Constrained Uniform Polynomial (CUP) and Constrained Adaptive Polynomial (CAP) solvers achieve lower error than standard QSVT and Chebyshev methods in noise-limited regimes by optimizing accuracy versus block-encoding normalization under uniform or moment-based spectral models.
A Theory of Quantum Subspace Diagonalization
3 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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A reorganized Hartree-Fock framework imposes tunable orbital locality by pairing local degrees of freedom with local solution conditions, maintaining efficient SCF optimization and competitive reaction-energy accuracy.
Constrained optimal polynomials (CUP and CAP) reduce quantum linear system solver errors under noise by jointly optimizing approximation accuracy and block-encoding normalization, outperforming standard QSVT and Chebyshev-iteration solvers in numerical benchmarks.
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Constrained Optimal Polynomials for Quantum Linear System Solvers
Constrained Uniform Polynomial (CUP) and Constrained Adaptive Polynomial (CAP) solvers achieve lower error than standard QSVT and Chebyshev methods in noise-limited regimes by optimizing accuracy versus block-encoding normalization under uniform or moment-based spectral models.
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Approximating Hartree-Fock theory via an efficiently local reformulation
A reorganized Hartree-Fock framework imposes tunable orbital locality by pairing local degrees of freedom with local solution conditions, maintaining efficient SCF optimization and competitive reaction-energy accuracy.
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Nonisothermal global-pressure exactness in fractured multiphase flow with aperture feedback
Constrained optimal polynomials (CUP and CAP) reduce quantum linear system solver errors under noise by jointly optimizing approximation accuracy and block-encoding normalization, outperforming standard QSVT and Chebyshev-iteration solvers in numerical benchmarks.