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.
In current quantum computer prototypes information is stored an processed in quantum bits or qubits which are controlled by electric pulses. In order to find the optimal control pulse for a given operation, this package simulates the system under control and applies optimization algorithms to the pulses. These include gradient based algorithms generalizing the GRAPE algorithm [1].
The package sets a focus on realistic noise models to enable noise mitigation through pulse tailoring. Imperfections of the control electronics can also be included in the simulations.
The recommended way is to use conda for the installation. To avoid difficulties, QuTiP needs to be installed first. To do so, follow their instructions or these instructions. Usually it is most convenient to create a new environment. The package was written and tested using python 3.7.
conda create --name qopt_env python=3.7
conda activate qopt_env
Start with all required dependencies including filter_functions package:
conda install numpy scipy matplotlib
pip install filter_functions
If you wish to use the plotting features of QuTiP, then install additionally:
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
Either install qopt via pip
pip install qopt
or alternatively download the source code and use
python setup.py develop to install using symlinks or
python setup.py install without.
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 in the package.
You can find the patch Notes in a markdown list in the package. Please be aware that the github repo is updated more frequently than the version on pypi.
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.
[1]: Khaneja, N., Reiss, T., Kehlet, C., Schulte-Herbrüggen, T., Glaser, S. (2004). Optimal control of coupled spin dynamics: design of NMR pulse sequences gy gradient ascent algorithms https://doi.org/10.1016/j.jmr.2004.11.004