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‎.devcontainer/devcontainer.json‎

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{
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"name": "Zoose Quantum",
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"image": "databaseline/zoose-quantum:3.1.1",
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"features": {
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"ghcr.io/devcontainers/features/node:1": {
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"version": "none"
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}
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},
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"extensions": ["ms-python.python",
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"ms-python.black-formatter",
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"ms-toolsai.jupyter",
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"ms-toolsai.jupyter-keymap",
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"ms-toolsai.jupyter-renderers",
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"ms-toolsai.vscode-jupyter-powertoys",
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"ms-pyright.pyright",
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"ms-vscode.references-view"],
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"forwardPorts": [8888]
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}

‎.gitignore‎

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/

‎LICENSE‎

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MIT License
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Copyright (c) 2022 Ian Hellström
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md‎

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# GitHub Codespace for Zoose
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Run Zoose Quantum with many useful Python and quantum computing libraries pre-installed in VSCode directly from your browser with only 2 clicks!
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You can either try [Zoose Quantum](https://github.com/ianhellstrom/zoose#zoose-quantum) in VSCode in your browser with only two clicks _or_ run it or any Zoose [flavour](https://github.com/ianhellstrom/zoose#flavours) (e.g. PyTorch) from your own GitHub account and repository (recommended).
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## Try Zoose Quantum
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Click on _Use this template_ and choose _Open in codespace_
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You will be running [Zoose Quantum](https://databaseline.tech/zoose-2.0/#zoose-quantum) in a GitHub Codespace, straight from this repository.
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This is best if you want to give Zoose Quantum a whirl but do not intend to save your work in your own account.
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Many popular quantum packages (e.g. Braket, Cirq, Pennylane, pytket, Qiskit) are already installed and ready to be used from VSCode, no installation required.
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## Run Zoose Base, Neo4j, PyTorch, or Quantum
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1. Click on _Use this template_ (green button) and choose _Create a new repository_ (in your own account).
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1. Give the repository a name and click _Create repository from template_.
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1. Click on _Code_ (green button), choose _Codespaces_, and click _Create codespace on master_ (green button) or hit the "+" sign (near the top).
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If you prefer to run a different Zoose flavour, just change the `image` in `.devcontainer/devcontainer.json` to the desired flavour, e.g. `databaseline/zoose-pytorch:3.1.1` and commit it to your own repository.
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Enjoy your GitHub Codespace.

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