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Finding semi-optimal measurements for entanglement detection using autoencoder neural networks

Yosefpor, M ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1088/2058-9565/aba34c
  3. Publisher: IOP Publishing Ltd , 2020
  4. Abstract:
  5. 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. © 2020 IOP Publishing Ltd
  6. Keywords:
  7. Autoencoders neural networks ; Deep learning ; Machine learning ; Neural networks ; Separability problem ; Learning systems ; Quantum optics ; Complete information ; Entanglement detection ; Entanglement witness ; Incomplete measurements ; Machine learning techniques ; Quantum-information ; Optimal measurements ; Quantum technologies ; Quantum entanglement
  8. Source: Quantum Science and Technology ; Volume 5, Issue 4 , 16 July , 2020
  9. URL: https://iopscience.iop.org/article/10.1088/2058-9565/aba34c