Filtered Spectral Projection for Quantum Principal Component Analysis
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 goal is simpler: projection onto the dominant spectral subspace. Here we introduce a projection-first framework, the Filtered Spectral Projection Algorithm (FSPA), which bypasses explicit eigenvalue estimation while preserving the relevant spectral structure. FSPA amplifies any nonzero warm-start overlap with the leading subspace and remains robust in small-gap and near-degenerate regimes, without artificial symmetry breaking in the absence of bias. We show that FSPA achieves an oracle complex...
| Authors | Sk Mujaffar Hossain, Satadeep Bhattacharjee |
|---|---|
| Primary category | stat.ML |
| Published | 13 March 2026 |
| Last updated at source | 7 May 2026 |
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