Detecting quantum entanglement with unsupervised learning

TL;DR

This paper introduces an unsupervised machine learning approach using complex neural networks to efficiently detect quantum entanglement in high-dimensional systems, achieving over 97.5% accuracy and revealing detailed entanglement structures.

Contribution

It presents a novel complex-valued neural network framework for entanglement detection that requires only separable states for training, applicable to various quantum resources.

Findings

Achieves high detection accuracy (>97.5%) in systems up to ten qubits.

Capable of identifying partial entanglement among subsystems.

Applicable to detecting other quantum resources like nonlocality and steerability.

Abstract

Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, there still lacks an efficient and scalable way to detecting these useful features, especially for high-dimensional and multipartite quantum systems. In this work, we exploit the convexity of samples without the desired quantum features and design an unsupervised machine learning method to detect the presence of such features as anomalies. Particularly, in the context of entanglement detection, we propose a complex-valued neural network composed of pseudo-siamese network and generative adversarial net, and then train it with only separable states to construct non-linear witnesses for entanglement. It is shown via numerical examples, ranging from two-qubit to ten-qubit systems, that our network is able to achieve high detection accuracy which is above 97.5% on average.Moreover, it is capable of revealing rich structures of entanglement, such as partial entanglement among subsystems. Our results are readily applicable to the detection of other quantum resources such as Bell nonlocality and steerability, and thus our work could provide a powerful tool to extract quantum features hidden in multipartite quantum data.