Sampling asymmetric open quantum systems for artificial neural networks

• URL: http://arxiv.org/abs/2012.10990v2
• Date: Sat, 17 Apr 2021 09:53:10 GMT
• Title: Sampling asymmetric open quantum systems for artificial neural networks
• Authors: Oliver K\"astle and Alexander Carmele
• Abstract summary: We present a hybrid sampling strategy which takes asymmetric properties explicitly into account, achieving fast convergence times and high scalability for asymmetric open systems. We highlight the universal applicability of artificial neural networks, underlining the universal applicability of neural networks.
• Score: 77.34726150561087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While established neural network approaches based on restricted Boltzmann machine architectures and Metropolis sampling methods are well suited for symmetric open quantum systems, they result in poor scalability and systematic errors for setups without symmetries of translational invariance, independent of training parameters such as the sample size. To overcome this representational limit, we present a hybrid sampling strategy which takes asymmetric properties explicitly into account, achieving fast convergence times and high scalability for asymmetric open systems, underlining the universal applicability of artificial neural networks.

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