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Wavelet correlation noise analysis for qubit operation variable time series.

Amanda E Seedhouse1,2, Nard Dumoulin Stuyck3,4, Santiago Serrano3,4

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Wavelet analysis reveals noise correlations in quantum computing, aiding error mitigation and fabrication. This technique helps identify noise sources like two-level fluctuators in silicon-metal-oxide-semiconductor quantum dots.

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Area of Science:

  • Quantum computing
  • Quantum information science
  • Solid-state quantum systems

Background:

  • Characterizing qubit noise is crucial for improving coherence and fidelity.
  • Spin qubits in MOS quantum dots experience complex noise from two-level fluctuators.
  • Large datasets from qubit experiments require advanced analysis techniques.

Purpose of the Study:

  • To propose and demonstrate wavelet-based analysis for qubit noise characterization.
  • To enhance understanding of noise sources by decomposing signals in time and frequency.
  • To identify microscopic causes of noise and improve multi-qubit operations.

Main Methods:

  • Application of wavelet-based signal decomposition techniques.
  • Analysis of data from a two-qubit experiment in SiMOS quantum dots.
  • Utilizing feedback on operation variables to study noise correlations.

Main Results:

  • Identification of noise features at specific times and frequencies.
  • Correlations linked to two-level fluctuators and hyperfine nuclei.
  • Elucidation of pathways for scalable feedback in multi-qubit systems.

Conclusions:

  • Wavelet analysis is effective for understanding complex qubit noise.
  • The findings aid in targeted error mitigation and device fabrication.
  • This approach supports the development of more robust quantum computing systems.