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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.02010 |
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| _version_ | 1866929605003182080 |
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| author | Hauschild, Johannes Unfried, Jakob Anand, Sajant Andrews, Bartholomew Bintz, Marcus Borla, Umberto Divic, Stefan Drescher, Markus Geiger, Jan Hefel, Martin Hémery, Kévin Kadow, Wilhelm Kemp, Jack Kirchner, Nico Liu, Vincent S. Möller, Gunnar Parker, Daniel Rader, Michael Romen, Anton Scalet, Samuel Schoonderwoerd, Leon Schulz, Maximilian Soejima, Tomohiro Thoma, Philipp Wu, Yantao Zechmann, Philip Zweng, Ludwig Mong, Roger S. K. Zaletel, Michael P. Pollmann, Frank |
| author_facet | Hauschild, Johannes Unfried, Jakob Anand, Sajant Andrews, Bartholomew Bintz, Marcus Borla, Umberto Divic, Stefan Drescher, Markus Geiger, Jan Hefel, Martin Hémery, Kévin Kadow, Wilhelm Kemp, Jack Kirchner, Nico Liu, Vincent S. Möller, Gunnar Parker, Daniel Rader, Michael Romen, Anton Scalet, Samuel Schoonderwoerd, Leon Schulz, Maximilian Soejima, Tomohiro Thoma, Philipp Wu, Yantao Zechmann, Philip Zweng, Ludwig Mong, Roger S. K. Zaletel, Michael P. Pollmann, Frank |
| contents | TeNPy (short for 'Tensor Network Python') is a python library for the simulation of strongly correlated quantum systems with tensor networks. The philosophy of this library is to achieve a balance of readability and usability for new-comers, while at the same time providing powerful algorithms for experts. The focus is on MPS algorithms for 1D and 2D lattices, such as DMRG ground state search, as well as dynamics using TEBD, TDVP, or MPO evolution. This article is a companion to the recent version 1.0 release of TeNPy and gives a brief overview of the package. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02010 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tensor Network Python (TeNPy) version 1 Hauschild, Johannes Unfried, Jakob Anand, Sajant Andrews, Bartholomew Bintz, Marcus Borla, Umberto Divic, Stefan Drescher, Markus Geiger, Jan Hefel, Martin Hémery, Kévin Kadow, Wilhelm Kemp, Jack Kirchner, Nico Liu, Vincent S. Möller, Gunnar Parker, Daniel Rader, Michael Romen, Anton Scalet, Samuel Schoonderwoerd, Leon Schulz, Maximilian Soejima, Tomohiro Thoma, Philipp Wu, Yantao Zechmann, Philip Zweng, Ludwig Mong, Roger S. K. Zaletel, Michael P. Pollmann, Frank Strongly Correlated Electrons TeNPy (short for 'Tensor Network Python') is a python library for the simulation of strongly correlated quantum systems with tensor networks. The philosophy of this library is to achieve a balance of readability and usability for new-comers, while at the same time providing powerful algorithms for experts. The focus is on MPS algorithms for 1D and 2D lattices, such as DMRG ground state search, as well as dynamics using TEBD, TDVP, or MPO evolution. This article is a companion to the recent version 1.0 release of TeNPy and gives a brief overview of the package. |
| title | Tensor Network Python (TeNPy) version 1 |
| topic | Strongly Correlated Electrons |
| url | https://arxiv.org/abs/2408.02010 |