Year: 2025
Type: article
Abstract: The recently proposed Clifford circuits augmented matrix product states (CA-MPSs) [X. Qian, J. Huang, and M. Qin, Phys. Rev. Lett. 133, 190402 (2024)PRLTAO0031-900710.1103/PhysRevLett.133.190402] seamlessly augment density matrix renormalization groups with Clifford circuits. In CA-MPSs, the entanglement from stabilizers is transferred to the Clifford circuits, which can be easily handled according to the Gottesman-Knill theorem. As a result, an MPS needs only to deal with the nonstabilizer entanglement, which largely reduces the bond dimension and the resource required for the accurate simulation of many-body systems. In this Letter, we generalize CA-MPSs to the framework of the time-dependent variational principle (TDVP) for time evolution simulations. In this method, we apply Clifford circuits to the resulting MPS in each TDVP step with a two-site sweeping process similar as in density matrix renormalization groups, aiming at reducing the entanglement entropy in the MPS, and the Hamiltonian is transformed accordingly using the chosen Clifford circuits. Similar as in CA-MPSs, the Clifford circuits does not increase the number of terms in the Hamiltonian, which makes the overhead very small in the new method. We test this method in XXZ chain, 2D Heisenberg model, and Kitaev honeycomb model. The results show that the Clifford circuits augmented TDVP method can reduce the entanglement entropy in the time evolution process and hence makes the simulation reliable for a longer time. The Clifford circuits augmented time-dependent variational principle provides a useful tool for the simulation of the time evolution process of many-body systems in the future.
Source: Physical Review Letters
Authors: Xiangjian Qian, Jiale Huang, Mingpu Qin
Institution: Shanghai Jiao Tong University
Language: English
Topic: Quantum many-body systems
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: G7403570660, G8985750264
OpenAlex ID: W4409473232
