Simulating quantum dynamics in two-dimensional lattices with tensor network influence functional belief propagation
Abstract
Describing nonequilibrium quantum dynamics remains a significant computational challenge due to the growth of spatial entanglement. The tensor network influence functional (TN-IF) approach mitigates this problem for computing the time evolution of local observables by encoding the subsystem's influence functional path integral as a matrix product state, thereby shifting the resource governing computational cost from spatial entanglement to temporal entanglement. We extend the applicability of the TN-IF method to two-dimensional lattices by demonstrating its construction on tree lattices and proposing a belief propagation (BP) algorithm for the TN-IF, termed influence functional BP (IF-BP), to simulate local observable dynamics on arbitrary graphs. Even though the BP algorithm introduces uncontrolled approximation errors on arbitrary graphs, it provides an accurate description for locally treelike lattices. Numerical simulations of the kicked Ising model on a heavy-hex lattice, motivated by a recent quantum experiment, highlight the effectiveness of the IF-BP method, which demonstrates superior performance in capturing long-time dynamics where traditional tensor network state-based methods struggle. Our results further reveal that the temporal entanglement entropy only grows logarithmically with time for this model, resulting in a polynomial computational cost for the whole method. We further construct a cluster expansion of IF-BP to introduce loop correlations beyond the BP approximation, providing a systematic correction to the IF-BP estimate. We demonstrate the power of the cluster expansion of the IF-BP in simulating the quantum quench dynamics of the two-dimensional transverse field Ising model, obtaining numerical results that improve on the state-of-the-art.
Acknowledgement (English)
We thank Jacek Dziarmaga, Tomislav Begušić, and Alessandro Sinibaldi for sharing the iPEPS [92], SPD [47], and t-NQG [48,95] data, respectively, presented in Fig. 16. The authors thank Tomislav Begušić, Giuseppe Carleo, Filippo Vicentini, and Alessandro Sinibaldi for helpful discussions. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of Basic Energy Sciences, Scientific Discovery through Advanced Computing (SciDAC) program under Award No. DE-SC0022088. The quimb library [100] has been used in the numerical experiments. Computations presented here were conducted in the Resnick High Performance Computing Center, a facility supported by Resnick Sustainability Institute at the California Institute of Technology. G.P. acknowledges support from the Eddleman Quantum Graduate Fellowship at Caltech.
Data Availability (English)
The data that support the findings of this article are not publicly available. The data are available from the authors upon reasonable request.
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Additional details
Related works
- Is new version of
- Discussion Paper: arXiv:2504.07344 (arXiv)
Funding
- United States Department of Energy
- DE-SC0022088
Dates
- Submitted
-
2025-04-19
- Accepted
-
2025-11-03
Caltech Custom Metadata
- Caltech groups
- Division of Chemistry and Chemical Engineering (CCE) , Division of Engineering and Applied Science (EAS)
- Publication Status
- Published