Parallel time-dependent variational principle algorithm for matrix product 
states

Secular, P.; Gourianov, N.; Lubasch, M.; Dolgov, S.; Clark, S. R.; Jaksch, D.

doi:10.1103/PhysRevB.101.235123

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学術論文

Parallel time-dependent variational principle algorithm for matrix product states

MPS-Authors

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Clark, S. R.
H.H. Wills Physics Laboratory, University of Bristol;
Quantum Condensed Matter Dynamics, Condensed Matter Dynamics Department, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society;

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https://dx.doi.org/10.1103/PhysRevB.101.235123
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フルテキスト (公開)

PhysRevB.101.235123.pdf
(出版社版), 3MB

付随資料 (公開)

supplemental-parallel-TDVP.pdf
(付録資料), 582KB

引用

Secular, P., Gourianov, N., Lubasch, M., Dolgov, S., Clark, S. R., & Jaksch, D. (2020). Parallel time-dependent variational principle algorithm for matrix product states. Physical Review B, 101(23):. doi:10.1103/PhysRevB.101.235123.

引用: https://hdl.handle.net/21.11116/0000-0006-9610-E

要旨

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.