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Deep autoregressive models for the efficient variational simulation of many-body quantum systems [article]

Or Sharir, Yoav Levine, Noam Wies, Giuseppe Carleo, Amnon Shashua
2019 arXiv   pre-print
In practical applications, neural-network states inherit numerical schemes used in Variational Monte Carlo, most notably the use of Markov-Chain Monte-Carlo (MCMC) sampling to estimate quantum expectations  ...  Inspired by the state-of-the-art generative models used in machine learning, we propose a specialized Neural Network architecture that supports efficient and exact sampling, completely circumventing the  ...  quantum Monte Carlo (QMC) simulation.  ... 
arXiv:1902.04057v2 fatcat:2jmmln75xngcpbdg5vfxttl47q

Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation [article]

Jeffmin Lin, Gil Goldshlager, Lin Lin
2021 arXiv   pre-print
The combination of neural networks and quantum Monte Carlo methods has arisen as a path forward for highly accurate electronic structure calculations.  ...  We demonstrate that the resulting FermiNet-GA architecture can yield effectively the exact ground state energy for small systems.  ...  We also thank the hospitality of the American Institute of Mathematics (AIM) for the SQuaREs program “Deep learning and quantum Monte Carlo” in 2021.  ... 
arXiv:2112.03491v1 fatcat:wfi4tyhbmfednb5te336czs2bi

Combining Maximum-Likelihood with Deep Learning for Event Reconstruction in IceCube [article]

Mirco Hünnefeld
2021 arXiv   pre-print
The field of deep learning has become increasingly important for particle physics experiments, yielding a multitude of advances, predominantly in event classification and reconstruction tasks.  ...  Most commonly used deep learning architectures fail at utilizing this available information.  ...  The strength of deep learning lies in the universality of its methods. Typical deep learning architectures were developed for a generalized usage in a wide field of applications.  ... 
arXiv:2107.12110v1 fatcat:blajefjjdjd2fhr4hnaroejokq

Quantum Loop Topography for Machine Learning

Yi Zhang, Eun-Ah Kim
2017 Physical Review Letters  
independent Monte Carlo steps.  ...  The loop configuration is guided by characteristic response for defining the phase, which is Hall conductivity for the cases at hand.  ...  the bilinear operator with a single Monte Carlo sample α defining P jk | α ≡ c † j c k α .  ... 
doi:10.1103/physrevlett.118.216401 pmid:28598670 fatcat:qzwui73ylbcu3ihwmblw5cbxim

Convergence to the fixed-node limit in deep variational Monte Carlo [article]

Zeno Schätzle, Jan Hermann, Frank Noé
2020 arXiv   pre-print
Variational quantum Monte Carlo (QMC) is an ab-initio method for solving the electronic Schrödinger equation that is exact in principle, but limited by the flexibility of the available ansatzes in practice  ...  This analysis helps understanding the superb performance of deep variational ansatzes, and will guide future improvements of the neural network architectures in deep QMC.  ...  ACKNOWLEDGMENTS We gratefully acknowledge funding and support from the  ... 
arXiv:2010.05316v1 fatcat:tt6ln3lvs5eepkhhs5ysgtnzyi

Introductory notes for the Acta IMEKO second issue 2022

Francesco Lamonaca
2022 ACTA IMEKO  
cloud via a modified extension of the Monte Carlo integration approach.  ...  Covre et al., in "Monte Carlo-based 3D surface point cloud volume estimation by exploding local cubes faces", propose a state-of-the-art algorithm for estimating the 3D volume enclosed in a surface point  ... 
doi:10.21014/acta_imeko.v11i2.1307 fatcat:trgfunyiyfgvvbh22stzfaob2e

Style-based quantum generative adversarial networks for Monte Carlo events [article]

Carlos Bravo-Prieto, Julien Baglio, Marco Cè, Anthony Francis, Dorota M. Grabowska, Stefano Carrazza
2022 arXiv   pre-print
We propose and assess an alternative quantum generator architecture in the context of generative adversarial learning for Monte Carlo event generation, used to simulate particle physics processes at the  ...  The new quantum generator architecture leads to a generalization of the state-of-the-art implementations, achieving smaller Kullback-Leibler divergences even with shallow-depth networks.  ...  More specifically, we explore the uses of QNNs for the generation of Monte Carlo events through quantum generative adversarial networks (qGANs) [31, 32] .  ... 
arXiv:2110.06933v2 fatcat:j4vxx2lkmbbbpgc3m6oo6qtlty

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

Markus Schmitt, Moritz Reh
2021 arXiv   pre-print
The introduction of Neural Quantum States (NQS) has recently given a new twist to variational Monte Carlo (VMC).  ...  However, performant implementations are crucial to reach the numerical state of the art. Here, we present a Python codebase that supports arbitrary NQS architectures and model Hamiltonians.  ...  The recent proposal of neural quantum states (NQS) as a new ansatz class for variational quantum Monte Carlo opened new perspectives and holds the potential to overcome existing limitations [10] .  ... 
arXiv:2108.03409v2 fatcat:obmrrs6stfexzdxpnpam5vkbse

Combining Maximum-Likelihood with Deep Learning for Event Reconstruction in IceCube

Mirco Huennefeld, Rasha Abbasi, Markus Ackermann, Jenni Adams, Juanan Aguilar, M. Ahlers, Maryon Ahrens, Cyril Martin Alispach, Antonio Augusto Alves Junior, Najia Moureen Binte Amin, Rui An, Karen Andeen (+371 others)
2022 37th International Cosmic Ray Conference  
The field of deep learning has become increasingly important for particle physics experiments, yielding a multitude of advances, predominantly in event classification and reconstruction tasks.  ...  Most commonly used deep learning architectures fail at utilizing this available information.  ...  The strength of deep learning lies in the universality of its methods. Typical deep learning architectures were developed for a generalized usage in a wide field of applications.  ... 
doi:10.18154/rwth-2022-06025 fatcat:avu24ddh5jdtbmn3dra57twxju

Style-based quantum generative adversarial networks for Monte Carlo events

Carlos Bravo-Prieto, Julien Baglio, Marco Cè, Anthony Francis, Dorota M. Grabowska, Stefano Carrazza
2022 Quantum  
We propose and assess an alternative quantum generator architecture in the context of generative adversarial learning for Monte Carlo event generation, used to simulate particle physics processes at the  ...  The new quantum generator architecture leads to a generalization of the state-of-the-art implementations, achieving smaller Kullback-Leibler divergences even with shallow-depth networks.  ...  More specifically, we explore the uses of QNNs for the generation of Monte Carlo events through quantum generative adversarial networks (qGANs) [31, 32] .  ... 
doi:10.22331/q-2022-08-17-777 fatcat:6lefybhvn5fchpvnkw5rpb7uyy

Machine learning quantum phases of matter beyond the fermion sign problem

Peter Broecker, Juan Carrasquilla, Roger G. Melko, Simon Trebst
2017 Scientific Reports  
Using auxiliary-field quantum Monte Carlo (QMC) simulations to sample the many-fermion system, we show that the Green's function (but not the auxiliary field) holds sufficient information to allow for  ...  the distinction of different fermionic phases via a CNN.  ...  Our choice of employing a deep CNN is rooted in the above observation that the configurations generated from a quantum Monte Carlo algorithm can be often interpreted as "images".  ... 
doi:10.1038/s41598-017-09098-0 pmid:28821785 pmcid:PMC5562897 fatcat:kvdgxythcbbufjpexpszfc6dhy

Generalized transfer matrix states from artificial neural networks

Lorenzo Pastori, Raphael Kaubruegger, Jan Carl Budich
2019 Physical review B  
By means of an explicit example using variational Monte Carlo, we also show that GTMS can parametrize critical quantum many-body ground states to a good accuracy.  ...  Our findings suggest that GTMS are a promising candidate for the study of critical and dynamical quantum many-body systems.  ...  EFFICIENT CALCULATION OF SECOND RÉNYI ENTROPY Here we review the algorithm for the computation of the second Rényi entropy applicable to Monte Carlo calculations, introduced in [51].  ... 
doi:10.1103/physrevb.99.165123 fatcat:kr4jhlaz2vd2bgfklamqmtlq3a

Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need? [article]

Leon Gerard, Michael Scherbela, Philipp Marquetand, Philipp Grohs
2022 arXiv   pre-print
Recently the combination of deep learning with Monte Carlo methods has emerged as a promising way to obtain highly accurate energies and moderate scaling of computational cost.  ...  Using our method we establish a new benchmark by calculating the most accurate variational ground state energies ever published for a number of different atoms and molecules.  ...  Additionally, we thank Nicholas Gao for providing his results and data and Rafael Reisenhofer for providing valuable feedback to the manuscript.  ... 
arXiv:2205.09438v2 fatcat:kz36zwr5dzbujnndydoglqbcn4

Sample generation for the spin-fermion model using neural networks [article]

Georgios Stratis, Phillip Weinberg, Tales Imbiriba, Pau Closas, Adrian E. Feiguin
2022 arXiv   pre-print
Quantum Monte-Carlo simulations of hybrid quantum-classical models such as the double exchange Hamiltonian require calculating the density of states of the quantum degrees of freedom at every step.  ...  The simplicity of the architecture we use in conjunction with the model agnostic form of the neural networks can enable fast sample generation without the need of a researcher's intervention.  ...  GS was supported by the Roux Institute at Northeastern University and the Harold Alfond foundation.  ... 
arXiv:2206.07753v1 fatcat:7a5io2dgbvahvbr765hemsrhxu

D2.5: Electronic structure E-CAM modules IV

Michele Ruggeri
2020 Zenodo  
9 software modules delivered to the E-CAM repository in the area of Electronic Structure responding to requests of users, and their documentation.  ...  There are several methods for the theoretical study of electronic structure systems, such as the Density Functional Theory, Quantum Monte Carlo, mean field and quantum chemical methods, and more recently  ...  classical and quantum Monte Carlo and various quantum chemistry methods), each implemented in different softwares and employing different data standards.  ... 
doi:10.5281/zenodo.3931516 fatcat:esgu7g42ivgnjpfj65d7koojsy
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