pyqpanda_alg.QPCA

QPCA stands for Quantum Principal Component Analysis, which is a quantum algorithm that can be used to extract principal components from a set of quantum data. QPCA is a quantum version of the classical PCA algorithm, which is widely used in data analysis and machine learning. The main advantage of QPCA over classical PCA is that it can process quantum data without measuring it, which can help preserve the coherence of the data and speed up the analysis. QPCA has potential applications in quantum machine learning, quantum chemistry, and other fields where data analysis is important.

Submodules

Functions

qpca(sample_A, k)

QPCA is a quantum version of the classical PCA algorithm, which is widely used in data analysis and machine learning.

Package Contents

pyqpanda_alg.QPCA.qpca(sample_A, k)

QPCA is a quantum version of the classical PCA algorithm, which is widely used in data analysis and machine learning.

Parameters:

sample_A: ndarray

the input matrix for analysis

k: int

the dimension to reduce

Returns:

out: ndarray

the output matrix after reducing dimension

Examples:
import numpy as np
from pyqpanda_alg.QPCA import qpca

A = np.array([[-1, 2], [-2, -1], [-1, -2], [1, 3], [2, 1], [3, 2]])
data_q = qpca(A, 1)
print(data_q)