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}).
Physics Letters A , volume =
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Single-ancilla approximate block encoding of A = sum alpha_j H_j is achieved via generalized quantum signal processing applied to Hamiltonian simulation, yielding near-optimal depth with one or O(log log(1/epsilon)) ancilla.
Quantum algorithm for photodissociation wavefunction propagation on quantum computers via split-operator, QFT, dilated non-unitary absorber, and Hadamard-test autocorrelation, matching benchmarks on NOCl under ideal conditions with noise robustness.
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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Low-ancilla block encodings via Hamiltonian simulation
Single-ancilla approximate block encoding of A = sum alpha_j H_j is achieved via generalized quantum signal processing applied to Hamiltonian simulation, yielding near-optimal depth with one or O(log log(1/epsilon)) ancilla.
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Quantum Mechanical Studies of Photodissociation Dynamics on Quantum Computers
Quantum algorithm for photodissociation wavefunction propagation on quantum computers via split-operator, QFT, dilated non-unitary absorber, and Hadamard-test autocorrelation, matching benchmarks on NOCl under ideal conditions with noise robustness.