netqasm 2.0

Tool for parsing, encoding, decoding and executing NetQASM applications

NetQASM

Utilities for writing, compiling, and running quantum network applications.

Intro

NetQASM is an instruction set architecture that allows one to interface with quantum network controllers and run applications on a quantum network. Applications may be written directly in the NetQASM language, which resembles assembly code. However, this package also provides an SDK which allows writing application code in Python. For the paper introducing NetQASM, see here.

Applications written with this SDK may be run on a simulator backend, like SquidASM or SimulaQron. In the future, these same applications may be run on a hardware backend consisting of real implementations of quantum network controllers.

This NetQASM Python library is used by the QNE ADK, which allows interaction with the Quantum Network Explorer. When developing applications specifically for the QNE platform, it is recommended to use the QNE ADK. For more generic application development, this NetQASM package can be used directly.

Prerequisites

This package has only been tested on Linux, specifically Ubuntu. On Windows, WSL may be used.

Installation

From PyPI

NetQASM is available as a package on PyPI and can be installed with

pip install netqasm

If you also want to run NetQASM applications on an actual simulator, you may install squidasm, with:

pip install squidasm --extra-index-url=https://pypi.netsquid.org

which whill prompt for your NetSquid username and password.

From source

Clone this repository and create an editable install with:

pip install -e .

Additionally you may want to install the dev dependencies in order to run the tests and linter:

pip install -e .[dev]

The squidasm simulator can also be installed, with:

pip install -e .[squidasm] --extra-index-url=https://pypi.netsquid.org

Alternatively, you can use the make install and make install-dev Makefile commands. For also installing squidasm, use make install-squidasm. This requires you to have the NETSQUIDPYPI_USER and NETSQUIDPYPI_PWD environment variables set to your NetSquid username and password respectively.

To verify the installation and run all tests and examples:

make verify

Documentation

The documentation is hosted on Read the Docs.

The documentation source lives in the docs directory. See the docs README for information about building and rendering docs.

Examples

Example applications can be found in netqasm/examples.

Applications can be run in two ways:

• From the command line, using netqasm simulate. This requires the application code to be organized in a directory with a specific format (see the Application file structure page in the docs).
• By running a Python script that contains code to start the application.

Examples of applications organized in a directory can be found in netqasm/examples/apps and netqasm/examples/qne_apps. They can be run on a simulator using

netqasm simulate --app-dir netqasm/examples/<app>

Examples of Python scripts that can run applications can be found in netqasm/examples/sdk_scripts. These files can be run directly using python <filename>.

netqasm/examples/sdk_compilation contains SDK scripts that use a debug backend. Running these files does not involve an actual simulation of the application code but can be used to see the NetQASM subroutines that are compiled from the Python application code.

For more information, check the documentation.

CLI

Once installed, netqasm can be used as a command-line interface (CLI) to perform various operations.

See netqasm --help and netqasm <command> --help for the options.

For example, you can use the --simulator=<simulator> to specify which simulator to use. Currently there is support for:

• SquidASM, which internally uses NetSquid
• SimulaQron

We note that SquidASM is the recommended (and also default) simulator since it is generally faster than SimulaQron and can also simulate noise much more accurately.

License and patent

A patent application (NL 2029673) has been filed which covers parts of the software in this repository. We allow for non-commercial and academic use but if you want to explore a commercial market, please contact us for a license agreement.

Development

For code formatting, black and isort are used. Type hints should be added as much as possible. Types are checked using mypy.

Before code is pushed, make sure that the make lint command succeeds, which runs black, isort, flake8 and mypy.

Branches

A form of "git flow" is used for branch and release management. The main active branch is develop. New features are developed in new separate branches, preferrably with a name representing the new feature. To get the new features in the main branch, open a pull request for merging the feature branch into the develop branch. These pull requests are then reviewed by maintainers of the repository. A master or main branch is not used.

Releases (for maintainers only)

When a release is made, a new branch release-X.Y (e.g. release-0.12) is created from the develop branch. Only small fixes (patches) may be pushed onto this release branch. Bigger new features need to go into separate branches, merged into develop, and will hence end up in a later release. Tags are only applied on commits in the release branch. The first tag on a new release branch needs to be vX.Y.0, e.g. v0.12.0. Patches (i.e. commits on the release branch) may then be tagged with vX.Y.1, vX.Y.2 etc. Pushing a 'tag' automatically triggers the Github Action for publishing the corresponding version to PyPI. Whenever a new tag is pushed for a patch, the corresponding commit (on the release branch) should be merged into develop.

Example list of steps for releasing a new version:

• (0) develop contains all the features that are needed for the release
• (1) update CHANGELOG.md with the new version number and the changes (this will require a final PR to develop)
• (2) create a release-X.Y branch from develop and switch to this branch
• (3) create a tag vX.Y.0 on this branch
• (4) push the release branch as well as the tag (pushing the tag will automatically publish to PyPI)
• (5) develop a small fix (patch) as a commit on the release-X.Y branch
• (6) update CHANGELOG.md with the new (minor) version number and the changes
• (7) push the new commit and see if all the workflows (in Github Actions) succeed for release-X.Y
• (8) add a tag vX.Y.1 to this latest commit
• (9) push the new tag; the patch will automatically be published on PyPI
• (10) create a pull request for merging release-X.Y into develop

Contributors

In alphabetical order:

• Axel Dahlberg
• Carlo Delle Donne
• Wojciech Kozlowski
• Martijn Papendrecht
• Ingmar te Raa
• Bart van der Vecht (b.vandervecht[at]tudelft.nl)