ml.classifiers.vc

Scikit-learn compliant Variational Quantum Classifier

https://github.com/tno-quantum/ml.classifiers.vc

Science Score: 44.0%

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CITATION.cff file
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codemeta.json file
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Scientific vocabulary similarity
Low similarity (9.2%) to scientific vocabulary

Keywords

classification-algorithm quantum-computing quantum-machine-learning

Last synced: 6 months ago · JSON representation ·

Repository

Scikit-learn compliant Variational Quantum Classifier

Basic Info

Host: GitHub
Owner: TNO-Quantum
License: apache-2.0
Language: Python
Default Branch: main
Homepage: https://tno-quantum.github.io/documentation/
Size: 3.02 MB

Statistics

Stars: 5
Watchers: 0
Forks: 1
Open Issues: 1
Releases: 3

Topics

classification-algorithm quantum-computing quantum-machine-learning

Created about 3 years ago · Last pushed 9 months ago

Metadata Files

Readme Changelog Contributing License Citation

TNO Quantum: Variational classifier

TNO Quantum provides generic software components aimed at facilitating the development of quantum applications.

The tno.quantum.ml.classifiers.vc package provides a VariationalClassifier class, which has been implemented in accordance with the scikit-learn estimator API. This means that the classifier can be used as any other (binary and multiclass) scikit-learn classifier and combined with transforms through Pipelines. In addition, the VariationalClassifier makes use of PyTorch tensors, optimizers, and loss functions.

Limitations in (end-)use: the content of this software package may solely be used for applications that comply with international export control laws.

Documentation

Documentation of the tno.quantum.ml.classifiers.vc package can be found here.

Install

Easily install the tno.quantum.ml.classifiers.vc package using pip:

console $ python -m pip install tno.quantum.ml.classifiers.vc

If you wish to run the tests you can use: console $ python -m pip install 'tno.quantum.ml.classifiers.vc[tests]'

Example

Here's an example of how the VariationalClassifier class can be used for classification based on the Iris dataset: Note that tno.quantum.ml.datasets is required for this example.

```python from tno.quantum.ml.classifiers.vc import VariationalClassifier from tno.quantum.ml.datasets import getirisdataset

Xtraining, ytraining, Xvalidation, yvalidation = getirisdataset() vc = VariationalClassifier().fit(Xtraining, ytraining, niter=5) predictions = vc.predict(Xvalidation) ```

Owner

Name: TNO - Quantum
Login: TNO-Quantum
Kind: organization
Email: tnoquantum@tno.nl
Location: Netherlands

Twitter: TNO_Research
Repositories: 2
Profile: https://github.com/TNO-Quantum

Citation (CITATION.cff)

cff-version: 1.2.0
license: Apache-2.0
message: If you use this software, please cite it using these metadata.
authors:
      - name: TNO Quantum
        city: The Hague
        country: NL
        email: tnoquantum@tno.nl
        website: https://tno.nl
type: software
url: https://tno.nl
contact:
      - name: TNO Quantum
        city: The Hague
        country: NL
        email: tnoquantum@tno.nl
        website: https://tno.nl
repository-code: https://github.com/TNO-Quantum/ml.classifiers.vc
repository-artifact: https://pypi.org/project/tno.quantum.ml.classifiers.vc
title: TNO Quantum - Machine Learning - Variational Classifier
version: v3.0.1
date-released: 2025-05-12

GitHub Events

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Issues event: 1
Release event: 1
Watch event: 2
Delete event: 1
Issue comment event: 1
Push event: 2

Last Year

Create event: 1
Issues event: 1
Release event: 1
Watch event: 2
Delete event: 1
Issue comment event: 1
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Issues and Pull Requests

Last synced: 10 months ago

All Time

Total issues: 2
Total pull requests: 1
Average time to close issues: 5 months
Average time to close pull requests: 4 months
Total issue authors: 2
Total pull request authors: 1
Average comments per issue: 0.5
Average comments per pull request: 1.0
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
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Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
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Bot issues: 0
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