Learning Entanglement Quasiprobability from Noisy and Incomplete Data

TL;DR

This paper introduces a deep learning method to efficiently reconstruct entanglement quasiprobabilities from incomplete measurements, significantly reducing resource requirements and enabling scalable entanglement detection.

Contribution

It presents a novel neural network approach that bypasses full quantum state tomography for entanglement characterization, improving scalability and efficiency.

Findings

Over 30-fold reduction in reconstruction error compared to traditional methods.

Successful experimental validation on photonic entangled states.

Effective entanglement detection with fewer measurement resources.

Abstract

Negativities in quasiprobability distributions, a foundational concept originating in quantum optics, serve as a fundamental signature of quantum nonclassicality, with entanglement quasiprobabilities offering a necessary and sufficient criterion for entanglement. However, practical reconstruction of entanglement quasiprobabilities conventionally requires full quantum state tomography, severely limiting scalability. Here, we propose a deep-learning framework that reconstructs entanglement quasiprobabilities directly from incomplete local projective measurements, bypassing full state reconstruction. Using a residual neural network, partial measurement outcomes are mapped to high-fidelity entanglement quasiprobabilities. Numerical benchmarks up to three qubits show more than a $30\times$ reduction in reconstruction error compared with state-of-the-art tomographic methods. Experimental validation on photonic entangled states demonstrates reconstruction and entanglement detection with substantially reduced measurement resources. Our results establish machine-learning-assisted reconstruction of entanglement quasiprobabilities as a scalable and practical tool for entanglement characterization in quantum optical systems.