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Calibration of syndrome measurements in a single experiment
by
Epping, Michael
, Szangolies, Jochen
, Wimmer, Christian
in
Calibration
/ Circuits
/ Error analysis
/ Error correction
/ Error correction & detection
/ Error reduction
/ Experiments
/ Noise measurement
/ noisy measurements
/ Quantum computers
/ Quantum computing
/ quantum error correction
/ quantum error mitigation
/ quantum process tomography
/ syndrome measurements
/ Tomography
2024
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Calibration of syndrome measurements in a single experiment
by
Epping, Michael
, Szangolies, Jochen
, Wimmer, Christian
in
Calibration
/ Circuits
/ Error analysis
/ Error correction
/ Error correction & detection
/ Error reduction
/ Experiments
/ Noise measurement
/ noisy measurements
/ Quantum computers
/ Quantum computing
/ quantum error correction
/ quantum error mitigation
/ quantum process tomography
/ syndrome measurements
/ Tomography
2024
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Do you wish to request the book?
Calibration of syndrome measurements in a single experiment
by
Epping, Michael
, Szangolies, Jochen
, Wimmer, Christian
in
Calibration
/ Circuits
/ Error analysis
/ Error correction
/ Error correction & detection
/ Error reduction
/ Experiments
/ Noise measurement
/ noisy measurements
/ Quantum computers
/ Quantum computing
/ quantum error correction
/ quantum error mitigation
/ quantum process tomography
/ syndrome measurements
/ Tomography
2024
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Calibration of syndrome measurements in a single experiment
Journal Article
Calibration of syndrome measurements in a single experiment
2024
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Overview
Quantum error correction can reduce the effects of noise in quantum systems, e.g. in metrology or most notably in quantum computing. Typically, this requires making measurements that provide information about the errors that have occurred in the system. However, these syndrome measurements themselves introduce noise into the system, for example by using noisy gates. A complete characterization of the measurements is very costly. Here we describe a calibration method to obtain the syndrome statistics taking into account the additional noise sources. All calibration data are extracted from a single experiment in which the syndrome measurement is performed twice in a row. Thus, our method allows an accurate evaluation of syndrome measurements with significantly less effort than existing methods. We give examples of the application of this method to noise estimation and error correction. Finally, we discuss the results of experiments performed on an IBM quantum computer.
Publisher
IOP Publishing
Subject
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