Joint IQIM/AWS Seminar Series
Abstract: Building error-corrected quantum computers relies crucially on measuring and modeling noise on candidate devices. In particular, optimal error correction requires knowing the noise that occurs in the device as it executes circuits. Here we discuss one method of doing this, with a focus on devices running surface code style circuits. We execute the protocol on a superconducting device using 39 of its qubits. We show how to extract from these experiments the noise needed to build models of various sophistication. These models are designed to capture elements of the noise that are important in assessing how effective error correction might be. We show that as error rates approach those that will be needed for successful error correction then the complexities of real world noise means that models capturing such additional complexities become increasingly important in predicting the success of the error correcting protocol.
Attendees joining in person must demonstrate that they comply with Caltech's vaccination requirements (present Caltech ID or AWS ID or vaccination and booster confirmation).