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Quantum Networks

Project description



Please checkout this presentation for a quick overview of quantumnetworks!


Conda users, please make sure to conda install pip before running any pip installation if you want to install quantumnetworks into your conda environment.

quantumnetworks will soon be published on PyPI. So, to install, simply run:

pip install quantumnetworks

If you also want to download the dependencies needed to run optional tutorials, please use pip install quantumnetworks[dev] or pip install 'quantumnetworks[dev]' (for zsh users).

To check if the installation was successful, run:

>>> import quantumnetworks as qn

Building from source

To build quantumnetworks from source, pip install using:

git clone
cd quantumnetworks
pip install --upgrade .

If you also want to download the dependencies needed to run optional tutorials, please use pip install --upgrade .[dev] or pip install --upgrade '.[dev]' (for zsh users).

Installation for Devs

If you intend to contribute to this project, please install quantumnetworks in develop mode as follows:

git clone
cd quantumnetworks
pip install -e .[dev]

Please use pip install -e '.[dev]' if you are a zsh user.


We present quantumnetworks as a numerical simulation tool with which to explore the time-dynamics of a driven, lossness, and nonlinear multi-mode quantum network using the Heisenberg-Langevin Equations. The applications of this tooling span quantum transduction, bosonic quantum error correction systems, quantum communication, and more.


The codebase is split across quantumnetworks/systems and quantumnetworks/analysis, which respectively provide solvers and analysis tools for several quantum network systems of interest.

Future Directions

Checkout issues to see what we are working on these days!


Core Devs: Shantanu Jha, Shoumik Chowdhury, Lamia Ateshian

Thanks to Professor Luca Daniel and our TA, Taqiyyah Safi, for invaluable feedback during the development of this package in the Fall 2021 iteration of Introduction to Numerical Simulation (6.336) at MIT.

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