Quantum Machine Learning and PCA

This chapter explores quantum machine learning through the lens of eigenvalues and eigenvectors, emphasizing their significance in data processing algorithms like Grover's. It also covers Principal Component Analysis (PCA) as a method for dimensionality reduction, illustrating its application in clustering data sets while preserving relationships among data points.