Entanglement Witness Derived By Using Kolmogorov-Arnold Networks

TL;DR

This paper introduces an interpretable Kolmogorov-Arnold Network model that detects quantum entanglement with high accuracy and identifies key features, enabling simpler entanglement witnesses without full state tomography.

Contribution

The work presents a novel application of Kolmogorov-Arnold Networks as entanglement witnesses, with feature importance analysis leading to simplified, more efficient detection methods.

Findings

Achieved 94% accuracy in entanglement detection.

Identified key features for entanglement classification.

Developed simplified entanglement witnesses without full state tomography.

Abstract

We utilize Kolmogorov-Arnold Networks to design an interpretable model capable of detecting quantum entanglement within a set of nine-parameter two-qubit states. This network serves as an entanglement witness, achieving an accuracy of $94\%$ in distinguishing entangled states. Additionally, by analyzing the output functions of the KAN models, we explore the significance of each parameter (feature) in identifying the presence of entanglement. This analysis enables us to rank the features and eliminate the less significant ones, leading to the development of new entanglement witness functions that rely on fewer number of features, and hence do not require complete state tomography for their evaluation.