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Model-Based Optimization of Superconducting Qubit Readout.

Andreas Bengtsson1, Alex Opremcak1, Mostafa Khezri1

  • 1Google Quantum AI, Santa Barbara, 93111 California, USA.

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Summary
This summary is machine-generated.

We optimized superconducting qubit measurements to reduce errors in quantum algorithms. This model-based technique achieves 1.5% error per qubit for 17 qubits, improving quantum computing performance.

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Area of Science:

  • Quantum Computing
  • Superconducting Qubits
  • Quantum Information Science

Background:

  • Measurement is critical for quantum algorithms but is error-prone in superconducting qubits.
  • Existing readout methods can introduce detrimental side effects and errors.

Purpose of the Study:

  • To demonstrate a model-based readout optimization technique for superconducting qubits.
  • To minimize measurement errors and associated detrimental effects in quantum systems.

Main Methods:

  • Implemented model-based readout optimization for simultaneous and midcircuit measurements.
  • Characterized performance across 17 superconducting qubits.

Main Results:

  • Achieved 1.5% error per qubit with a 500 ns end-to-end measurement duration.
  • Minimized excess reset error from residual resonator photons.
  • Suppressed measurement-induced state transitions, with leakage rate limited by natural heating.

Conclusions:

  • Model-based readout optimization significantly reduces measurement errors in superconducting qubits.
  • The technique is scalable to hundreds of qubits.
  • This method can enhance quantum error correction and near-term quantum applications.