### Deep Learning and Quantum Gravity

Koji Hashimoto, Osaka University, 12:00 EDT*Abstract:* 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 in understanding how the
correspondence works. I discuss similarities between the quantum gravity and
deep learning architecture, by regarding the neural network as a discretized
spacetime. In particular, the questions such as, when, why and how a neural network
can be a space or a spacetime, may lead to a novel way to look at machine learning.
I implement concretely 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.