Presents a quantum soft PCA framework with Fermi-Dirac filter for principal subspace scoring without eigenvector recovery, claiming dimension-independent sample complexity O(η^{-2}).
Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptions.Physical Review Letters, 127(6):060503, 2021
2 Pith papers cite this work. Polarity classification is still indexing.
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DQMW-Sample realizes a classically hard online learning primitive via dissipative quantum dynamics with sublinear regret and proven hardness for classical simulation including PH collapse.
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Quantum principal component analysis without eigenvector recovery
Presents a quantum soft PCA framework with Fermi-Dirac filter for principal subspace scoring without eigenvector recovery, claiming dimension-independent sample complexity O(η^{-2}).
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Dissipative Quantum Multiplicative Weights with Sampling Feedback: A Classically Hard Primitive Realized via Engineered Open-System Dynamics
DQMW-Sample realizes a classically hard online learning primitive via dissipative quantum dynamics with sublinear regret and proven hardness for classical simulation including PH collapse.