Modelling non-Markovian noise in driven superconducting qubits
Abhishek Agarwal, Lachlan P. Lindoy, Deep Lall, Francois Jamet, Ivan, Rungger

TL;DR
This paper develops and compares models for non-Markovian noise in superconducting qubits, demonstrating that a qubit-TLS interaction model accurately captures experimental data and varies with drive amplitude, aiding error correction.
Contribution
It introduces a noise modelling approach based on qubit-TLS interactions that effectively characterizes non-Markovian noise in both idle and driven superconducting qubits.
Findings
Purely Markovian models fail to fit experimental data.
Qubit-TLS interaction model closely matches observed noise behaviour.
Noise parameters fluctuate over time but are predictable with drive amplitude.
Abstract
Non-Markovian noise can be a significant source of errors in superconducting qubits. We develop gate sequences utilising mirrored pseudoidentities that allow us to characterise and model the effects of non-Markovian noise on both idle and driven qubits. We compare three approaches to modelling the observed noise: (i) a Markovian noise model, (ii) a model including interactions with a two-level system (TLS), (iii) a model utilising the post Markovian master equation (PMME), which we show to be equivalent to the qubit-TLS model in certain regimes. When running our noise characterisation circuits on a superconducting qubit device we find that purely Markovian noise models cannot reproduce the experimental data. Our model based on a qubit-TLS interaction, on the other hand, is able to closely capture the observed experimental behaviour for both idle and driven qubits. We investigate the…
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Taxonomy
TopicsQuantum Information and Cryptography · Quantum Computing Algorithms and Architecture · Quantum and electron transport phenomena
