===================== Krotov Python Package

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Python implementation of Krotov's method for quantum optimal control.

This implementation follows the original implementation in the QDYN Fortran library_.

The krotov package is built on top of QuTiP_.

Development happens on Github. You can read the full documentation online__ or download a PDF version.

.. Documentation: https://qucontrol.github.io/krotov __ Documentation

If you use the krotov package in your research, please cite it <https://qucontrol.github.io/krotov/v1.2.1/01_overview.html#citing-the-krotov-package>_.

.. _QDYN Fortran library: https://www.qdyn-library.net .. _QuTiP: http://qutip.org .. _download a PDF version: https://github.com/qucontrol/krotov/tree/master/docs/pdf

Purpose

Optimal control is a cornerstone of quantum technology: relying not just on a passive understanding of quantum mechanics, but on the active utilization of the quantum properties of matter. Quantum optimal control asks how to manipulate the dynamics of a quantum system in some desired way. This is essential for the realization of quantum computers and related technologies such as quantum sensing.

Krotov's method and GRAPE are the two leading gradient-based optimization algorithms used in numerical quantum optimal control. Krotov's method distinguishes itself by guaranteeing monotonic convergence for near-continuous control fields. This makes is particularly useful for exploring the limits of controllability in a physical system. While GRAPE is found in various software packages, there has not been an open source implementation of Krotov's method to date. Our package provides that missing implementation.

The Krotov package targets both students wishing to enter the field of quantum control and researchers in the field. It was designed towards the following goals:

• Leverage the QuTiP_ library as a platform for numerically describing quantum systems.
• Provide a collection of examples inspired by recent publications in the Jupyter notebook_ format, allowing for interactive exploration of the method.
• Define a general interface for formulating any quantum control problem, which may extend to other optimization methods in the future.
• Serve as a reference implementation of Krotov's method, and as a foundation against which to test other implementations.
• Enable the more widespread use of Krotov's method, for example in the design of experiments.

Prerequisites

The Krotov package is available for Python versions >= 3.5. Its main dependency is QuTiP_ (apart from the core packages of the Python scientific ecosystem). Thus, you should consider QuTiP's installation instructions.

In any case, using some sort of virtual environment_ is strongly encouraged. Most packages in the Python scientific ecosystem are now available as wheels, making installation via pip easy. However, QuTiP currently does not provide wheels. Thus, on systems that do not have the necessary compilers installed (Windows, macOS), the conda package manager provides a good solution.

Assuming conda is installed (e.g. through Miniconda_), the following commands set up a virtual (conda) environment into which the Krotov package can then be installed:

.. code-block:: shell

conda create -n qucontrolenv "python=3.7"
conda activate qucontrolenv
conda config --append channels conda-forge
conda install qutip

.. _core packages of the Python scientific ecosystem: https://www.scipy.org .. _QuTiP's installation instructions: http://qutip.org/docs/latest/installation.html .. _virtual environment: https://docs.python.org/3/glossary.html#term-virtual-environment .. _wheels: https://packaging.python.org/tutorials/installing-packages/#source-distributions-vs-wheels .. _QuTiP currently does not provide wheels: https://github.com/qutip/qutip/issues/933 .. _conda: https://conda.io/docs/index.html .. _Miniconda: https://conda.io/miniconda.html

Installation

To install the latest released version of krotov into your current (conda) environment, run this command in your terminal:

.. code-block:: shell

python -m pip install krotov

This is the preferred method to install the krotov package, as it will always install the most recent stable release.

You may also do

.. code-block:: shell

python -m pip install krotov[dev,extras]

to install additional development dependencies, including packages required to run the example notebooks.

If you don't have pip_ installed, the Python installation guide, respectively the Python Packaging User Guide can guide you through the process.

.. _pip: https://pip.pypa.io .. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/ .. _Python Packaging User Guide: https://packaging.python.org/tutorials/installing-packages/

To install the latest development version of krotov from Github_:

.. code-block:: shell

python -m pip install git+https://github.com/qucontrol/krotov.git@master#egg=krotov

.. _Github: https://github.com/qucontrol/krotov

Usage

To use Krotov's method for quantum optimal control in a Python script or Jupyter notebook_, start with::

import krotov
import qutip

Then,

1. define the necessary quantum operators and states using QuTiP_.
2. create a list of objectives, as instances of |krotov.Objective|_.
3. call |krotov.optimize_pulses|_ to perform an optimization of an arbitrary number of control fields over all the objectives.

.. |krotov.Objective| replace:: krotov.Objective .. _krotov.Objective: https://qucontrol.github.io/krotov/v1.2.1/API/krotov.objectives.html#krotov.objectives.Objective

.. |krotov.optimize_pulses| replace:: krotov.optimize_pulses .. _krotov.optimize_pulses: https://qucontrol.github.io/krotov/v1.2.1/API/krotov.optimize.html#krotov.optimize.optimize_pulses

See Using Krotov with QuTiP <https://qucontrol.github.io/krotov/v1.2.1/08_qutip_usage.html#using-krotov-with-qutip>_ and Examples <https://qucontrol.github.io/krotov/v1.2.1/09_examples.html>_ for details.

.. _Jupyter notebook: https://jupyter.org