Neural-Network Approach to Dissipative Quantum Many-Body Dynamics
Hartmann M, Carleo G (2019)
Publication Type: Journal article
Publication year: 2019
Journal
Book Volume: 122
Article Number: 250502
Journal Issue: 25
DOI: 10.1103/PhysRevLett.122.250502
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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APA:
Hartmann, M., & Carleo, G. (2019). Neural-Network Approach to Dissipative Quantum Many-Body Dynamics. Physical Review Letters, 122(25). https://dx.doi.org/10.1103/PhysRevLett.122.250502
MLA:
Hartmann, Michael, and Giuseppe Carleo. "Neural-Network Approach to Dissipative Quantum Many-Body Dynamics." Physical Review Letters 122.25 (2019).
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