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This article aims to summarize recent and ongoing efforts to simulate continuous-variable quantum systems using flow-based variational quantum Monte Carlo techniques, focusing for pedagogical purposes on the example of bosons in the field amplitude (quadrature) basis.
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Weak binding between two aromatic rings: Feeling the van der Waals attraction by quantum Monte Carlo methods
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Studying a relativistic field theory at finite chemical potential with the density matrix renormalization group
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James Glimm and Arthur Jaffe · 2012
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Giuseppe Carleo, Federico Becca, Laurent Sanchez-Palencia, Sandro Sorella, and Michele Fabrizio · 2014
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Quantum computation of scattering in scalar quantum field theories
Stephen P Jordan, Keith SM Lee, and John Preskill · 2014
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Time-dependent many-variable variational Monte Carlo method for nonequilibrium strongly correlated electron systems
Kota Ido, Takahiro Ohgoe, and Masatoshi Imada · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Uniqueness of the Fisher–Rao metric on the space of smooth densities
Martin Bauer, Martins Bruveris, and Peter W Michor · 2016
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Pymanopt: A python toolbox for optimization on manifolds using automatic differentiation
James Townsend, Niklas Koep, and Sebastian Weichwald · 2016
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Solving the quantum many-body problem with artificial neural networks
Giuseppe Carleo and Matthias Troyer · 2017
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Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Inferring the quantum density matrix with machine learning
Kyle Cranmer, Siavash Golkar, and Duccio Pappadopulo · 2019
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Backflow transformations via neural networks for quantum many-body wave functions
Integrable nonparametric flows
David Pfau and Danilo Rezende · 2020
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Normalizing flows on tori and spheres
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael Albergo, Gurtej Kanwar, Phiala Shanahan, and Kyle Cranmer · 2020
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Deep autoregressive models for the efficient variational simulation of many-body quantum systems
Or Sharir, Yoav Levine, Noam Wies, Giuseppe Carleo, and Amnon Shashua · 2020
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Quantum natural gradient
James Stokes, Josh Izaac, Nathan Killoran, and Giuseppe Carleo · 2020
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An efficient quantum algorithm for the time evolution of parameterized circuits
Stefano Barison, Filippo Vicentini, and Giuseppe Carleo · 2021
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Flow-based sampling for multimodal distributions in lattice field theory
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Di Luo and Bryan K Clark · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Theory of variational quantum simulation
Xiao Yuan, Suguru Endo, Qi Zhao, Ying Li, and Simon C Benjamin · 2019
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Backpack: Packing more into backprop
Felix Dangel, Frederik Kunstner, and Philipp Hennig · 2020
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nflows: normalizing flows in PyTorch, November 2020
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2020
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Geometry of variational methods: dynamics of closed quantum systems
Lucas Hackl, Tommaso Guaita, Tao Shi, Jutho Haegeman, Eugene Demler, and Ignacio Cirac · 2020
Cited alongside, same era.
Deep quantum geometry of matrices
Xizhi Han, Sean A Hartnoll, et al · 2020
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Daniel C Hackett, Chung-Chun Hsieh, Michael S Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, and Phiala E Shanahan · 2021
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Neural quantum states for supersymmetric quantum gauge theories
Xizhi Han and Enrico Rinaldi · 2021
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Towards a variational Jordan-Lee-Preskill quantum algorithm
Junyu Liu, Jinzhao Sun, and Xiao Yuan · 2021
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2021
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Continuous-variable neural-network quantum states and the quantum rotor model
James Stokes, Saibal De, Shravan Veerapaneni, and Giuseppe Carleo · 2021
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Rayleigh-Gauss-Newton optimization with enhanced sampling for variational Monte Carlo
Robert J Webber and Michael Lindsey · 2021
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Ab-initio study of interacting fermions at finite temperature with neural canonical transformation
Hao Xie, Linfeng Zhang, and Lei Wang · 2021
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Overcoming barriers to scalability in variational quantum Monte Carlo
Tianchen Zhao, Saibal De, Brian Chen, James Stokes, and Shravan Veerapaneni · 2021
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Matrix-model simulations using quantum computing, deep learning, and lattice Monte Carlo
Enrico Rinaldi, Xizhi Han, Mohammad Hassan, Yuan Feng, Franco Nori, Michael McGuigan, and Masanori Hanada · 2022
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