Finding Semi-Optimal Measurements for Entanglement Detection Using Autoencoder Neural Networks

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Yosefpor, Mohammad | 2021

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 54169 (04)
University: Sharif University of Technology
Department: Physics
Advisor(s): Raeisi, Sadegh
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 measurements for 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
Keywords:
Quantum Information Theory ; Neural Networks ; Machine Learning ; Autoencoder ; Entanglement ; Artificial Intelligence