Neural-Network Approach to Dissipative Quantum Many-Body Dynamics.
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TLDR
In this paper, the authors presented an approach to the effective simulation of the dynamics of open quantum many-body systems based on machine-learning techniques and derived a variational Monte-Carlo algorithm for their time evolution and stationary states.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.read more
Citations
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Machine learning and the physical sciences
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Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems.
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TL;DR: A variational method to efficiently simulate the nonequilibrium steady state of Markovian open quantum systems based on variational Monte Carlo methods and on a neural network representation of the density matrix is developed.
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Constructing neural stationary states for open quantum many-body systems
TL;DR: A new variational scheme based on the neural-network quantum states to simulate the stationary states of open quantum many-body systems, which is dubbed as the neural stationary state ansatz, and shown to simulate various spin systems efficiently.
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Photonic materials in circuit quantum electrodynamics
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References
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