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

TL;DR

This paper introduces the Schmidt tensor network state (Schmidt TNS), an efficient method for representing the Schmidt decomposition of quantum many-body states, enabling scalable analysis of entanglement in complex quantum systems.

Contribution

The paper proposes the Schmidt TNS, combining tensor networks and MPS to efficiently encode entanglement spectra, applicable to finite and infinite quantum states with nontrivial bipartitions.

Findings

Successfully simulates ground states of frustrated spin models

Shows MPS encoding of Schmidt coefficients remains weakly entangled

Demonstrates exponential speedup potential in state sampling tasks

Abstract

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 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.