SciPost Submission Page
Reweighted Time-Evolving Block Decimation for Improved Quantum Dynamics Simulations
by Sayak Guha Roy, Kevin Slagle
Submission summary
| Authors (as registered SciPost users): | Sayak Guha Roy |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2412.08730v3 (pdf) |
| Code repository: | https://github.com/guharoysayak/reweighted_TEBD |
| Date submitted: | Feb. 5, 2026, 6:11 a.m. |
| Submitted by: | Sayak Guha Roy |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Theoretical, Computational |
The author(s) disclose that the following generative AI tools have been used in the preparation of this submission:
Generative AI tool (OpenAI's ChatGPT 5.2) was used to assist with language editing and minor code refactoring, including improvements to data-saving and code organization. All scientific ideas, algorithms, simulations, and analyses presented in this work were conceived, implemented, and validated by the authors.
Abstract
We introduce a simple yet significant improvement to the time-evolving block decimation (TEBD) tensor network algorithm for simulating the time dynamics of strongly correlated one-dimensional (1D) mixed quantum states. The efficiency of 1D tensor network methods stems from using a product of matrices to express either: the coefficients of a wavefunction, yielding a matrix product state (MPS); or the expectation values of a density matrix, yielding a matrix product density operator (MPDO). To avoid exponential computational costs, TEBD truncates the matrix dimension while simulating the time evolution. However, when truncating an MPDO, TEBD does not favor the likely more important low-weight expectation values, such as $\langle c_i^\dagger c_j \rangle$, over the exponentially many high-weight expectation values, such as $\langle c_{i_1}^\dagger c^\dagger_{i_2} \cdots c_{i_n} \rangle$ of weight $n$, despite the critical importance of the low-weight expectation values. Motivated by this shortcoming, we propose a reweighted TEBD (rTEBD) algorithm that deprioritizes high-weight expectation values by a factor of $γ^{-n}$ during the truncation. This simple modification (which only requires reweighting certain matrices by a factor of $γ$ in the MPDO) makes rTEBD significantly more accurate than the TEBD time-dependent simulation of an MPDO, and competitive with and sometimes better than TEBD using MPS. Furthermore, by prioritizing low-weight expectation values, rTEBD preserves conserved quantities to high precision.
Author indications on fulfilling journal expectations
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- Present a breakthrough on a previously-identified and long-standing research stumbling block