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Neural-Network Approach to Dissipative Quantum Many-Body Dynamics

Michael J. Hartmann and Giuseppe Carleo
Phys. Rev. Lett. 122, 250502 – Published 28 June 2019
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Abstract

In experimentally realistic situations, quantum systems are never perfectly isolated and the coupling to their environment needs to be taken into account. Often, the effect of the environment can be well approximated by a Markovian master equation. However, solving this master equation for quantum many-body systems becomes exceedingly hard due to the high dimension of the Hilbert space. Here we present an approach to the effective simulation of the dynamics of open quantum many-body systems based on machine-learning techniques. We represent the mixed many-body quantum states with neural networks in the form of restricted Boltzmann machines and derive a variational Monte Carlo algorithm for their time evolution and stationary states. We document the accuracy of the approach with numerical examples for a dissipative spin lattice system.

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  • Received 28 February 2019

DOI:https://doi.org/10.1103/PhysRevLett.122.250502

© 2019 American Physical Society

Physics Subject Headings (PhySH)

Statistical Physics & ThermodynamicsAtomic, Molecular & OpticalCondensed Matter, Materials & Applied PhysicsNetworksQuantum Information, Science & Technology

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Neural Networks Take on Open Quantum Systems

Published 28 June 2019

Simulating a quantum system that exchanges energy with the outside world is notoriously hard, but the necessary computations might be easier with the help of neural networks.

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Authors & Affiliations

Michael J. Hartmann1,2,3 and Giuseppe Carleo4

  • 1Institute of Photonics and Quantum Sciences, Heriot-Watt University Edinburgh EH14 4AS, United Kingdom
  • 2Google Research, Erika-Mann-Str. 33, 80636 München, Germany
  • 3Department of Physics, University of Erlangen-Nürnberg (FAU), 91058 Erlangen, Germany
  • 4Center for Computational Quantum Physics, Flatiron Institute, 162 5th Avenue, New York, New York 10010, USA

See Also

Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems

Alexandra Nagy and Vincenzo Savona
Phys. Rev. Lett. 122, 250501 (2019)

Variational Neural-Network Ansatz for Steady States in Open Quantum Systems

Filippo Vicentini, Alberto Biella, Nicolas Regnault, and Cristiano Ciuti
Phys. Rev. Lett. 122, 250503 (2019)

Constructing neural stationary states for open quantum many-body systems

Nobuyuki Yoshioka and Ryusuke Hamazaki
Phys. Rev. B 99, 214306 (2019)

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Issue

Vol. 122, Iss. 25 — 28 June 2019

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