Tensor Network Efficiently Representing Schmidt Decomposition of Quantum Many-Body States

Efficient methods to access the entanglement of a quantum many-body state, where the complexity generally scales exponentially with the system size N, have long been a concern. Here we propose the Schmidt tensor network state (Schmidt TNS) that efficiently represents the Schmidt decomposition of finite-and even infinite-size quantum states with nontrivial bipartition boundary. The key idea is to represent the Schmidt coefficients (i.e., entanglement spectrum) and transformations in the decomposition to tensor networks (TNs) with linearly scaled complexity versus N. Specifically, the transformations are written as the TNs formed by local unitary tensors, and the Schmidt coefficients are encoded in a positive -definite matrix product state (MPS). Translational invariance can be imposed on the TNs and MPS for the infinite-size cases. The validity of Schmidt TNS is demonstrated by simulating the ground state of the quasi-one-dimensional spin model with geometrical frustration. Our results show that the MPS encoding the Schmidt coefficients is weakly entangled even when the entanglement entropy of the decomposed state is strong. This justifies the efficiency of using MPS to encode the Schmidt coefficients, and promises an exponential speedup on the full-state sampling tasks.

Automated summary

Efficient methods for accessing quantum entanglement in many-body systems have been a long-standing concern due to exponential scaling complexity. In this study, a Schmidt tensor network state (Schmidt TNS) is proposed, which efficiently represents the Schmidt decomposition of quantum states of finite and infinite sizes with nontrivial bipartition boundary. The key idea is to represent the Schmidt coefficients and transformations as tensor networks with linearly scaled complexity. Simulation results demonstrate the validity of the Schmidt TNS, showing that the encoded Schmidt coefficients are weakly entangled, supporting the efficiency of using matrix product states (MPS) for encoding.