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Quantum Decision Trees (qtree)

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This Python package implements quantum decision tree classifiers for binary data. The details of the method can be found in Representation of binary classification trees with binary features by quantum circuits <https://doi.org/10.22331/q-2022-03-30-676>_.

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Installation

Install via pip or clone this repository. In order to use pip, type:

.. code-block:: sh

$ pip install quantum-tree

The package is tested with Python 3.8 and Python 3.9.

🌳 Usage

Minimal working example:

.. code-block:: python

create quantum tree instance

from qtree.qtree import QTree qtree = QTree(max_depth=1)

create simple training data

import numpy as np X = np.array([[1,0,0], [0,1,0], [0,0,1]]) # features y = np.array([[0,0], [0,1], [1,1]]) # labels

fit quantum tree

qtree.fit(X, y)

make quantum tree prediction

qtree.predict([[0,0,1]])

Documentation

Documentation is available on <https://qtree.readthedocs.io/en/latest>_.

Demo notebooks can be found in the examples/ directory.

📖 Citation

If you find this code useful in your research, please consider citing Representation of binary classification trees with binary features by quantum circuits <https://doi.org/10.22331/q-2022-03-30-676>_:

.. code-block:: tex

@article{Heese2022representationof,
         doi = {10.22331/q-2022-03-30-676},
         url = {https://doi.org/10.22331/q-2022-03-30-676},
         title = {Representation of binary classification trees with binary features by quantum circuits},
         author = {Heese, Raoul and Bickert, Patricia and Niederle, Astrid Elisa},
         journal = {{Quantum}},
         issn = {2521-327X},
         publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
         volume = {6},
         pages = {676},
         month = {3},
         year = {2022}
        }

This project is currently not under development and is not actively maintained.