AI RESEARCH
Filtered Spectral Projection for Quantum Principal Component Analysis
arXiv CS.LG
•
ArXi:2603.13441v1 Announce Type: cross Quantum principal component analysis (qPCA) is commonly formulated as the extraction of eigenvalues and eigenvectors of a covariance-encoded density operator. Yet in many qPCA settings, the practical objective is simpler: projecting data onto the dominant spectral subspace. In this work, we