Quantum Spectral Anomaly Detection
A core task in quantum anomaly detection is to compute an anomaly score that quantifies how strongly a test quantum state deviates from a given quantum dataset assumed to be normal. Classically, principal component analysis (PCA) for centered data computes the anomaly score by evaluating the test sample relative to the subspace spanned by the selected leading eigenvectors. However, for quantum data that lack a standard centering, explicitly recovering principal eigenvectors, constructing full Gram matrices, or loading quantum-random-access-memory-style data can be more costly than estimating the anomaly score itself. To avoid these costs, we propose Quantum Spectral Anomaly Detection (QSP...
| Authors | Yewei Yuan, Michele Minervini, Mark M. Wilde et al. |
|---|---|
| Primary category | quant-ph |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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