Time-dependent variational principle for open quantum systems with artificial neural networks
We develop a variational approach to simulating the dynamics of open quantum many-body systems using deep autoregressive neural networks. The parameters of a compressed representation of a mixed quantum state are adapted dynamically according to the Lindblad master equation by employing a time-depen...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
1 December 2021
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| In: |
Physical review letters
Year: 2021, Volume: 127, Issue: 23, Pages: 1-7 |
| ISSN: | 1079-7114 |
| DOI: | 10.1103/PhysRevLett.127.230501 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1103/PhysRevLett.127.230501 Verlag, lizenzpflichtig, Volltext: https://link.aps.org/doi/10.1103/PhysRevLett.127.230501 
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| Author Notes: | Moritz Reh, Markus Schmitt, and Martin Gärttner |
| Summary: | We develop a variational approach to simulating the dynamics of open quantum many-body systems using deep autoregressive neural networks. The parameters of a compressed representation of a mixed quantum state are adapted dynamically according to the Lindblad master equation by employing a time-dependent variational principle. We illustrate our approach by solving the dissipative quantum Heisenberg model in one dimension for up to 40 spins and in two dimensions for a 4×4 system and by applying it to the simulation of confinement dynamics in the presence of dissipation. |
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| Item Description: | Gesehen am 17.12.2021 |
| Physical Description: | Online Resource |
| ISSN: | 1079-7114 |
| DOI: | 10.1103/PhysRevLett.127.230501 |