In this talk I want to introduce shadow sequence estimation. This is a protocol for learning noise in (random) quantum circuits in a flexible and scalable manner. It arises essentially as a combination of randomised shadow estimation (in the Huang-Kueng-Preskill sense) and randomised benchmarking, a time-honoured gate-fidelity estimation protocol. I will introduce the protocol, sketch the mathematics behind its correctness and scalability, and then I will (hopefully) demonstrate its usefulness through several example estimation protocols, namely unitary optimisation, crosstalk tomography and a robust state shadow estimation protocol.
This talk is based on arXiv: 2110.13178.
(Please note the change to the date of this seminar.)
ATL 3100A and Virtual Via Zoom
