Researchers have developed an adaptive technique designed to significantly improve the efficiency of Quantum Singular Value Transformation (QSVT), a critical primitive for estimating properties of unknown quantum states. Current QSVT implementations frequently necessitate the use of high-degree polynomials, which introduces considerable computational overhead. This inefficiency arises because existing methods determine polynomial degrees based on conservative worst-case bounds, often tied to the minimum non-zero eigenvalue or the rank of density matrices. The new methodology introduces an adaptive strategy for identifying and utilizing *low-degree* polynomials, directly mitigating the resource intensity associated with higher-order polynomial transformations. This innovation facilitates a more efficient and resource-economical estimation of complex nonlinear quantum properties, moving beyond the constraints imposed by previous conservative assumptions1. Such a development is pivotal for streamlining quantum algorithm design by reducing the polynomial degree required for intricate transformations. Ultimately, this research represents a crucial step toward more practical and scalable quantum computation by optimizing core quantum subroutines, which could accelerate progress in diverse applications, including quantum chemistry and cryptography.