Develops a quantum algorithm for linear matrix differential equations with query complexity O~(ν L t / ε) that is nearly optimal and yields polynomial to exponential speedups for open quantum system simulation.
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10 Pith papers cite this work, alongside 771 external citations. Polarity classification is still indexing.
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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}).
Continuous TE-PAI provides an unbiased randomized protocol for Hamiltonian simulation free of Trotter error at finite circuit depth, combined with structure-aware variance reduction that achieves up to 96% sampling-cost savings in n=30 tensor-network simulations.
Develops a threshold-regularized Moore-Penrose pseudoinverse formulation of PGM with hybrid classical-quantum circuit implementation using block-encoding for stable discrimination in ill-conditioned and rank-deficient ensembles.
Quantum algorithm implements Kravchuk transform via su(2) fast-forwarding with logarithmic scaling in dimension and error.
P-GONE applies generative ML to optimize Trotter-Suzuki decompositions, reporting up to 19.4x circuit depth reduction at F >= 0.95 versus Qiskit baselines on structured Hamiltonians.
Quantum integer multiplier with O(log^2 n) circuit depth and T-depth via parallel partial products and binary adder tree in the Clifford+T model.
Quadratic bosonic Hamiltonian simulation is BQP-complete for a broad class that includes classical oscillator networks and continuous-time quantum walks, but becomes PostBQP-hard when extended to more general quadratic interactions.
Human-AI collaboration expanded a meta-idea on rational approximation into sign-embedding quantum algorithms for matrix problems, with humans retaining final judgment on routes and refinements.
Chemical properties and symmetries, not variational energy, should guide UHF trial selection for ph-AFQMC on iron-sulfur clusters, yielding accurate energies despite suboptimal sampling and bias compensation.
citing papers explorer
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Efficient quantum algorithm for linear matrix differential equations and applications to open quantum systems
Develops a quantum algorithm for linear matrix differential equations with query complexity O~(ν L t / ε) that is nearly optimal and yields polynomial to exponential speedups for open quantum system simulation.
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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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Structure-Aware Variance Reduction for Unbiased Randomized Hamiltonian Simulation
Continuous TE-PAI provides an unbiased randomized protocol for Hamiltonian simulation free of Trotter error at finite circuit depth, combined with structure-aware variance reduction that achieves up to 96% sampling-cost savings in n=30 tensor-network simulations.
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Robust Pretty Good Measurement via Hybrid Classical-Quantum Pseudoinverse Approximation and Circuit-Level Realization
Develops a threshold-regularized Moore-Penrose pseudoinverse formulation of PGM with hybrid classical-quantum circuit implementation using block-encoding for stable discrimination in ill-conditioned and rank-deficient ensembles.
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Quantum Kravchuk Transform using $\mathfrak{su}(2)$ fast-forwarding
Quantum algorithm implements Kravchuk transform via su(2) fast-forwarding with logarithmic scaling in dimension and error.
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Physics Guided Generative Optimization for Trotter Suzuki Decomposition
P-GONE applies generative ML to optimize Trotter-Suzuki decompositions, reporting up to 19.4x circuit depth reduction at F >= 0.95 versus Qiskit baselines on structured Hamiltonians.
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A Polylogarithmic-Depth Quantum Multiplier
Quantum integer multiplier with O(log^2 n) circuit depth and T-depth via parallel partial products and binary adder tree in the Clifford+T model.
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Complexity of Quadratic Bosonic Hamiltonian Simulation: $\mathsf{BQP}$-Completeness and $\mathsf{PostBQP}$-Hardness
Quadratic bosonic Hamiltonian simulation is BQP-complete for a broad class that includes classical oscillator networks and continuous-time quantum walks, but becomes PostBQP-hard when extended to more general quadratic interactions.
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From Meta Idea to Advanced Mathematical Discovery -- Human-AI Co-Discovery of Sign-Embedding Quantum Algorithms
Human-AI collaboration expanded a meta-idea on rational approximation into sign-embedding quantum algorithms for matrix problems, with humans retaining final judgment on routes and refinements.
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Selecting optimal unrestricted Hartree-Fock trial wavefunctions for phaseless auxiliary-field quantum Monte Carlo: Accuracy and limitations in modeling three iron-sulfur clusters
Chemical properties and symmetries, not variational energy, should guide UHF trial selection for ph-AFQMC on iron-sulfur clusters, yielding accurate energies despite suboptimal sampling and bias compensation.