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.
The framework supports Python versions 3.7 - 3.11 and installs via 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 the installation in development/editable mode, use the option -e
.
After installing the package, a good starting point is a look into the Jupyter Notebook
example_SimCATS_simulation_class.ipynb
, which provides an overview of 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), 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.
The tests 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 run the following command:
pytest --cov=simcats -n auto --dist loadfile .\tests\
The argument
--cov=simcats
enables a coverage summary of theSimCATS
package,-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--dist loadfile
specifies that each file should be executed only by one thread.
The official documentation is hosted on ReadtheDocs, but can also be built 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:
.\make html
To view the generated HTML documentation, open the file docs\build\html\index.html
.
The primary user interface for SimCATS
is the class Simulation
, which combines all the necessary functionalities to
measure (simulate) a CSD and adjust the parameters for the simulated measurement. The class Simulation
and default
configurations for the simulation (default_configs
) can be imported directly from simcats
. Aside from that,
SimCATS
contains the subpackages ideal_csd
, sensor
, distortions
, and support_functions
, described in
the following sections.
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
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
).
This subpackage contains the IdealCSDInterface
used by the Simulation
class and an implementation of
the IdealCSDInterface
(IdealCSDGeometric
) based on our geometric simulation approach.
Additionally, it contains in the subpackage geometric
the functions used by IdealCSDGeometric
, including the
implementation of the total charge transition (TCT) definition and functions for calculating the occupations using TCTs.
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 or Fermi-Dirac blurring.
The implementations also offer the option to set ratios (parameter ratio
) for the occurrence of the distortion (e.g. 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 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 restricting distortion fluctuations during a simulation while enabling 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 jump length or the blurring strength 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.
For a simulated measurement with a continuous voltage sweep involving an averaging for each pixel, the noise strength of the
white and pink noise should be adjusted if the resolution (volt per pixel) changes, due to smoothing out the noise. This smoothing depends on the type of averaging used and is not incorporated in the default implementation.
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 the sensor uses. SensorGeneric
implements the SensorInterface
and offers functions for simulating the sensor response and potential. It offers the possibility to simulate with a single peak or multiple sensor peaks. Current implementations of the SensorPeakInterface
are SensorPeakGaussian
and SensorPeakLorentzian
.
This subpackage contains support functions, which are used by the end user and by different functions of the framework.
fermi_filter1d
is an implementation of a one-dimensional Fermi-Dirac filter.plot_csd
plots one and two-dimensional CSDs. The function can also plot ground truth data (seeexample_SimCATS_simulation_class.ipynb
for examples).rotate_points
simply rotates coordinates (stored in a (n, 2) shaped array) by a given angle. It is especially used during the generation of the ideal data.ParameterSamplingInterface
defines an interface for randomly sampled (fluctuated) strengths of distortions.NormalSamplingRange
andUniformSamplingRange
are implementations of theParameterSamplingInterface
.
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 of the GitHub repository.
Copyright © 2023 Forschungszentrum Jülich GmbH - Central Institute of Engineering, Electronics and Analytics (ZEA) - Electronic Systems (ZEA-2)