qbiocode.utils.data_encoding module

Quantum Data Encoding Utilities

This module provides utility functions for encoding classical data into quantum states, including normalization, label encoding, and training set preparation for quantum machine learning algorithms.

These functions are generic and can be used across different quantum algorithms, not just ensemble learning.

Summary

Functions:

label_to_array	Convert binary labels to one-hot encoded arrays.
normalize_data	Normalize data vector for quantum state encoding.
prepare_training_set	Select and prepare balanced training subset for quantum ensemble.

Reference

normalize_data(x, C=1.0)

Normalize data vector for quantum state encoding.

Normalizes a classical data vector to unit L2 norm and converts to complex amplitudes suitable for quantum state initialization.

Parameters:

• x (np.ndarray) – Classical data vector to normalize

• C (float, optional) – Scaling constant (default: 1.0)

Returns:

Normalized vector as list of complex numbers

Return type:

List[complex]

Examples

>>> x = np.array([3.0, 4.0])
>>> x_norm = normalize_data(x)
>>> print([abs(xi) for xi in x_norm])
[0.6, 0.8]
>>> print(sum([abs(xi)**2 for xi in x_norm]))
1.0

label_to_array(y)

Convert binary labels to one-hot encoded arrays.

Transforms binary classification labels (0 or 1) into one-hot encoded format required by quantum circuits. Label 0 becomes [1, 0] and label 1 becomes [0, 1].

Parameters:

y (np.ndarray) – Binary labels (0 or 1)

Returns:

One-hot encoded labels, shape (n_samples, 2)

Return type:

np.ndarray

Examples

>>> y = np.array([0, 1, 0])
>>> label_to_array(y)
array([[1, 0],
       [0, 1],
       [1, 0]])

prepare_training_set(X, y, n=4, seed=123)

Select and prepare balanced training subset for quantum ensemble.

Creates a balanced training set by selecting equal numbers of samples from each class and normalizing them for quantum encoding.

Parameters:

• X (np.ndarray, shape (n_samples, n_features)) – Training feature data

• y (np.ndarray, shape (n_samples,)) – Training labels (binary: 0 or 1)

• n (int, optional) – Total number of training samples to select (must be even, default: 4)

• seed (int, optional) – Random seed for reproducibility (default: 123)

Returns:

X_data : Normalized training samples, shape (n, n_features) Y_data : One-hot encoded labels, shape (n, 2)

Return type:

Tuple[np.ndarray, np.ndarray]

Examples

>>> X = np.random.rand(20, 4)
>>> y = np.array([0]*10 + [1]*10)
>>> X_data, Y_data = prepare_training_set(X, y, n=4, seed=42)
>>> print(X_data.shape, Y_data.shape)
(4, 4) (4, 2)