https://github.com/cda-tum/mqt-planqk

Service provider for the MQT on the PlanQK platform

https://github.com/cda-tum/mqt-planqk

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (17.7%) to scientific vocabulary

Keywords from Contributors

archiving parallel projection distributed energy-system-model quantum-circuit generic transformers mesh interactive
Last synced: 11 months ago · JSON representation

Repository

Service provider for the MQT on the PlanQK platform

Basic Info
  • Host: GitHub
  • Owner: cda-tum
  • Language: Python
  • Default Branch: main
  • Size: 138 KB
Statistics
  • Stars: 17
  • Watchers: 2
  • Forks: 1
  • Open Issues: 6
  • Releases: 0
Created over 2 years ago · Last pushed 11 months ago
Metadata Files
Readme

README.md

MQT as a Service @ PlanQK

This repository contains services for the PlanQK Platform based on the Munich Quantum Toolkit (MQT) that is developed by the Chair for Design Automation at the Technical University of Munich. Currently, the following services are available:

  • MQT DDSIM: A Tool for Classical Quantum Circuit Simulation based on Decision Diagrams.
  • MQT QMAP: A Tool for Quantum Circuit Mapping.
  • MQT QCEC: A Tool for Quantum Circuit Equivalence Checking.
  • MQT Bench: A tool for Benchmarking Software and Design Automation Tools for Quantum Computing.

For a full list of tools and libraries available as part of the MQT, please visit the MQT website.

Documentation

[!NOTE]\ All services are still experimental and under development. Expect breaking changes.

Usage

The fist goal is to be able to run the src directory as a Python module with the code inside program.py.

We recommend building your service from within a dedicated and fresh Conda environment to install and track all required packages from the start. For this reason, the template already contains an environment.yml file from which a fresh environment can be created:

HINT: As an alternative to Conda, you may use the requirements.txt file to create a virtual environment with the tooling of your choice.

bash conda env create -f environment.yml conda activate mqt-<SERVICE_NAME> python3 -m src

Run the project using Docker

You may utilize Docker to run your code locally and test your current implementation. In general, by following the next steps you replicate the steps done by the PlanQK platform, which is a way to verify your service in an early stage.

Build the Docker image

```bash docker pull ghcr.io/planqk/job-template:latest-base-1.0.0 docker build -t mqt- .

or (for Apple M1 chips)

docker buildx build -o type=docker --platform "linux/amd64" --tag mqt- . ```

Start the Docker container

In case, you do not use any input data or parameters that need to be passed into the container, you may run the container with the following command:

bash docker run -it \ -e BASE64_ENCODED=false \ -e LOG_LEVEL=DEBUG \ mqt-<SERVICE_NAME>

However, to pass the "data" and "params" attributes as JSON-serialized files into the container, you either mount it in the form of separate files (recommended) or pass it as environment variables (base64 encoded).

To use the data.json and params.json from the input directory, you could execute the following command:

bash PROJECT_ROOT=(`pwd`) docker run -it \ -e BASE64_ENCODED=false \ -e LOG_LEVEL=DEBUG \ -v $PROJECT_ROOT/input:/var/input \ mqt-<SERVICE_NAME>

If the service executed successfully, you should see something like Job:ResulsResponse: followed by the output you defined for your service. Otherwise, if you see Job:ErrorResponse: Bad news, something went wrong. However, the details of the response hopefully give you a clue what the problem was.


Acknowledgements

The Munich Quantum Toolkit has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 101001318), the Bavarian State Ministry for Science and Arts through the Distinguished Professorship Program, as well as the Munich Quantum Valley, which is supported by the Bavarian state government with funds from the Hightech Agenda Bayern Plus.

Owner

  • Name: Chair for Design Automation, TU Munich
  • Login: cda-tum
  • Kind: organization
  • Location: Germany

The CDA provides expertise for all main steps in the design and realization of integrated circuits, embedded systems, as well as cyber-physical systems.

GitHub Events

Total
  • Watch event: 7
  • Delete event: 24
  • Issue comment event: 4
  • Push event: 35
  • Pull request review event: 12
  • Pull request event: 46
  • Fork event: 1
  • Create event: 29
Last Year
  • Watch event: 7
  • Delete event: 24
  • Issue comment event: 4
  • Push event: 35
  • Pull request review event: 12
  • Pull request event: 46
  • Fork event: 1
  • Create event: 29

Committers

Last synced: about 1 year ago

All Time
  • Total Commits: 109
  • Total Committers: 3
  • Avg Commits per committer: 36.333
  • Development Distribution Score (DDS): 0.486
Past Year
  • Commits: 48
  • Committers: 2
  • Avg Commits per committer: 24.0
  • Development Distribution Score (DDS): 0.333
Top Committers
Name Email Commits
pre-commit-ci[bot] 6****] 56
dependabot[bot] 4****] 29
burgholzer b****r@m****m 24
Committer Domains (Top 20 + Academic)
me.com: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 0
  • Total pull requests: 105
  • Average time to close issues: N/A
  • Average time to close pull requests: 1 day
  • Total issue authors: 0
  • Total pull request authors: 3
  • Average comments per issue: 0
  • Average comments per pull request: 0.05
  • Merged pull requests: 93
  • Bot issues: 0
  • Bot pull requests: 102
Past Year
  • Issues: 0
  • Pull requests: 53
  • Average time to close issues: N/A
  • Average time to close pull requests: 3 days
  • Issue authors: 0
  • Pull request authors: 2
  • Average comments per issue: 0
  • Average comments per pull request: 0.04
  • Merged pull requests: 41
  • Bot issues: 0
  • Bot pull requests: 53
Top Authors
Issue Authors
  • dependabot[bot] (1)
Pull Request Authors
  • pre-commit-ci[bot] (103)
  • dependabot[bot] (64)
  • burgholzer (2)
Top Labels
Issue Labels
dependencies (1) python (1)
Pull Request Labels
dependencies (64) python (62) major (9) continuous integration (3) github_actions (2) documentation (1)

Dependencies

.github/workflows/test_services.yml actions
  • actions/checkout v4 composite
  • actions/setup-python v4 composite
mqt-ddsim/Dockerfile docker
  • ghcr.io/planqk/job-template latest-base-1.0.0 build
mqt-qcec/Dockerfile docker
  • ghcr.io/planqk/job-template latest-base-1.0.0 build
mqt-qmap/Dockerfile docker
  • ghcr.io/planqk/job-template latest-base-1.0.0 build
mqt-ddsim/requirements.txt pypi
  • loguru *
  • mqt-ddsim ==1.20.2
mqt-qcec/requirements.txt pypi
  • loguru *
  • mqt-qcec ==2.4.3
mqt-qmap/requirements.txt pypi
  • loguru *
  • mqt-qmap ==2.4.2
mqt-bench/Dockerfile docker
  • ghcr.io/planqk/job-template latest-base-1.0.0 build
mqt-bench/requirements.txt pypi
  • loguru *
  • mqt-bench ==1.0.7
mqt-bench/environment.yml conda
  • pip >=21
  • python >=3.9,<3.13
mqt-ddsim/environment.yml pypi
mqt-qcec/environment.yml pypi
mqt-qmap/environment.yml pypi