During this time of lockdown, the centre for quantum software and information (QSI) at the University of Technology Sydney has launched an online seminar series. With talks once or twice a week from leading researchers in the field, meQuanics is supporting this series by mirroring the audio from each talk. I would encourage if you listen to this episode, to visit and subscribe to the UTS:QSI YouTube page to see each of these talks with the associated slides to help it make more sense.
https://www.youtube.com/watch?v=N1We-MpoJlM&t=5s
SEMINAR 1
TITLE: Understanding Crosstalk in Quantum Processors
SPEAKER: A/Prof Robin Blume-Kohout
AFFILIATION: Quantum Performance Lab, Sandia National Laboratories, Albuquerque, New Mexico
HOSTED BY: A/Prof Chris Ferrie, UTS Centre for Quantum Software and Information
ABSTRACT: Multi-qubit quantum processors fail – i.e., deviate from ideal behavior – in many ways. One of the most important, especially as the number of qubits grows, is crosstalk. But “crosstalk” refers to a wide range of distinct phenomena. In this talk, I will present a precise and rigorous framework that we have developed for defining and classifying crosstalk errors, and compare it to existing ad hoc definitions. Then, I will present two protocols that we are deploying to detect and characterize crosstalk, and show how we are using them to break down and demystify the error behavior of testbed-class quantum processors in the wild.
SEMINAR 2
TITLE: Hold the onion: using fewer circuits to characterize your qubits
SPEAKER: Dr Erik Nielsen
AFFILIATION: Quantum Performance Lab, Sandia National Laboratories, Albuquerque, New Mexico
HOSTED BY: A/Prof Chris Ferrie, UTS Centre for Quantum Software and Information
ABSTRACT: Model-based quantum tomography protocols like gate set tomography optimize a noise model with some number of parameters in order to fit experimental data. As the number of qubits increases, two issues emerge: 1) the number of model parameters grows, and 2) the cost of propagating quantum states (density matrices) increases exponentially. The first issue can be addressed by considering reduced models that limit errors to being low-weight and geometrically local. In this talk, we focus on the second issue and present a method for performing approximate density matrix propagation based on perturbative expansions of error generators. The method is tailored to the likelihood optimization problem faced by model-based tomography protocols. We will discuss the advantages and drawbacks of using this method when characterizing the errors in up to 8-qubit systems.