thangleiter / qopt

qopt: A Simulation and Quantum Optimal Control Package

Documentation

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.

Version

This is the open alpha version. The name might be changed in the future. The contend will be extended based on feedback concerning the needs and demands of (possible) users. If you miss an important feature, you can request it by opening a issue in the git repository or write me directly at [email protected].

Introduction

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 implementation was inspired by the optimal control package of QuTiP.

Installation

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 recommended dependencies of QuTiP:

conda install numpy scipy cython matplotlib pytest pytest-cov jupyter

Then open a conda forge channel:

conda config --append channels conda-forge

and install QuTiP:

conda install qutip

Then install two remaining dependencies and the filter_functions package via pip:

conda install simanneal
pip install filter_functions

And 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.

References

[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