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Variational quantum calculations have borrowed many tools and algorithms from the machine learning community in the recent years.
Quantum bits with josephson junctions
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Über das paulische äquivalenzverbot
Jordan, P. & Wigner, E · 1928
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On the constitution of metallic sodium. ii
Wigner, E. & Seitz, F · 1934
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Equation of state calculations by fast computing machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. & Teller, E · 1953
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Atomic theory of the two-fluid model of liquid helium
Feynman, R. P · 1954
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Many-body problem with strong forces
Jastrow, R · 1955
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Energy spectrum of the excitations in liquid helium
Feynman, R. P. & Cohen, M · 1956
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On the eigenfunctions of many-particle systems in quantum mechanics
Kato, T · 1957
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Stability conditions and nuclear rotations in the hartree-fock theory
Thouless, D · 1960
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Possible new effects in superconductive tunnelling
Josephson, B · 1962
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Coefficients for the study of runge-kutta integration processes
Butcher, J. C · 1963
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Electron correlations in narrow energy bands
Hubbard, J. & Flowers, B. H · 1963
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Supercurrents through barriers
Josephson, B · 1965
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Monte carlo sampling methods using markov chains and their applications
Hastings, W. K · 1970
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New method for the anderson model 6
Barnes, S. E · 1976
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New method for the anderson model. II. the u=0 limit 7
Barnes, S. E · 1977
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New approach to the mixed-valence problem
Coleman, P · 1984
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Cosmological experiments in superfluid helium?
Zurek, W. H · 1985
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New functional integral approach to strongly correlated fermi systems: The gutzwiller approximation as a saddle point
Kotliar, G. & Ruckenstein, A. E · 1986
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There exists a neural network that does not make avoidable mistakes
Gallant & White · 1988
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Approximation by superpositions of a sigmoidal function
Cybenko, G · 1989
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Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M. & White, H · 1989
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Collective monte carlo updating for spin systems
Wolff, U · 1989
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Spin-rotation-invariant slave-boson approach to the hubbard model
Li, T., Wölfle, P. & Hirschfeld, P. J · 1989
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Mean-field theory for the t-j model
Jayaprakash, C., Krishnamurthy, H. R. & Sarker, S · 1989
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Mean-field theory of the spiral phases of a doped antiferromagnet
Kane, C. L., Lee, P. A., Ng, T. K., Chakraborty, B. & Read, N · 1990
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Density matrix formulation for quantum renormalization groups
White, S. R · 1992
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Unified slave boson representation of spin and charge degrees of freedom for strongly correlated fermi systems
Frésard, R. & Wölfle, P · 1992
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Effects of three-body and backflow correlations in the two-dimensional electron gas
Kwon, Y., Ceperley, D. M. & Martin, R. M · 1993
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The large n expansion and the strong correlation problem
Kotliar, G · 1995
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Cosmological experiments in condensed matter systems
Zurek, W · 1996
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Quantum Many-particle Systems (Westview Press, 1998)
Negele, J. W. & Orland, H · 1998
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Green function monte carlo with stochastic reconfiguration
Sorella, S · 1998
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Natural gradient works efficiently in learning
Amari, S. I · 1998
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Effects of backflow correlation in the three-dimensional electron gas: Quantum monte carlo study
Kwon, Y., Ceperley, D. M. & Martin, R. M · 1998
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Monte Carlo Methods in Statistical Physics - M. E. J. Newman; G. T. Barkema (1999)
Newman, M. & Barkema, G · 1999
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Two Dimensional Josephson Junction Arrays
Martinoli, P. & Leemann, C · 2000
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Monte carlo methods in statistical physics
Strawderman, R. L · 2001
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Quantum impurity solvers using a slave rotor representation
Florens, S. & Georges, A · 2002
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Iterative retraining of quantum spin models using recurrent neural networks (2020)
Roth, C · 2003
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Renormalization algorithms for quantum-many body systems in two and higher dimensions (2004)
Verstraete, F. & Cirac, J. I · 2004
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Slave-rotor mean-field theories of strongly correlated systems and the mott transition in finite dimensions
Florens, S. & Georges, A · 2004
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Orbital-selective mott transition in multiband systems: Slave-spin representation and dynamical mean-field theory
de’Medici, L., Georges, A. & Biermann, S · 2005
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Rotationally invariant slave-boson formalism and momentum dependence of the quasiparticle weight
Lechermann, F., Georges, A., Kotliar, G. & Parcollet, O · 2007
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Numerical Methods for Ordinary Differential Equations (John Wiley & Sons, Ltd, 2008)
Butcher, J. C · 2008
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Role of backflow correlations for the nonmagnetic phase of the t – t ′ t\text{--}{t}^{{}^{\prime}} hubbard model
Tocchio, L. F., Becca, F., Parola, A. & Sorella, S · 2008
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Kernel methods in machine learning
Hofmann, T., Schölkopf, B. & Smola, A. J · 2008
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The density matrix renormalization group self-consistent field method: Orbital optimization with the density matrix renormalization group method in the active space
Zgid, D. & Nooijen, M · 2008
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Orbital optimization in the density matrix renormalization group, with applications to polyenes and β \beta -carotene
Ghosh, D., Hachmann, J., Yanai, T. & Chan, G. K.-L · 2008
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Luo, D., Chen, Z., Carrasquilla, J. & Clark, B. K · 2009
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URL https://www.taylorfrancis.com/books/9781420079425
Brooks, S., Gelman, A., Jones, G. & Meng, X.-L. (eds.) Handbook of Markov Chain Monte Carlo (Chapman and Hall/CRC, 2011) · 2011
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Handbook of Markov Chain Monte Carlo (Chapman and Hall/CRC, 2011)
Neal, R. M · 2011
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The no-u-turn sampler: Adaptively setting path lengths in hamiltonian monte carlo
Hoffman, M. D. & Gelman, A · 2011
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Better, Faster Fermionic Neural Networks (2020)
Spencer, J. S., Pfau, D., Botev, A. & Foulkes, W. M. C · 2011
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Backflow correlations in the hubbard model: An efficient tool for the study of the metal-insulator transition and the large- u u limit
Tocchio, L. F., Becca, F. & Gros, C · 2011
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Localization and glassy dynamics of many-body quantum systems
Carleo, G., Becca, F., Schiró, M. & Fabrizio, M · 2012
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Quantum Computation and Quantum Information (Cambridge University Press, 2012)
Nielsen, M. A. & Chuang, I. L · 2012
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Generating sequences with recurrent neural networks (2013)
Graves, A · 2013
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Adam: A method for stochastic optimization (2014)
Kingma, D. P. & Ba, J · 2014
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A quantum approximate optimization algorithm (2014)
Farhi, E., Goldstone, J. & Gutmann, S · 2014
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Optimizing neural networks with kronecker-factored approximate curvature (2015)
Martens, J. & Grosse, R · 2015
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Time-dependent many-variable variational monte carlo method for nonequilibrium strongly correlated electron systems
Ido, K., Ohgoe, T. & Imada, M · 2015
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Libcint: An efficient general integral library for gaussian basis functions
Sun, Q · 2015
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Deep learning
LeCun, Y., Bengio, Y. & Hinton, G · 2015
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One-dimensional Josephson junction arrays: Lifting the Coulomb blockade by depinning
Vogt, N. et al · 2015
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Deep Learning (MIT Press, 2016)
Goodfellow, I., Bengio, Y. & Courville, A · 2016
Cited alongside, same era.
Tunable two-dimensional arrays of single rydberg atoms for realizing quantum ising models
Labuhn, H. et al · 2016
Cited alongside, same era.
Quantum supremacy through the quantum approximate optimization algorithm
Farhi, E. & Harrow, A. W · 2016
Cited alongside, same era.
Solving the quantum many-body problem with artificial neural networks
Carleo, G. & Troyer, M · 2017
Cited alongside, same era.
Efficient representation of quantum many-body states with deep neural networks
Gao, X. & Duan, L.-M · 2017
Cited alongside, same era.
Restricted Boltzmann machine learning for solving strongly correlated quantum systems
Solving quasiparticle band spectra of real solids using neural-network quantum states
Yoshioka, N., Mizukami, W. & Nori, F · 2021
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Implementation of Quantum Machine Learning for Electronic Structure Calculations of Periodic Systems on Quantum Computing Devices
Sureshbabu, S. H., Sajjan, M., Oh, S. & Kais, S · 2021
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Determinant-free fermionic wave function using feed-forward neural networks
Inui, K., Kato, Y. & Motome, Y · 2021
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Mott insulating states with competing orders in the triangular lattice hubbard model
Wietek, A. et al · 2021
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Quantum embedding description of the anderson lattice model with the ghost gutzwiller approximation
Frank, M. S. et al · 2021
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Nomura, Y., Darmawan, A. S., Yamaji, Y. & Imada, M · 2017
Cited alongside, same era.
Quantum Monte Carlo Approaches for Correlated Systems (Cambridge University Press, 2017)
Becca, F. & Sorella, S · 2017
Cited alongside, same era.
Stan: A probabilistic programming language
Carpenter, B. et al · 2017
Cited alongside, same era.
A conceptual introduction to hamiltonian monte carlo (2017)
Betancourt, M · 2017
Cited alongside, same era.
Unitary dynamics of strongly interacting bose gases with the time-dependent variational monte carlo method in continuous space
Carleo, G., Cevolani, L., Sanchez-Palencia, L. & Holzmann, M · 2017
Cited alongside, same era.
Solving the bose–hubbard model with machine learning
Saito, H · 2017
Cited alongside, same era.
Quantum Entanglement in Neural Network States
Deng, D.-L., Li, X. & Das Sarma, S · 2017
Cited alongside, same era.
Small to large fermi surface transition in a single-band model using randomly coupled ancillas
Nikolaenko, A., Tikhanovskaya, M., Sachdev, S. & Zhang, Y.-H · 2021
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Variational classical networks for dynamics in interacting quantum matter
Verdel, R., Schmitt, M., Huang, Y.-P., Karpov, P. & Heyl, M · 2021
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Disorder-free localization in an interacting 2d lattice gauge theory
Karpov, P., Verdel, R., Huang, Y.-P., Schmitt, M. & Heyl, M · 2021
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Unitary long-time evolution with quantum renormalization groups and artificial neural networks
Burau, H. & Heyl, M · 2021
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Modern applications of machine learning in quantum sciences (2022)
Dawid, A. et al · 2022
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Neural tensor contractions and the expressive power of deep neural quantum states
Sharir, O., Shashua, A. & Carleo, G · 2022
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Lattice Convolutional Networks for Learning Ground States of Quantum Many-Body Systems (2022)
Fu, C. et al · 2022
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Fermionic wave functions from neural-network constrained hidden states
Robledo Moreno, J., Carleo, G., Georges, A. & Stokes, J · 2022
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Real time evolution with neural-network quantum states
Gutiérrez, I. L. & Mendl, C. B · 2022
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Autoregressive neural-network wavefunctions for ab initio quantum chemistry
Barrett, T. D., Malyshev, A. & Lvovsky, A. I · 2022
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Correlation-enhanced neural networks as interpretable variational quantum states
Valenti, A., Greplova, E., Lindner, N. H. & Huber, S. D · 2022
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Neural-network quantum states for periodic systems in continuous space
Pescia, G., Han, J., Lovato, A., Lu, J. & Carleo, G · 2022
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Hibat-Allah, M., Melko, R. G. & Carrasquilla, J · 2022
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Data-enhanced variational monte carlo simulations for rydberg atom arrays
Czischek, S., Moss, M. S., Radzihovsky, M., Merali, E. & Melko, R. G · 2022
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Autoregressive neural-network wavefunctions for ab initio quantum chemistry
Barrett, T. D., Malyshev, A. & Lvovsky, A. I · 2022
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A self-attention ansatz for ab-initio quantum chemistry (2022)
von Glehn, I., Spencer, J. S. & Pfau, D · 2022
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Hidden-nucleons neural-network quantum states for the nuclear many-body problem
Lovato, A., Adams, C., Carleo, G. & Rocco, N · 2022
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Dilute neutron star matter from neural-network quantum states (2022)
Fore, B., Kim, J. M., Carleo, G., Hjorth-Jensen, M. & Lovato, A · 2022
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Variational solutions to fermion-to-qubit mappings in two spatial dimensions
Nys, J. & Carleo, G · 2022
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Quantum phase transition dynamics in the two-dimensional transverse-field ising model
Schmitt, M., Rams, M. M., Dziarmaga, J., Heyl, M. & Zurek, W. H · 2022
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Transmon platform for quantum computing challenged by chaotic fluctuations
Berke, C., Varvelis, E., Trebst, S., Altland, A. & DiVincenzo, D. P · 2022
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Neural error mitigation of near-term quantum simulations
Bennewitz, E. R., Hopfmueller, F., Kulchytskyy, B., Carrasquilla, J. & Ronagh, P · 2022
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Wu, A.-K., Fishman, M. T., Pixley, J. H. & Stoudenmire, E. M · 2022
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From tensor-network quantum states to tensorial recurrent neural networks
Wu, D., Rossi, R., Vicentini, F. & Carleo, G · 2023
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Mott transition and volume law entanglement with neural quantum states (2023)
Gauvin-Ndiaye, C., Tindall, J., Moreno, J. R. & Georges, A · 2023
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Unbiasing time-dependent variational monte carlo by projected quantum evolution
Sinibaldi, A., Giuliani, C., Carleo, G. & Vicentini, F · 2023
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Infinite neural network quantum states: entanglement and training dynamics
Luo, D. & Halverson, J · 2023
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Optimizing design choices for neural quantum states
Reh, M., Schmitt, M. & Gärttner, M · 2023
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Efficient optimization of deep neural quantum states toward machine precision (2023)
Chen, A. & Heyl, M · 2023
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Variational benchmarks for quantum many-body problems (2023)
Wu, D. et al · 2023
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Variational quantum dynamics of two-dimensional rotor models
Medvidović, M. & Sels, D · 2023
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Dynamics with autoregressive neural quantum states: Application to critical quench dynamics
Donatella, K., Denis, Z., Boité, A. L. & Ciuti, C · 2023
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Ab initio quantum chemistry with neural-network wavefunctions
Hermann, J. et al · 2023
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Investigating topological order using recurrent neural networks
Hibat-Allah, M., Melko, R. G. & Carrasquilla, J · 2023
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Wave function network description and kolmogorov complexity of quantum many-body systems (2023)
Mendes-Santos, T. et al · 2023
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Neural network approach to quasiparticle dispersions in doped antiferromagnets (2023)
Lange, H., Döschl, F., Carrasquilla, J. & Bohrdt, A · 2023
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Autoregressive neural quantum states with quantum number symmetries (2023)
Malyshev, A., Arrazola, J. M. & Lvovsky, A. I · 2023
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Mean-field theories are simple for neural quantum states (2023)
Trigueros, F. B., Mendes-Santos, T. & Heyl, M · 2023
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Electronic excited states in deep variational monte carlo
Entwistle, M. T., Schätzle, Z., Erdman, P. A., Hermann, J. & Noé, F · 2023
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Discovering quantum phase transitions with fermionic neural networks
Cassella, G. et al · 2023
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Message-passing neural quantum states for the homogeneous electron gas (2023)
Pescia, G., Nys, J., Kim, J., Lovato, A. & Carleo, G · 2023
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Neural-network quantum states for ultra-cold fermi gases (2023)
Kim, J. et al · 2023
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Neural wave functions for superfluids (2023)
Lou, W. T. et al · 2023
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Distilling the essential elements of nuclear binding via neural-network quantum states (2023)
Gnech, A., Fore, B. & Lovato, A · 2023
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Solving the nuclear pairing model with neural network quantum states
Rigo, M., Hall, B., Hjorth-Jensen, M., Lovato, A. & Pederiva, F · 2023
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Framework for efficient ab initio electronic structure with gaussian process states
Rath, Y. & Booth, G. H · 2023
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Scalable neural quantum states architecture for quantum chemistry
Zhao, T., Stokes, J. & Veerapaneni, S · 2023
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Moreno, J. R., Cohn, J., Sels, D. & Motta, M · 2023
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Fragmented superconductivity in the hubbard model as solitons in ginzburg-landau theory (2023)
Baldelli, N., Kloss, B., Fishman, M. & Wietek, A · 2023
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Autoregressive neural Slater-Jastrow ansatz for variational Monte Carlo simulation
Humeniuk, S., Wan, Y. & Wang, L · 2023
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Natural quantum monte carlo computation of excited states (2023)
Pfau, D., Axelrod, S., Sutterud, H., von Glehn, I. & Spencer, J. S · 2023
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Artificial intelligence for artificial materials: moiré atom (2023)
Luo, D., Reddy, A. P., Devakul, T. & Fu, L · 2023
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Evidence for the utility of quantum computing before fault tolerance
Kim, Y. et al · 2023
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Highly resolved spectral functions of two-dimensional systems with neural quantum states
Mendes-Santos, T., Schmitt, M. & Heyl, M · 2023
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Enhancing variational monte carlo using a programmable quantum simulator (2023)
Moss, M. S. et al · 2023
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Infinite neural network quantum states: entanglement and training dynamics
Luo, D. & Halverson, J · 2023
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Dugan, O., Lu, P. Y., Dangovski, R., Luo, D. & Soljačić, M · 2023
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From architectures to applications: A review of neural quantum states (2024)
Lange, H., de Walle, A. V., Abedinnia, A. & Bohrdt, A · 2024
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The density matrix renormalization group for ab initio quantum chemistry 68
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