UWashington Distinguished Seminar in Optimization and Data: Ryan O'Donnell
Topic
​​​​​​​New directions in (quantum) distribution learning and testing
Speakers
Details
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)
Additional Information
This is a hybrid event.