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A quantum algorithm for counting zero-crossings

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arxiv 2212.11814 v2 pith:GBQOK5TV submitted 2022-12-21 quant-ph

classification quant-ph
keywords problemquantumalgorithmorderingsequencywalsh-hadamardzero-crossingscounting
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

We present a zero-crossings counting problem that is a generalization of the Bernstein-Vazirani problem. The goal of this problem is to count the number of zero-crossings (or sign changes) in a special type of sequence S, whose definition depends upon a secret string. A quantum algorithm is presented to solve this problem. The proposed quantum algorithm requires only one oracle query to solve the problem, whereas a classical algorithm would need at least n oracle queries, where $2^n$ is the size of the sequence S. In addition to solving the zero-crossings counting problem, we also give a quantum circuit for performing the Walsh-Hadamard transforms in sequency ordering. The Walsh-Hadamard transform in sequency ordering is used in a wide range of scientific and engineering applications, including in digital signal and image processing. Therefore, the proposed quantum circuit for computing the Walsh-Hadamard transforms in sequency ordering may be helpful in quantum computing algorithms for applications for which the computation of the Walsh-Hadamard transform in sequency ordering is required.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Quantum algorithm for edge detection in digital grayscale images

    quant-ph 2025-07 conditional novelty 5.0 of 10

    A quantum edge detection algorithm based on the sequency-ordered Walsh-Hadamard transform and a quantum high-pass filter achieves O(log N) circuit depth when state preparation is excluded.

  2. Generalized tensor transforms and their applications in classical and quantum computing

    quant-ph 2025-07 reject novelty 3.0 of 10

    The claim that tunable tensor-product transforms outperform fixed transforms for quantum compression and encoding rests on fits to the data, not on independent predictions.

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