SimCATS is a python framework for simulating charge stability diagrams (CSDs) typically measured during the tuning process of qubits.
Project description
SimCATS
Simulation of CSDs for Automated Tuning Solutions (SimCATS
) is a python framework for simulating charge stability
diagrams (CSDs) typically measured during the tuning process of qubits.
Installation
The framework supports python versions 3.7 - 3.11 and can be installed with pip:
pip install simcats
Alternatively, the SimCATS
package can be installed by cloning the GitHub repository, navigating to the folder
containing the setup.py
file and executing
pip install .
For installation in development/editable mode use the option -e
.
Examples / Tutorials
After the package is installed, a good starting point is a look into the Jupyter Notebook
example_SimCATS_simulation_class.ipynb
, which provides an overview for the usage of the simulation class offered by
the framework.
For more detailed examples and explanations of the geometric ideal CSD simulation using Total Charge Transitions (TCTs),
have a look at the Jupyter Notebook example_SimCATS_IdealCSDGeometric.ipynb
. This notebook also includes a hint
regarding the generation of required labels for training algorithms, that might need line labels defined as start and
end points or require semantic information about particular transitions.
Tests
The test are written for the PyTest
framework but should also work with the unittest
framework.
To run the tests install the packages pytest
, pytest-cov
and pytest-xdist
with
pip install pytest pytest-cov pytest-xdist
and then simply run following command in the terminal of your choice:
pytest --cov=simcats -n auto --dist loadfile .\tests\
The argument --cov=simcats
enables a coverage summary of the SimCATS
package, the argument -n auto
enables the
test to run with multiple threads (auto will choose as many threads as possible, but can be replaced with a specific
number of threads to use) and the argument --dist loadfile
specifies that each file should be executed only by one
thread.
Documentation
The official documentation is hosted on ReadtheDocs, but can also be build locally.
To do this, first install the packages sphinx
, sphinx-rtd-theme
, sphinx-autoapi
,myst-nb
and jupytext
with
pip install sphinx sphinx-rtd-theme sphinx-autoapi myst-nb jupytext
and then, in the docs
folder, execute the following command in a terminal of your choice:
.\make html
To view the generated HTML documentation open the file docs\build\html\index.html
with a browser of your choice.
Structure of SimCATS
The main user interface for SimCATS
is the class Simulation
, which combines all the necessary functionalities to
measure (simulate) a CSD and to adjust the parameters for the simulated measurement. The class Simulation
and default
configurations for the simulation (default_configs
) can be imported directly from simcats
. Asides from that,
SimCATS
contains the subpackages ideal_csd
, sensor
, distortions
, and support_functions
. These are described in
the following sections.
Module simulation
An instance of the simulation class requires
- an implementation of the
IdealCSDInterface
for the simulation of ideal CSD data, - an implementation of the
SensorInterface
for the simulation of the sensor (dot) reaction based on the ideal CSD data, and - (optionally) implementations of the desired types of distortions, which can be implementations from either
OccupationDistortionInterface
,SensorPotentialDistortionInterface
, orSensorResponseDistortionInterface
.
With an initialized instance of the Simulation
class it is possible to run simulations using the measure
function
(see example_SimCATS_simulation_class.ipynb
).
Subpackage ideal_csd
This subpackage contains the IdealCSDInterface
, that is used by the Simulation
class for the generation of ideal CSD
data, and an implementation of the IdealCSDInterface
(IdealCSDGeometric
) based on our geometric simulation approach.
Additionally, in the subpackage geometric
, it contains the functions used by IdealCSDGeometric
, including the
implementation of the total charge transition (TCT) definition and functions for calculating the occupations using TCTs.
Subpackage distortions
The distortions subpackage contains the DistortionInterface
from which the OccupationDistortionInterface
, the
SensorPotentialDistortionInterface
, and the SensorResponseDistortionInterface
are derived. Distortion functions used
in the Simulation
class have to implement these specific interfaces. Implemented distortions included in the
subpackage are:
- white noise, generated by sampling from a normal distribution,
- pink noise, generated using the package colorednoise (https://github.com/felixpatzelt/colorednoise),
- random telegraph noise (RTN), generated using the algorithm described in "Toward Robust Autotuning of Noisy Quantum Dot Devices" by Ziegler et al. (RTN is called sensor jumps there),
- dot jumps, simulated using the algorithm described in "Toward Robust Autotuning of Noisy Quantum Dot Devices" by Ziegler et al. (In the
Simulation
class this is applied to a whole block of rows or columns, but there is also a function for applying it linewise.), and - lead transition blurring, simulated using gaussian blurring.
The implementations also offer the option to set ratios (parameter ratio
), for how often the distortion is active
(f.e. dot jumps may only happen sometimes and not in every measurement). Moreover, it is also possible to sample the
noise parameters from a given sampling range. This can be done by using an object of type ParameterSamplingInterface
.
Classes for randomly sampling from a normal distribution or a uniform distribution within a given range are available in
the subpackage support_functions
.
In this case the strength is randomly chosen from the given range for every measurement.
Additionally, it is possible to specify, that this range should be a smaller subrange of the provided range.
This allows to restrict distortion fluctuations during a simulation while allowing a large variety of different strengths
for the initialization of the objects.
RTN, dot jumps and lead transition blurring are applied in the pixel domain. However, the length of jumps or the strength
of the blurring should be consistent in the voltage domain even if the resolution changes. Therefore, the parameters
are given in the voltage domain and adjusted according to the resolution in terms of pixel per voltage.
If a measurement with a continuous voltage sweep and averaging for each pixel is simulated, the noise strength of the white and pink noise should be adjusted if the resolution (volt per pixel) changes, as some of the noise is smoothed out. This smoothing depends on the type of averaging that is used and is not incorporated in the default implementation.
Subpackage sensor
This subpackage contains the SensorInterface
that defines how a sensor simulation must be implemented to be used by the Simulation
class. The SensorPeakInterface
provides the desired representation for the definition of the Coulomb peaks used by the sensor. SensorGeneric
implements the SensorInterface
and offers functions for simulating the sensor response and the sensor potential. It offers the possibility to simulate with a single or multiple sensor peaks. Current implementations of the SensorPeakInterface
are SensorPeakGaussian
and SensorPeakLorentzian
.
Subpackage support_functions
This subpackage contains support functions, which are used by the end user as well as from different functions of the framework.
fermi_filter1d
is an implementation of a one dimensional Fermi-Dirac filter.plot_csd
is a function for plotting one and two-dimensional CSDs. The function can also be used to plot ground truth data (seeexample_SimCATS_simulation_class.ipynb
for examples).rotate_points
is a function for simply rotating coordinates (stored in a (n, 2) shaped array) by a given angle. This is especially used during the generation of the ideal data.ParameterSamplingInterface
defines an interface that can be implemented for randomly sampling (fluctuating) strengths of distortions.NormalSamplingRange
andUniformSamplingRange
are implementations of theParameterSamplingInterface
.
License, CLA, and Copyright
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Contributions must follow the Contributor License Agreement. For more information see the CONTRIBUTING.md file at the top level of the GitHub repository.
Copyright © 2023 Forschungszentrum Jülich GmbH - Central Institute of Engineering, Electronics and Analytics (ZEA) - Electronic Systems (ZEA-2)
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