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}).
Variational quantum state diagonalization
3 Pith papers cite this work, alongside 206 external citations. Polarity classification is still indexing.
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SQD needs an exponentially increasing number of computational-basis configurations to approximate ground-state energies of Heisenberg and Hubbard models within fixed accuracy, even when configurations are chosen optimally by probability.
QARA, a recursive algorithm combining classical pruning, one-layer QAOA guidance, and rollback verification, reports higher exact-solution success than QAOA and RQAOA on 140 small synthetic exact cover instances, without public code or data.
citing papers explorer
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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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A Critical Assessment of the Sample-Based Quantum Diagonalization for Heisenberg and Hubbard Models
SQD needs an exponentially increasing number of computational-basis configurations to approximate ground-state energies of Heisenberg and Hubbard models within fixed accuracy, even when configurations are chosen optimally by probability.
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Quantum-Assisted Recursive Algorithm for Solving the Exact Cover Problem
QARA, a recursive algorithm combining classical pruning, one-layer QAOA guidance, and rollback verification, reports higher exact-solution success than QAOA and RQAOA on 140 small synthetic exact cover instances, without public code or data.