\u3010Date\u3011
10:00-11:30, Thur. Nov. 10<\/p>\n
\u3010Speaker\u3011
Koji HASHIMOTO \/ Kyoto University<\/p>\n
\u3010Title\u3011
\"Deep learning and emergent spacetime\"<\/p>\n
\u3010Abstract\u3011
Formulating quantum gravity is one of the final goals of fundamental physics. Recent progress in string theory brought a concrete formulation called AdS\/CFT correspondence, in which a gravitational spacetime emerges from lower-dimensional non gravitational-quantum systems, but we still lack understanding how the correspondence works.
At this end, I discuss similarities between quantum gravity and deep learning architecture, by regarding the neural network as a discretized spacetime. The application of machine learning in physics has been successful in some subjects, which I will review, and I use it to implement the AdS\/CFT framework into a deep learning architecture, and show the emergence of a curved spacetime as a neural network, from a given teacher data of quantum systems.<\/p>\n