Model-based Optimization of Superconducting Qubit Readout
- URL: http://arxiv.org/abs/2308.02079v2
- Date: Mon, 5 Feb 2024 20:08:26 GMT
- Title: Model-based Optimization of Superconducting Qubit Readout
- Authors: Andreas Bengtsson, Alex Opremcak, Mostafa Khezri, Daniel Sank,
Alexandre Bourassa, Kevin J. Satzinger, Sabrina Hong, Catherine Erickson,
Brian J. Lester, Kevin C. Miao, Alexander N. Korotkov, Julian Kelly, Zijun
Chen, Paul V. Klimov
- Abstract summary: We demonstrate model-based readout optimization for superconducting qubits.
We observe 1.5% error per qubit with a 500ns end-to-end duration and minimal excess reset error from residual resonator photons.
This technique can scale to hundreds of qubits and be used to enhance the performance of error-correcting codes and near-term applications.
- Score: 59.992881941624965
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Measurement is an essential component of quantum algorithms, and for
superconducting qubits it is often the most error prone. Here, we demonstrate
model-based readout optimization achieving low measurement errors while
avoiding detrimental side-effects. For simultaneous and mid-circuit
measurements across 17 qubits, we observe 1.5% error per qubit with a 500ns
end-to-end duration and minimal excess reset error from residual resonator
photons. We also suppress measurement-induced state transitions achieving a
leakage rate limited by natural heating. This technique can scale to hundreds
of qubits and be used to enhance the performance of error-correcting codes and
near-term applications.
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