Entanglement and discord classification via deep learning

TL;DR

This paper introduces a deep learning approach using convolutional autoencoders to classify quantum entanglement and discord, achieving high accuracy and enabling the generation of bound entangled states.

Contribution

It presents a novel deep learning framework for classifying quantum entanglement and discord, including the generation of bound entangled states, with high accuracy and efficiency.

Findings

High classification accuracy for entanglement and discord detection

Effective generation of bound entangled states

Reduced training time compared to traditional methods

Abstract

In this work, we propose a deep learning-based approach for quantum entanglement and discord classification using convolutional autoencoders. We train models to distinguish entangled from separable bipartite states for $d \times d$ systems with local dimension $d$ ranging from two to seven, which enables identification of bound and free entanglement. Through extensive numerical simulations across various quantum state families, we demonstrate that our model achieves high classification accuracy. Furthermore, we leverage the learned representations to generate samples of bound entangled states, the rarest form of entanglement and notoriously difficult to construct analytically. We separately train the same convolutional autoencoders architecture for detecting the presence of quantum discord and show that the model also exhibits high accuracy while requiring significantly less training time.