From Block-encoding to Generalized Quantum Signal Processing: Principles, Algorithms and Applications

• URL: http://arxiv.org/abs/2609.09977v1
• Date: Wed, 09 Sep 2026 10:06:42 GMT
• Title: From Block-encoding to Generalized Quantum Signal Processing: Principles, Algorithms and Applications
• Abstract summary: Five tools are used to transform matrix functions into implementable quantum circuits: block-encoding, qubitization, QSP, QSVT and GQSP.<n>This paper develops these techniques from first principles as a unified framework for constructing quantum algorithms.
• Score: 4.014371002279176
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern quantum algorithms are increasingly formulated as coherent procedures for implementing polynomial transformations of operators and singular values. This perspective provides a powerful and unifying language for quantum algorithm design, connecting a wide range of distinct problems through five closely related key tools: block-encoding, qubitization, QSP, QSVT and GQSP. Block-encoding embeds non-unitary matrices into larger unitaries; qubitization converts block-encodings into structured operators; QSP, QSVT and GQSP enable polynomial transformations with near-optimal query complexity. Together, these techniques form a general toolkit for transforming matrix functions into implementable quantum circuits. This paper develops these techniques from first principles as a unified framework for constructing quantum algorithms. We apply this framework to representative applications to highlight design principles and demonstrate how distinct algorithms can be constructed from a unified sequence of operator transformations. A central contribution is a systematic decision workflow for selecting the appropriate approach according to the operator structure and the desired transformation polynomial. This perspective clarifies when direct GQSP or through qubitization, or Laurent expansion, or QSVT is most appropriate. We organize algorithmic design into an end-to-end pipeline: identifying the target matrix function, constructing an appropriate block-encoding, determining the relevant spectral domain, designing a polynomial or Laurent-polynomial approximation, synthesizing the phase factors, and translating the transformation into an executable quantum circuit. By applying this unified framework to example applications, we showcase a practical methodology for reasoning, designing, and implementing quantum algorithms based on polynomial transformations.

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