Estimating Entanglement Entropy via Variational Quantum Circuits with Classical Neural Networks

TL;DR

This paper introduces QNEE, a hybrid quantum-classical neural network approach that accurately estimates quantum entanglement entropy and classifies quantum phases, demonstrated on the 1D XXZ Heisenberg model.

Contribution

The paper presents a novel hybrid quantum-classical neural network method for entropy estimation and phase classification in quantum systems.

Findings

QNEE accurately estimates von Neumann and Renyi entropies.

QNEE detects phase transitions with high sensitivity.

QNEE provides eigenvalues and eigenstates of the density matrix.

Abstract

Entropy plays a crucial role in both physics and information science, encompassing classical and quantum domains. In this work, we present the Quantum Neural Entropy Estimator (QNEE), a novel approach that combines classical neural network (NN) with variational quantum circuits to estimate the von Neumann and Renyi entropies of a quantum state. QNEE provides accurate estimates of entropy while also yielding the eigenvalues and eigenstates of the input density matrix. Leveraging the capabilities of classical NN, QNEE can classify different phases of quantum systems that accompany the changes of entanglement entropy. Our numerical simulation demonstrates the effectiveness of QNEE by applying it to the 1D XXZ Heisenberg model. In particular, QNEE exhibits high sensitivity in estimating entanglement entropy near the phase transition point. We expect that QNEE will serve as a valuable tool for quantum entropy estimation and phase classification.