation theory methods on a quantum computer

Li, J., Jones, B., & Kais, S. (2023) · Science Advances · doi:10.1126/sciadv.adg4576 · Cited by 16

Learning by confusion approach to identification of discontinuous phase transitions

Richter-Laskowska, M., Kurpas, M., & Maśka, M. M. (2023) · Physical review. E · doi:10.1103/physreve.108.024113 · Cited by 17

A Nonstochastic Optimization Algorithm for Neural-Network Quantum States

Li, X., Huang, J.-C., Zhang, G.-Z., Li, H.-E., Cao, C., Lv, D., & Hu, H. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00831 · Cited by 17

Ab initio artificial intelligence: Future research of Materials Genome Initiative

Li, H., Xu, Y., & Duan, W. (2023) · Materials Genome Engineering Advances · doi:10.1002/mgea.16 · Cited by 16

The quantum house of cards

Waintal, X. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2313269120 · Cited by 19

Physics-Constrained Hardware-Efficient Ansatz on Quantum Computers That Is Universal, Systematically Improvable, and Size-Consistent

Xiao, X., Zhao, H., Ren, J., Fang, W., & Li, Z. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00966 · Cited by 21

SoK: quantum computing methods for machine learning optimization

Baniata, H. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00180-1 · Cited by 16

Neural networks for quantum state tomography with constrained measurements

Ma, H., Dong, D., Petersen, I. R., Huang, C.-J., & Xiang, G. (2024) · Quantum Information Processing · doi:10.1007/s11128-024-04522-7 · Cited by 19

Spin- 1 / 2 Kagome Heisenberg Antiferromagnet: Machine Learning Discovery of the Spinon Pair-Density-Wave Ground State

Đurić, T., Chung, J. H., Yang, B., & Sengupta, P. (2025) · Physical Review X · doi:10.1103/physrevx.15.011047 · Cited by 19

Quantum computing quantum Monte Carlo algorithm

Zhang, Y., Huang, Y., Sun, J., Lv, D., & Yuan, X. (2025) · Physical Review A · doi:10.1103/jt8s-hzhd · Cited by 27

Machine learning the many-body localization transition in random spin systems

Rao, W.-J. (2018) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/aaddc6 · Cited by 15

Comparison of Use of a 2000 Qubit D-Wave Quantum Annealer and MCMC for Sampling, Image Reconstruction, and Classification

Koshka, Y., & Novotny, M. A. (2018) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2018.2871466 · Cited by 16

Efficient representation of long-range interactions in tensor network algorithms

O’Rourke, M. J., Li, Z., & Chan, G. K. (2018) · Physical review. B./Physical review. B · doi:10.1103/physrevb.98.205127 · Cited by 16

Stochastic replica voting machine prediction of stable cubic and double perovskite materials and binary alloys

Mazaheri, T., Sun, B., Scher-Zagier, J., Thind, A. S., Magee, D., Ronhovde, P., Lookman, T., Mishra, R., & Nussinov, Z. (2019) · Physical Review Materials · doi:10.1103/physrevmaterials.3.063802 · Cited by 20

Speeding up ab initio diffusion Monte Carlo simulations by a smart lattice regularization

Nakano, K., Maezono, R., & Sorella, S. (2020) · Physical review. B./Physical review. B · doi:10.1103/physrevb.101.155106 · Cited by 16

DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems

Rupe, A., Kumar, N., Epifanov, V., Kashinath, K., Pavlyk, O., Schlimbach, F., Patwary, M. A., Maidanov, S., Lee, V., Prabhat, M., et al. (2019) · Workshop on Machine Learning in HPC Environments · doi:10.1109/mlhpc49564.2019.00013 · Cited by 17

Locality meets machine learning: Excited and ground-state energy surfaces of large systems at the cost of small ones

Babaei, M., Azar, Y. T., & Sadeghi, A. (2020) · Physical review. B./Physical review. B · doi:10.1103/physrevb.101.115132 · Cited by 15

Time-dependent atomic magnetometry with a recurrent neural network

Khanahmadi, M., & Mølmer, K. (2021) · Physical Review A · doi:10.1103/physreva.103.032406 · Cited by 17

The future of the correlated electron problem

Alexandradinata, A., Armitage, N. P., Baydin, A., Bi, W., Cao, Y., Changlani, H. J., Chertkov, E., Neto, E. H. D. S., Delacrétaz, L. V., Baggari, I. E., et al. (2025) · SciPost Physics Community Reports · doi:10.21468/scipostphyscommrep.8 · Cited by 37

Principal component analysis of the geometry in anisotropic quantum Hall states

Jiang, N., Ke, S., Ji, H., Wang, H., Hu, Z.-X., & Wan, X. (2020) · Physical review. B./Physical review. B · doi:10.1103/physrevb.102.115140 · Cited by 16

Could the Hilbert Space Be a Smaller Place? A Neural Network PerspectivePreprint

Sellier, J. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.08577

Machine learning for quantum physics

Hush, M. (2017) · Science · doi:10.1126/science.aam6564 · Cited by 31

Approximating quantum many-body wave functions using artificial neural networks

Cai, Z., & Liu, J.-G. (2018) · Physical review. B./Physical review. B · doi:10.1103/physrevb.97.035116 · Cited by 221

Articial Neural Networks as Function Approximators in Quantum Many-Body Physics

Rigo, J. (2018) · Digital Library of the University of Innsbruck (University of Innsbruck)

Solving Quantum Many-Body Problems using Artificial Neural Networks

Mondal, S., & Roy, K. (2024) · doi:10.13140/rg.2.2.34951.10409