Qadence-Model
Qadence-Model is a Python package that provides quantum models for quantum machine learning features in Qadence. Model documentation about QNN and optimizer is here
Installation guide
qadence-model and can be installed using pip as follows:
pip install qadence-model
Contributing
Before making a contribution, please review our code of conduct.
- Submitting Issues: To submit bug reports or feature requests, please use our issue tracker.
- Developing in qadence: To learn more about how to develop within
qadence-model, please refer to contributing guidelines.
Setting up qadence in development mode
We recommend to use the hatch environment manager to install qadence_model from source:
python -m pip install hatch
# get into a shell with all the dependencies
python -m hatch shell
# run a command within the virtual environment with all the dependencies
python -m hatch run python my_script.py
WARNING
hatch will not combine nicely with other environment managers such as Conda. If you still want to use Conda,
install it from source using pip:
# within the Conda environment
python -m pip install -e .
License
Qadence-Model is a free and open source software package, released under the Apache License, Version 2.0.
Metadata
Release files for qadence-model 1.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| qadence_model-1.0.1.tar.gz | 30.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| qadence_model-1.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 63.8 kB
Release files / qadence_model-1.0.1.tar.gz
| Download URL | qadence_model-1.0.1.tar.gz |
|---|---|
| Size | 30.3 kB |
| Tags | Source |
|
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Transparency logRelease files / qadence_model-1.0.1-py3-none-any.whl
| Download URL | qadence_model-1.0.1-py3-none-any.whl |
|---|---|
| Size | 33.5 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
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