Analysis of Neural Network Predictions for Entanglement Self-Catalysis

Abstract

Machine learning techniques have been successfully applied to classifying an extensive range of phenomena in quantum theory. From detecting quantum phase transitions to identifying Bell non-locality, it has been established that classical machines can learn genuine quantum features via classical data. Quantum entanglement is one of the uniquely quantum phenomena in that range, as it has been shown that neural networks can be used to classify different types of entanglement. Our work builds on this topic. We investigate whether distinct models of neural networks can learn how to detect catalysis and self-catalysis of entanglement. Additionally, we also study whether a trained machine can detect another related phenomenon - which we dub transfer knowledge. As we build our models from scratch, besides making all the codes available, we can study a whole gamut of paradigmatic measures, including accuracy, execution time, training time, bias in the training data set and so on.

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References in corpus (11)

• Quantum cryptography: Public key distribution and coin tossing
• An introduction to quantum machine learning
• Quantum Darwinism
• Experimental quantum key distribution beyond the repeaterless secret key capacity
• A generalized no-broadcasting theorem
• Experimental Quantum Teleportation of a Two-Qubit Composite System
• Entanglement Classification via Neural Network Quantum States
• Satellite-based photonic quantum networks are small-world
• The view from a Wigner bubble
• Local Transformations of Multiple Multipartite States
• A link between symmetries of critical states and the structure of SLOCC classes in multipartite systems