Journal article
Parallel time-dependent variational principle algorithm for matrix product states
- Abstract:
- Combining the time-dependent variational principle (TDVP) algorithm with the parallelization scheme introduced by Stoudenmire and White for the density matrix renormalization group (DMRG), we present the first parallel matrix product state (MPS) algorithm capable of time evolving one-dimensional (1D) quantum lattice systems with long-range interactions. We benchmark the accuracy and performance of the algorithm by simulating quenches in the long-range Ising and XY models. We show that our code scales well up to 32 processes, with parallel efficiencies as high as 86%. Finally, we calculate the dynamical correlation function of a 201-site Heisenberg XXX spin chain with 1/r2 interactions, which is challenging to compute sequentially. These results pave the way for the application of tensor networks to increasingly complex many-body systems.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, 1.8MB, Terms of use)
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- Publisher copy:
- 10.1103/PhysRevB.101.235123
Authors
- Publisher:
- American Physical Society
- Journal:
- Physical Review B More from this journal
- Volume:
- 101
- Issue:
- 23
- Article number:
- 235123
- Publication date:
- 2020-06-05
- Acceptance date:
- 2020-06-05
- DOI:
- EISSN:
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2469-9969
- ISSN:
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2469-9950
- Language:
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English
- Keywords:
- Pubs id:
-
1113998
- Local pid:
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pubs:1113998
- Deposit date:
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2020-07-27
Terms of use
- Copyright holder:
- American Physical Society
- Copyright date:
- 2020
- Rights statement:
- © 2020 American Physical Society.
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from the American Physical Society at: https://doi.org/10.1103/PhysRevB.101.235123
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