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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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The z-transform is a fundamental tool in digital signal processing, enabling the analysis of discrete-time systems through its various properties. It is an invaluable tool for analyzing discrete-time systems, offering a range of properties that simplify complex signal manipulations. One fundamental property is linearity. For any two discrete-time signals, the z-transform of their linear combination equals the same linear combination of their individual z-transforms. This property is essential...
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Related Experiment Video

Updated: May 7, 2025

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

8.9K

Optimizing qubit performance through smoothing techniques.

Ivan P Malashin1, Igor S Masich2, Vadim S Tynchenko2

  • 1Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005, Moscow, Russia. ivan.p.malashin@gmail.com.

Scientific Reports
|January 2, 2025
PubMed
Summary

Signal smoothing algorithms enhance qubit performance by reducing variability and improving stability. This leads to more precise Hamiltonian spectrum determination and longer qubit coherence times.

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Last Updated: May 7, 2025

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Area of Science:

  • Quantum Computing
  • Quantum Information Science

Background:

  • Experimental variability and instability in qubit chips hinder performance.
  • Accurate determination of the Hamiltonian spectrum is crucial for qubit control.

Purpose of the Study:

  • To enhance qubit performance using signal smoothing algorithms.
  • To mitigate experimental variability and improve qubit stability.
  • To facilitate data processing for qubit spectroscopy.

Main Methods:

  • Application of signal smoothing algorithms to qubit chips.
  • Optimization of qubit operation through smoothing techniques.
  • Utilizing two-tone qubit spectroscopy for data transformation and calibration.

Main Results:

  • Improved precision in determining the Hamiltonian spectrum on two-tone spectroscopy maps.
  • Enhanced accuracy in qubit parameter construction.
  • Mitigation of experimental variability leading to increased qubit stability.

Conclusions:

  • Signal smoothing is an effective approach to enhance qubit performance.
  • Precise Hamiltonian spectrum determination improves qubit state calibration.
  • The study demonstrates a pathway to longer qubit coherence times through improved calibration.