tno.quantum.ml.components 2.0.3

Components for machine learning

TNO Quantum: Machine Learning - Components

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

This package contains generic components for our machine learning packages.

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.components package can be found here.

Install

Easily install the tno.quantum.ml.components package using pip:

$ python -m pip install tno.quantum.ml.components

Usage

The Quantum Machine Learning Components package allows you to define custom scikit-learn-compatible estimators that depend on solving QUBO problems.

from __future__ import annotations

from typing import TYPE_CHECKING, Self

import numpy as np
from numpy.typing import NDArray
from sklearn.base import ClusterMixin

from tno.quantum.ml.components import QUBOEstimator

if TYPE_CHECKING:
    from tno.quantum.optimization.qubo.components import QUBO
    from tno.quantum.utils import BitVectorLike


class CustomQUBOClustering(ClusterMixin, QUBOEstimator):
    """Custom QUBO clustering for demonstration purposes.

    This is **NOT** a functional clustering class but serves as an example of how to 
      implement a QUBO estimator in the TNO Quantum framework.
    """

    def _check_X_and_y(
        self, X: NDArray[np.float64], y: NDArray[np.float64] | None = None
    ) -> None:
        if y is None:
            error_msg = (
                "This estimator requires target values (y) to be provided for training."
            )
            raise ValueError(error_msg)

    def _make_qubo(
        self, X: NDArray[np.float64], y: NDArray[np.float64] | None = None
    ) -> QUBO:
        # Create here the QUBO matrix based on the input data.
        pass

    def _check_constraints(self, bit_vector: BitVectorLike) -> bool:
        # No specific constraints for this classifier.
        return True

    def _decode_bit_vector(self, bit_vector: BitVectorLike) -> Self:
        # Decode the bit vector to return the classifier instance.
        return self

For a more complete and functional example, see the QKMedoids implementation.