Learning and Benchmarking Quantum Channels

8:00 am – 11:00 am, Monday March 17 // Session MAR-A36 //Anaheim Convention Center, 258A (Level 2)

Chair:: Robin Harper, University of Sydney
Topics:
Sponsored by: DQI

Contracted Quantum Learning of Random Unitary Channels

8:00 am – 8:12 am

Presenter: Alexander M Jürgens (University of California, Los Angeles)

Authors: Scott Smart (University of California, Los Angeles), Joseph Peetz (University of California, Los Angeles), Prineha Narang (University of California, Los Angeles)

Online learning algorithms for quantum state tomography, where the learning model is sequentially updated in response to new data, comprise a promising and exciting topic in contemporary quantum information research. Seeking to apply online learning to quantum process tomography, we give a protocol for analyzing a class of orthogonal random unitary channels using Pauli learning with a contracted quantum learning procedure. Our approach utilizes multi-objective Pauli and unitary minimization, and allows for the learning of locally equivalent channels. We demonstrate the success of this technique for varying degrees of noise, as well as characterize the scalability of this approach, particularly for sparse evaluations of operators.

PRESENTATIONS (13)