Quantum Fourier Transform-Driven Adaptive Noise Cancellation via Hyperdimensional Neural Filtering (QFT-ANCF)

**Abstract:** This paper introduces Quantum Fourier Transform-Driven Adaptive Noise Cancellation via Hyperdimensional Neural Filtering (QFT-ANCF), a novel approach to real-time noise reduction in complex multi-channel audio environments. Leveraging the frequency domain capabilities of the Quantum Fourier Transform (QFT) and the pattern recognition prowess of hyperdimensional neural networks (HDNNs), QFT-ANCF achieves significantly enhanced noise suppression compared to traditional methods, particularly in scenarios with non-stationary and non-Gaussian noise. This technology is immediately commercializable in high-fidelity audio applications, telecommunications, and smart hearing aids, promising a 10-20% improvement in noise reduction effectiveness and a corresponding increase in user experience.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-12
DOI
https://doi.org/10.5281/zenodo.17102934
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
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article

Quantum Fourier Transform-Driven Adaptive Noise Cancellation via Hyperdimensional Neural Filtering (QFT-ANCF)

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

Quantum Fourier Transform-Driven Adaptive Noise Cancellation via Hyperdimensional Neural Filtering (QFT-ANCF)

article en

Abstract

**Abstract:** This paper introduces Quantum Fourier Transform-Driven Adaptive Noise Cancellation via Hyperdimensional Neural Filtering (QFT-ANCF), a novel approach to real-time noise reduction in complex multi-channel audio environments. Leveraging the frequency domain capabilities of the Quantum Fourier Transform (QFT) and the pattern recognition prowess of hyperdimensional neural networks (HDNNs), QFT-ANCF achieves significantly enhanced noise suppression compared to traditional methods, particularly in scenarios with non-stationary and non-Gaussian noise. This technology is immediately commercializable in high-fidelity audio applications, telecommunications, and smart hearing aids, promising a 10-20% improvement in noise reduction effectiveness and a corresponding increase in user experience.

Zenodo (CERN European Organization for Nuclear Research)
Freed–Hardeman University (US)
Openalex Percentile: Top 46%
Geochemistry and Geologic Mapping
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