qNetVO 0.1.0

The Quantum Network Variational Optimizer

qNetVO: The Quantum Network Variational Optimizer

Simulate and optimize quantum communication networks using quantum computers.

Features

QNetVO simulates quantum communication networks on differentiable quantum cicuits. The cicuit parameters are optimized with respect to a cost function using gradient descent. qNetVO is powered by PennyLane <https://pennylane.ai>_ an open-source framework for cross-platform quantum machine learning.

Simulating Quantum Communication Networks:

• Construct complex quantum network ansatzes from generic quantum circuit compenents.
• Simulate the quantum network on a quantum computer or classical simulator.

Optimizing Quantum Communication Networks:

• Use our library of network oriented cost functions or create your own.
• Gradient descent methods for tuning quantum network ansatz settings to minimize the cost.

Quick Start

Install qNetVO:

$ pip install qnetvo

Install PennyLane:

$ pip install pennylane==0.20

Import packages:

import pennylane as qml
import qnetvo as qnet

Development

Creating the qnetvo-dev Environment

The Anaconda distribution of Python is used to ensure a consistent development environment. Follow the Anaconda installation instructions to setup the conda command line tool for your operating system. The conda tool creates the dev environment from the environment.yml file. For more details on how to use conda see the managing environments page in the conda documentation.

To create the dev environment, navigate to the root directory of the qNetVO/ repository and follow these steps.

1. Create the qnetvo-dev conda environment:

(base) $ conda env create -f environment.yml

2. Activate the qnetvo-dev conda environment:

(base) $ conda activate qnetvo-dev

3. Install the local qnetvo package in editable mode:

(qnetvo-dev) $ pip install -e .

All packages for building docs, running tests, and running notebooks are installed. Changes to the local ./src/qnetvo codebase are reflected whenever import qnetvo is called.

Running Tests

Tests are found in the ./test directory and run using pytest. First, setup the dev environment. Then, from the root directory, run:

(qnetvo-dev) $ pytest

Running Demos

Demos are found in the ./demos directory and implemented in Jupyter notebooks. To run the demos locally, first setup the dev environment, then run:

(qnetvo-dev) $  jupyter-notebook

A Jupyter notebook server will launch in your browser allowing you to run the notebooks.

Building Documentation

It is important to view the documentation before committing changes. To locally build and view the documentation, first setup the dev environment. Then, follow these steps.

1. Build Documentation: From the root directory run:

(qnetvo-dev) $ sphinx-build -b html docs/source/ docs/build/html

2. Serve Documentation: Navigate to the ./docs/build/html directory and run:

(qnetvo-dev) $ python -m http.server --bind localhost

3. View Documentation: Copy and paste the returned IP address to your browser.

Formatting Code

All code in this project is autoformatted using black. First, setup the dev environment. Then, from the root directory, run:

(qnetvo-dev) $ black -l 100 src test docs