Mitigating Errors on Superconducting Quantum Processors through Fuzzy Clustering

Abstract

Quantum utility has been severely limited in superconducting quantum hardware until now by the modest number of qubits and the relatively high level of control and readout errors, due to the intentional coupling with the external environment required for manipulation and readout of the qubit states. Practical applications in the Noisy Intermediate Scale Quantum (NISQ) era rely on Quantum Error Mitigation (QEM) techniques, which are able to improve the accuracy of the expectation values of quantum observables by implementing classical post-processing analysis from an ensemble of repeated noisy quantum circuit runs. In this work, we focus on a recent QEM technique that uses Fuzzy C-Means (FCM) clustering to specifically identify measurement error patterns. For the first time, we report a proof-of-principle validation of the technique on a 2-qubit register, obtained as a subset of a real NISQ 5-qubit superconducting quantum processor based on transmon qubits. We demonstrate that the FCM-based QEM technique allows for reasonable improvement of the expectation values of single- and two-qubit gates based quantum circuits, without necessarily invoking state-of-the-art coherence, gate, and readout fidelities.

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Cited by in corpus (2)

• Bayesian mitigation of measurement errors in multiqubit experiments
• Challenges and opportunities in the supervised learning of quantum circuit outputs