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arXiv · 2003.07031

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

Mohammad Yosefpor·Mohammad Reza Mostaan·Sadegh Raeisi
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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.

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Mohammad Yosefpor, Mohammad Reza Mostaan, Sadegh Raeisi. 2020-08-16. Finding semi-optimal measurements for entanglement detection using Autoencoder Neural Networks. https://doi.org/10.1088/2058-9565%2Faba34c

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