Southwest Quantum Information and Technology

Predicting non-Markovian superconducting qubit dynamics from tomographic reconstruction

Presenting Author: Haimeng Zhang, University of Southern California
Contributing Author(s): Bibek Pokharel, E.M. Levenson-Falk, and Daniel Lidar

Non-Markovian noise presents a particularly relevant challenge in understanding and combating decoherence in quantum computers, yet is challenging to capture in terms of simple models. Here we show that a simple phenomenological dynamical model known as the post-Markovian master equation (PMME) accurately captures and predicts non-Markovian noise in a superconducting qubit system. The PMME is constructed using experimentally measured state dynamics of an IBM Quantum Experience cloud-based quantum processor, and the model thus constructed successfully predicts the non-Markovian dynamics observed in later experiments. The model also allows the extraction of information about crosstalk and measures of non-Markovianity. We demonstrate definitively that the PMME model predicts subsequent dynamics of the processor better than the standard Markovian master equation.

Read this article online: https://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.17.054018

(Session 9a : Friday from 3:45 pm - 4:15 pm)