# Quantum entanglement recognition

Source: [Quantum entanglement recognition](<https://www.sota2.com/research/paper/68fa3fe41e209131ce8ef3c2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T02:30:24.983Z

Authors: Jun Yong Khoo, Markus Heyl

Published: 2020-07-28T18:00:00.000Z

## Abstract

Entanglement constitutes a key characteristic feature of quantum matter. Its detection, however, still faces major challenges. In this letter, we formulate a framework for probing entanglement based on machine learning techniques. The central element is a protocol for the generation of statistical images from quantum many-body states, with which we perform image classification by means of convolutional neural networks. We show that the resulting quantum entanglement recognition task is accurate and can be assigned a well-controlled error across a wide range of quantum states. We discuss the potential use of our scheme to quantify quantum entanglement in experiments. Our developed scheme provides a generally applicable strategy for quantum entanglement recognition in both equilibrium and nonequilibrium quantum matter.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2007.14397>)

[PDF](<https://arxiv.org/pdf/2007.14397.pdf>)