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

Published on: August 03, 2023

Impact Score: 8.22

Arxiv code: Arxiv:2308.02079

Summary

• What is new: Shows a model-based readout optimization technique that drastically reduces measurement errors in superconducting qubits.
• Why this is important: Measurements in quantum algorithms, especially with superconducting qubits, are highly error-prone.
• What the research proposes: Introduced a model-based readout optimization that minimizes errors and avoids side-effects like residual resonator photons.
• Results: Achieved a low 1.5% error rate per qubit across 17 qubits with a 500ns measurement duration and significantly reduced leakage rates.

Technical Details

Technological frameworks used: Model-based readout optimization

Models used: nan

Data used: Measurements across 17 superconducting qubits

Potential Impact

Quantum computing companies, businesses in sectors like cryptography, material science, and pharmaceuticals that are potential users of quantum algorithms.

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