New directions in quantum state learning and testing

I will talk about:
. New efficient algorithms for quantum state tomography (the quantum analogue of estimating a probability distribution).
. Why you should care about the difference between total variation distance and Hellinger distance and KL divergence and chi-squared divergence.
. Quantum-inspired improvements to the classical problem of independence testing.

Includes joint work with Steven T. Flammia (Amazon)

ATL 3100A and Virtual Via Zoom.

*We strongly encourage attendees to use their full name (and if possible, their UMD credentials) to join the zoom session.*