Extracting many-particle entanglement entropy from observables using supervised machine learning

by Prof. Richard Berkovits

Bar Ilan University

at Condensed Matter Seminar

Mon, 04 Mar 2019, 11:30
Sacta-Rashi Building for Physics (54), room 207

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.

Created on 26-02-2019 by Meidan, Dganit (dganit)
Updaded on 26-02-2019 by Meidan, Dganit (dganit)