Spectrally Corrected Polynomial Approximation for Quantum Singular Value Transformation
This paper introduces a spectrally corrected polynomial approximation method for Quantum Singular Value Transformation that leverages prior knowledge of a subset of a matrix's eigenvalues to enforce exact interpolation at those points without increasing polynomial degree, thereby significantly reducing circuit depth while maintaining high fidelity and robustness.