Predicting Non-Markovian Superconducting-Qubit Dynamics from Tomographic Reconstruction

Haimeng Zhang, Bibek Pokharel, E.M. Levenson-Falk, and Daniel Lidar

Phys. Rev. Applied 17, 054018 – Published 11 May 2022

Abstract

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.

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Received 29 November 2021
Revised 27 January 2022
Accepted 21 March 2022

DOI:https://doi.org/10.1103/PhysRevApplied.17.054018

Physics Subject Headings (PhySH)

Open quantum systems & decoherence Quantum fluctuations & noise Quantum information with solid state qubits Quantum tomography Superconducting quantum optics

Superconducting qubits

Non-Markovian processes Quantum master equation

Statistical Physics & Thermodynamics

Authors & Affiliations

Haimeng Zhang ^1,2,*, Bibek Pokharel ^2,3, E.M. Levenson-Falk ^2,3, and Daniel Lidar ^1,2,3,4

¹Department of Electrical & Computer Engineering, University of Southern California, Los Angeles, California 90089, USA
²Center for Quantum Information Science & Technology, University of Southern California, Los Angeles, California 90089, USA
³Department of Physics & Astronomy, University of Southern California, Los Angeles, California 90089, USA
⁴Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA

^*haimeng@usc.edu

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Vol. 17, Iss. 5 — May 2022

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Physical Review Applied