# A Separability-Entanglement Classifier via Machine Learning

## TL;DR

This paper introduces a machine learning-based classifier for quantum state entanglement, offering faster and more accurate identification compared to traditional methods, thus advancing quantum information processing.

## Contribution

The paper presents a novel machine learning approach for quantum state separability classification, outperforming existing criteria in speed and accuracy.

## Key findings

- Outperforms traditional methods in speed and accuracy
- Effective for generic quantum states
- Facilitates exploration of quantum entanglement

## Abstract

The problem of determining whether a given quantum state is entangled lies at the heart of quantum information processing, which is known to be an NP-hard problem in general. Despite the proposed many methods such as the positive partial transpose (PPT) criterion and the k-symmetric extendibility criterion to tackle this problem in practice, none of them enables a general, effective solution to the problem even for small dimensions. Explicitly, separable states form a high-dimensional convex set, which exhibits a vastly complicated structure. In this work, we build a new separability-entanglement classifier underpinned by machine learning techniques. Our method outperforms the existing methods in generic cases in terms of both speed and accuracy, opening up the avenues to explore quantum entanglement via the machine learning approach.