Finding semi-optimal measurements for entanglement detection using Autoencoder Neural Networks

Abstract

Entanglement is one of the key resources of quantum information science which makes identification of entangled states essential to a wide range of quantum technologies and phenomena. This problem is however both computationally and experimentally challenging. Here we use autoencoder neural networks to find semi-optimal set of incomplete measurements that are most informative for the detection of entangled states. We show that it is possible to find high-performance entanglement detectors with as few as three measurements. Also, with the complete information of the state, we develop a neural network that can identify all two-qubits entangled states almost perfectly. This result paves the way for automatic development of efficient entanglement witnesses and entanglement detection using machine learning techniques.

4 pages, 5 figures

References in corpus (5)

• Entanglement detection
• Lower bounds on concurrence and separability conditions
• Understanding entanglement as resource: locally distinguishing unextendible product bases
• Entanglement Classification via Neural Network Quantum States
• Decomposition of symmetric multipartite observable

Cited by in corpus (4)

• Building separable approximations for quantum states via neural networks
• Quantum verification and estimation with few copies
• Effect of Weak Measurement Reversal on Quantum Correlations in a Correlated Amplitude Damping Channel, with a Neural Network Perspective
• Entanglement Witness Derived By Using Kolmogorov-Arnold Networks