Talks/Conferences
Upcoming
Quantum Thermalization: Emerging Paradigms and Open Challenges in Nonequilibrium Physics
Talk: Bias-Driven Entanglement and Sign Phase Transition in Random Tensor Networks
We study entanglement and sign phase transitions in biased random tensor networks. We tackle this problem in three ways: numerically, by mapping it to a classical stat mech model, and by mean-field treatment on the Bethe (tree) lattice. We show that the disorder-averaged stat mech model maps onto a Blume-Capel-type model. This predicts a phase diagram containing both first- and second-order transition lines meeting at a tricritical point. This is a new structure in entanglement transitions, distinct from percolation, Yang-Lee, or measurement-induced criticality.
Past
Work in progress.