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3-Clause BSD License
SyTen: A symmetry-protected tensor networks toolkit in C++
Sam Mardazad1, Martin Grundner2, Felix Lachenmaier, Nils-Oliver Linden, Teresa Reinhard, Leo Stenzel, Andreas Swoboda, and Claudius Hubig
1Heriot-Watt University
2Ludwig-Maximilians-Universität München
First published:
Jan. 10, 2023
No permanent ID assigned yet. URL: https://opendata.physik.lmu.de/wMSyolmi9xgpy1Z
Keywords:
syten
tensor network
matrix product state
symmetry protected
c++
python
tree tensor networks
iPEPS
ground state
many particle
time evolution

Mardazad, S., Grundner, M., Lachenmaier, F., Linden, N., Reinhard, T., Stenzel, L., Swoboda, A., and Hubig, C. (2023): SyTen: A symmetry-protected tensor networks toolkit in C++. LMU Munich, Faculty of Physics. (Software). URL: https://syten.eu

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