The documentation can be found on readthedocs. It features an API documentation and an introduction in the form of jupyter notebooks demonstrating how to utilize the package. A complementary theoretical introduction is given in the qopt paper on Phys. Rev. Applied and an older version can be found on the Arxiv.

We set up another open-source repository named qopt-applications to save and exchange quantum simulation and optimal control applications implemented using qopt.

Realistic modelling of qubit systems including noise and constraints imposed by control hardware is required for performance prediction and control optimization of quantum processors. qopt is a software framework for simulating qubit dynamics and robust quantum optimal control considering common experimental situations. It supports modelling of open and closed qubit systems with a focus on the simulation of realistic noise characteristics and experimental constraints. Specifically, the influence of noise can be calculated using Monte Carlo methods, effective master equations or with the filter function formalism, enabling the investigation and mitigation of auto-correlated noise. In addition, limitations of control electronics including finite bandwidth effects can be considered. The calculation of gradients based on analytic results is implemented to facilitate the efficient optimization of control pulses. The software is published under an open source license, well-tested and features a detailed documentation.

Qopt is available on github and the python index Pypi. To install qopt directly from the python index, you can use pip:

pip install qopt

or alternatively download the source code, navigate to the folder containing qopt and install by

pip install . 

or append the command -e to install qopt with symlinks

pip -e install . 

The -e stands for edible as the symlinks allow you to make local changes to the sourcecode.

If you wish to use the plotting features of the quantum toolbox in pythen (QuTiP), then you need to install additional dependencies:

conda install cython pytest pytest-cov jupyter

Then open a conda forge channel:

conda config --append channels conda-forge

and install QuTiP:

conda install qutip

Another optional package is simanneal for the use of simulated annealing for discrete optimization:

conda install simanneal

If you require an additional feature for your work, then please open an issue on github or reach out to me via e-mail [email protected]. There is a list in markdown format with possible extensions to the package.

You can find the patch Notes in a markdown file in the root folder of the package. You can also find it on github.

If you are using qopt for your work then please cite the qopt paper, as the funding of the development depends on the public impact.