Machine learning nonlocal correlations and phase transitions

Talk by Rafael Chaves

International Institute of Physics, Natal, Brazil


The ability to witness nonlocal correlations lies at the core of foundational aspects of quantum mechanics and its application in the processing of information. Commonly, this is achieved via the violation of Bell inequalities. Unfortunately, however, their systematic derivation quickly becomes unfeasible as the scenario of interest grows in complexity. In the first part of the talk we propose a machine learning approach for the detection and quantification of nonlocality. Not only can the machine learn to quantify nonlocality, but discover new kinds of nonlocal correlations inaccessible with other current methods as well. In a second part, using the axial next-nearest neighbor Ising (ANNNI) model as a benchmark, we show how unsupervised learning can detect phase transitions and employing supervised learning we show that transfer learning becomes possible: a machine trained only with nearest-neighbour interactions can learn to identify a new type of phase occurring when next-nearest-neighbour interactions are introduced.


Thu 19 Dec 19


DTU Fysik


DTU Physics
Building 311
1st floor / Lounge