Quantum principal component analysis without eigenvector recovery
Principal component analysis (PCA) is traditionally implemented through a covariance or kernel matrix, leading-eigenvector extraction, and hard rank-$k$ projection. These steps can be computationally costly in high-dimensional and quantum-data settings, sensitive to small eigengaps, and unnecessary when downstream tasks only require principal-subspace scores. Such score-based objectives are important in applications such as anomaly detection, spectral-energy profiling, and other postselection tasks. To address these needs, we introduce a measurement-based soft PCA framework replacing the hard top-$k$ projector with an entropy-regularized Fermi--Dirac filter. This filter is the unique opti...
| Authors | Yewei Yuan, Michele Minervini, Mark M. Wilde et al. |
|---|---|
| Primary category | quant-ph |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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