Quantum Detection of Sequency-Band Structure

TL;DR

This paper introduces a quantum algorithm that efficiently estimates the energy of specific sequency bands in quantum-encoded signals, enabling advanced signal analysis with exponential speedup over classical methods.

Contribution

The paper presents a novel quantum algorithm utilizing sequency-ordered QWHT and QAE for efficient band-specific amplitude estimation in quantum signals, offering exponential advantage over classical approaches.

Findings

Circuit depth for sequency-ordered QWHT is O(log N)

Quantum advantage over classical Walsh-Hadamard transform

Applicable to quantum signal processing tasks

Abstract

We present a quantum algorithm for estimating the amplitude content of user-specified sequency bands in quantum-encoded signals. The method employs a sequency-ordered Quantum Walsh-Hadamard Transform (QWHT), a comparator-based oracle that coherently marks basis states within an arbitrary sequency range, and Quantum Amplitude Estimation (QAE) to estimate the total probability mass in the selected band. This enables the detection of structured signal components, including both high- and low-sequency features, as well as the identification of rapid sign-change behavior associated with noise or anomalies. The proposed method can be embedded as a module within a larger quantum algorithm; in this setting, both the input and output remain fully quantum, enabling seamless integration with upstream and downstream quantum operations. We show that the sequency-ordered QWHT can be implemented with circuit depth $O(\log_2 N)$ (equivalently $O(n)$ for $N=2^n$) when acting on an amplitude-encoded quantum state, whereas computing the full Walsh-Hadamard spectrum of an explicit length-$N$ classical signal requires $O(N\log_2 N)$ operations via the fast Walsh-Hadamard transform. This results in an exponential quantum advantage when the QWHT is used as a modular block within a larger quantum algorithm, relative to classical fast Walsh-Hadamard transform-based approaches operating on explicit data. From an application perspective, the proposed sequency band-energy estimation may be interpreted as a structure-based anomaly indicator, enabling the detection of unexpected high-sequency components relative to a nominal low-sequency signal class. The algorithm is applicable to quantum-enhanced signal processing tasks such as zero-crossing analysis, band-limited noise estimation, and feature extraction in the Walsh basis.