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arXiv · 2111.07051

Predicting non-Markovian superconducting qubit dynamics from tomographic reconstruction

Haimeng Zhang·Bibek Pokharel·E. M. Levenson-Falk·Daniel Lidar
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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 cross-talk 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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Haimeng Zhang, Bibek Pokharel, E. M. Levenson-Falk, Daniel Lidar. 2021-11-13. Predicting non-Markovian superconducting qubit dynamics from tomographic reconstruction. https://doi.org/10.1103/physrevapplied.17.054018

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