Topic: many-body systems

Vijay Balasubramanian connects quantum chaos with the complexity of time evolution. One approach bounds the smallest circuit needed to simulate physical dynamics; another measures how quickly and ergodically a state explores its accessible Hilbert space. Examples from spin chains, billiards, many-body systems and random matrices distinguish chaotic and integrable behaviour. Applications address geometric conjectures for complexity in quantum gravity and possible failure of effective field theory near spacelike singularities.

Granular matter can serve as a prototype for exploring the rich physics of many-body systems driven far from equilibrium. This talk will outline a new direction for granular physics with macroscopic particles, where acoustic levitation compensates the forces due to gravity and eliminates frictional interactions with supporting surfaces in order to focus on particle interactions. Levitating small particles by intense ultrasound fields in air makes it possible to manipulate and control their positions and assemble them into larger aggregates. The small air viscosity implies that the regime of un

Neural networks can learn and recognize subtle correlations in high dimensional inputs. However, neural networks are simply many-body systems with strong non-linearities and disordered interactions. Hence, many-body physical systems with similar interactions should be able to show neural network-like behavior. Here we show neural network-like behavior in the nucleation dynamics of promiscuously interacting molecules with multiple stable crystalline phases. Using a combination of theory and experiments, we show how the physics of the system dictates relationships between the difficulty of the