Analysis of Neural Network Predictions for Entanglement Self-Catalysis

TL;DR

This paper explores the ability of various neural network models to detect entanglement catalysis and self-catalysis in quantum systems, analyzing their performance and biases.

Contribution

It introduces models trained from scratch to classify quantum entanglement phenomena, including catalysis, self-catalysis, and transfer knowledge, with comprehensive performance analysis.

Findings

Neural networks can classify entanglement catalysis phenomena.

Performance varies with model type and training data bias.

Code and paradigmatic measures are openly available.

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.