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Research Article | Open Access

A rigorous and self-contained proof of the Grover-Rudolph state preparation algorithm

Antonio Falcó1( )Daniela Falcó-Pomares2Hermann G. Matthies3
Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera-CEU, CEU Universities San Bartolomé 55, 46115 Alfara del Patriarca (Valencia), Spain
Grupo de Investigación Bisite Universidad de Salamanca Calle Espejo s/n, 37007 Salamanca, Spain
Institute of Scientific Computing, Technische Universität Braunschweig, Universitätsplatz 2, 38106 Braunschweig, Germany
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Abstract

We give a rigorous and self-contained analysis of the Grover-Rudolph quantum state-preparation algorithm, which encodes a probability distribution { p k } as an n-qubit amplitude state k p k | k via a hierarchy of controlled R y rotations determined by a dyadic refinement of the target. We formalize the dyadic probability tree, derive the trigonometric factorization of conditional masses, and prove by induction that the circuit prepares exactly the desired measurement law. We further prove that perturbing each rotation angle by at most η changes the output distribution by at most min ( 1 , n η ) in total variation, and combine this with a Hoeffding concentration bound to obtain an explicit design rule: b log 2 ( 2 n π / ε ) bits and S 2 n + 1 log ( 2 / δ ) / ε 2 shots suffice to achieve accuracy ε with confidence 1 δ. As a circuit-theoretic complement, we provide an ancilla-free transpilation of each stage into { R y ( ) , X , C N O T } via Gray-code ladders and a Walsh-Hadamard angle transform.

CLC number: 81P65, 81P68

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AIMS Mathematics
Pages 16366-16394

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Cite this article:
Falcó A, Falcó-Pomares D, Matthies HG. A rigorous and self-contained proof of the Grover-Rudolph state preparation algorithm. AIMS Mathematics, 2026, 11(6): 16366-16394. https://doi.org/10.3934/math.2026672

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Received: 30 December 2025
Revised: 10 May 2026
Accepted: 22 May 2026
Published: 15 June 2026
©2026 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)