Condensed Matter Seminar: Extracting many-particle entanglement entropy from observables using supervised machine learning

Abstract:

Entanglement, which quantifies non-local correlations in quantum mechanics, is the fascinating concept behind much of aspiration towards quantum technologies. Nevertheless, directly ,measuring the entanglement of a  many-particle system is very challenging. In this talk we show that via supervised machine learning using a convolutional neural network (all these concepts will be explained during the talk), we can infer the entanglement from a measurable observable for a disordered interacting quantum many-particle system.  Excellent agreement was found, except for several rare region which in a previous study were identified as belonging to an inclusion of a Griffiths-like quantum phase. Training the network on a test set with different parameters (in the same phase) also works quite well. General thoughts on the application of machine learning to physics will be discussed.

Event Organizer: Prof. Sasha Gerber