Analysis of Neural Network Predictions for Entanglement Self-Catalysis

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 for pure states. 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.

Automated summary

Machine learning techniques have been successfully applied to classify various phenomena in quantum theory, including quantum phase transitions and Bell non-locality. This study focuses on whether different models of neural networks can learn to detect catalysis, self-catalysis, and transfer knowledge in entanglement. By building models from scratch, the study also examines paradigmatic measures such as accuracy, execution time, training time, and bias in the training data set.