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Detecting and tracking drift in quantum information processors.

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Understanding time-varying errors is crucial for quantum information processors. This study introduces a spectral analysis technique to resolve these dynamic errors, improving quantum processor characterization and stability.

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Area of Science:

  • Quantum Information Science
  • Quantum Computing Error Characterization
  • Experimental Quantum Physics

Background:

  • Quantum information processors face diverse errors that fluctuate over time.
  • Current error characterization tools often assume static error rates, leading to inaccuracies.
  • This mismatch can result in processor failures and inefficient experimental design.

Purpose of the Study:

  • To develop a method for resolving time-dependent errors in quantum processors.
  • To improve the accuracy and efficiency of quantum processor characterization.
  • To enable better understanding and suppression of dynamic quantum errors.

Main Methods:

  • Demonstrated a spectral analysis technique for time-series data.
  • Applied the method to randomized benchmarking, gate set tomography, and Ramsey spectroscopy.
  • Utilized data from simulations and trapped-ion qubit experiments.

Main Results:

  • Successfully resolved time dependence in quantum processor error rates.
  • Detected and localized sources of instability in experimental data.
  • Showcased the ability to suppress detected instabilities through drift control.

Conclusions:

  • The spectral analysis technique is a fast, simple, and statistically sound method for characterizing dynamic quantum errors.
  • This approach enhances the reliability of quantum processor characterization protocols.
  • The method facilitates the suppression of time-dependent errors, crucial for advancing quantum computing.