标题： 基于量子傅里叶变换的去噪：用于增强语音清晰度的酉滤波 显示英文标题

标题： Quantum Fourier Transform Based Denoising: Unitary Filtering for Enhanced Speech Clarity

摘要： 本文介绍了一种量子启发的去噪框架，该框架将量子傅里叶变换（QFT）集成到经典的音频增强流水线中。 与传统基于快速傅里叶变换（FFT）的方法不同，QFT提供了一种具有全局相位相干性和能量保持的酉变换，从而提高了语音和噪声之间的区分能力。 所提出的方法用QFT算子替换了维纳和谱减法滤波器中的FFT，确保了超参数设置的一致性以进行公平比较。 在不同信噪比（SNR）条件下的干净语音、合成音调和噪声混合物的实验表明，SNR有统计学上的显著提升，最高可达15 dB，并减少了伪影的生成。 结果证实，基于QFT的去噪在低SNR和非平稳噪声场景下具有鲁棒性，且没有额外的计算开销，突显了其作为量子增强语音处理可扩展路径的潜力。 显示英文摘要

摘要： This paper introduces a quantum-inspired denoising framework that integrates the Quantum Fourier Transform (QFT) into classical audio enhancement pipelines. Unlike conventional Fast Fourier Transform (FFT) based methods, QFT provides a unitary transformation with global phase coherence and energy preservation, enabling improved discrimination between speech and noise. The proposed approach replaces FFT in Wiener and spectral subtraction filters with a QFT operator, ensuring consistent hyperparameter settings for fair comparison. Experiments on clean speech, synthetic tones, and noisy mixtures across diverse signal to noise ratio (SNR) conditions, demonstrate statistically significant gains in SNR, with up to 15 dB improvement and reduced artifact generation. Results confirm that QFT based denoising offers robustness under low SNR and nonstationary noise scenarios without additional computational overhead, highlighting its potential as a scalable pathway toward quantum-enhanced speech processing.