Sampling reduced density matrix to extract fine levels of entanglement spectrum and restore entanglement Hamiltonian
Bin-Bin Mao, Yi-Ming Ding, Zhe Wang, Shijie Hu, Zheng Yan
Abstract
The reduced density matrix (RDM) plays a key role in quantum entanglement and measurement, as it allows the extraction of almost all physical quantities related to the reduced degrees of freedom. However, restricted by the degrees of freedom in the environment, the total system size is often limited, let alone the subsystem. To address this challenge, we propose a quantum Monte Carlo scheme with a low technical barrier, enabling precise extraction of the RDM. To demonstrate the power of the method, we present the fine levels of the entanglement spectrum (ES), which is the logarithmic eigenvalues of the RDM. We clearly show the ES for a 1D ladder with a long entangled boundary, and that for the 2D Heisenberg model with a tower of states. Furthermore, we put forward an efficient way to restore the entanglement Hamiltonian in operator-form from the sampled RDM data. Our simulation results, utilizing unprecedentedly large system sizes, establish a practical computational framework for determining entanglement quantities based on the RDM, such as the ES, particularly in scenarios where the environment has a huge number of degrees of freedom. The reduced density matrix enables extraction of entanglement-related quantities but computing it for large quantum systems is challenging. Mao et al. combine quantum Monte Carlo with exact diagonalization to efficiently extract the reduced density matrix and entanglement spectrum in large 1D and 2D systems.