Spectroscopy #31
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Spectroscopy #31
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yaelbh
reviewed
May 4, 2021
| __analysis_class__ = SpectroscopyAnalysis | ||
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| # Supported units for spectroscopy. | ||
| __units__ = {"Hz": 1.0, "kHz": 1.e3, "MHz": 1.e6, "GHz": 1.e9} |
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It's the role of Terra to provide conversion methods between units. It's possibly already somewhere in Terra, and if so, should be used. If not then some day it can be good to move this logic to Terra.
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I had a look in terra and could not find a unit management system.
yaelbh
reviewed
May 4, 2021
yaelbh
reviewed
May 4, 2021
yaelbh
reviewed
May 4, 2021
yaelbh
reviewed
May 4, 2021
* Improved docstring.
* Improved docstring. * Implemented _fit_quality.
* Fixed typing issue.
* Made meas level 1 default.
yaelbh
reviewed
May 9, 2021
* Changed to a Gaussian fit * Amended result. * Added plotting. * Default meas level is now classified.
nkanazawa1989
requested changes
May 11, 2021
* Added check on backend.
* Minor cosmetics.
wshanks
reviewed
May 11, 2021
* Fixed error scalling issue in SVD.
nkanazawa1989
requested changes
May 23, 2021
…aProcessor as this will often be reused. * Aligned with PR qiskit-community#39 on options.
…into spectroscopy
* Added a bugfix in the SVD node.
Merged
eggerdj
commented
Jun 1, 2021
nkanazawa1989
approved these changes
Jun 2, 2021
wshanks
approved these changes
Jun 2, 2021
paco-ri
pushed a commit
to paco-ri/qiskit-experiments
that referenced
this pull request
Jul 11, 2022
* Qubit spectroscopy experiment which applies a GaussianSquare pulse and scans the frequency to find the resonance of the qubit.
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Summary
This is a qubit spectroscopy experiment that scans the frequency of a
GaussianSquarepulse.Details and comments
The spectroscopy experiment implements the
Spectroscopyexperiment and aSpectroscopyAnalysisclass. This is done with a pulse schedule with aset_frequencyinstruction followed by aGaussianSquarepulse encapsulated in a gate. AGaussianSquarepulse was chosen as it allows us to have Square, Gaussian, and GaussianSquare pulses. The circuits that are run areThe analysis is based on a fit to a Gaussian function with default values based on the x and y data.