Semidefinite programs over the unbounded PSD cone are equivalent to bosonic free-energy minimization problems recovered in the zero-temperature limit, with a new Bose-Einstein quantum relative entropy and hybrid algorithms.
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2 Pith papers cite this work. Polarity classification is still indexing.
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quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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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Bose-Einstein thermal operators for semidefinite optimization
Semidefinite programs over the unbounded PSD cone are equivalent to bosonic free-energy minimization problems recovered in the zero-temperature limit, with a new Bose-Einstein quantum relative entropy and hybrid algorithms.
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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}).