Talks

In this talk I will discuss some recent results on boundary degrees of freedom (or edge modes), and their description via an extended phase space structure containing extra boundary fields. Motivated by a slight modification of the covariant phase space formalism, I will show how the use of a boundary Lagrangian enables to include the edge modes in the phase space and to obtain their boundary dynamics. This will be illustrated on the example of Maxwell theory, where in addition the edge modes can be understood as contributing to entanglement entropy. The possible extension of this result to gravity will then be mentioned.

In this talk we describe a systematic way of producing stochastic solutions to the Yang-Baxter equation from a “base solution” that is not necessarily stochastic. This gives rise to a wide class of new families of stochastic (Markovian) vertex models with integrable weights. In various cases, these models are more amenable to algebraic analysis than are their non-stochastic counterparts, sometimes leading to new (and at times unexpected) refined asymptotic results for them. In other cases, these stochastic systems are amenable to probabilistic analysis, yielding results that seem less transparent to access algebraically and that can occasionally shed new light on the original (non-stochastic) model. This is partially based on joint work with Alexei Borodin and Alexey Bufetov.

The quantum Fisher information (QFI) measures the amount of information that a quantum state carries about an unknown parameter. Given a quantum channel, its entanglement-assisted QFI is the maximum QFI of the output state assuming an input state over the system and an arbitrarily large ancilla. Consider N identical copies of a quantum channel, the channel QFI grows either linearly or quadratically with N asymptotically. Here we obtain a simple criterion that determines whether the scaling is linear or quadratic. In both cases, we found a quantum error correction protocol achieving the channel QFI asymptotically and an SDP to solve the optimal code.