Explore improved quantum data analysis techniques in this 59-minute talk on shadow tomography. Delve into a paper that presents the best-known solution for the adaptive Quantum Shadow Tomography problem, focusing on optimizing the dependence on the number of observables, dimension of the unknown state, and error tolerance. Discover the connections between this quantum problem and classical adaptive data analysis. Learn about quantum threshold search, teacher properties, and stable threshold decision. Gain insights into the proof and implications of this research, presented by Ryan O'Donnell in collaboration with Costin Bădescu.
Overview
Syllabus
Intro
Data Analysis
Multiple Events
Data Adaptive Data Analysis
Warmup
Shadow Tomography Problem
Previous Work
Route
Quantum Threshold Search
Teacher Properties
Dream
Stable Threshold Decision
Proof
Conclusion
Taught by
Ryan O'Donnell