This article investigates the transformative potential of quantum computing in high-speed signal processing for Electrical and Electronic Engineering (EEE) systems. By examining quantum algorithms such as Quantum Fourier Transform (QFT) and Quantum Phase Estimation (QPE), the study identifies significant advancements in speed and accuracy for applications like frequency analysis, noise reduction, and phase detection. These advancements could greatly benefit industries requiring rapid processing of large datasets, including telecommunications, radar systems, and real-time image processing. Despite the promising benefits, challenges posed by Noisy Intermediate-Scale Quantum (NISQ) devices such as qubit coherence, error rates, and scalability currently limit practical applications. A hybrid quantum-classical approach is proposed to address these limitations, integrating quantum algorithms into existing systems. Additionally, quantum machine learning (QML) algorithms show promise in enhancing tasks like anomaly detection and feature extraction. The findings emphasize the importance of continued progress in quantum hardware, error correction, and algorithm optimization to unlock the full potential of quantum computing in EEE systems. This study highlights the need for standardized frameworks and hybrid architectures to drive future advancements in quantum signal processing and its real-world adoption.
Rafid et al. (Mon,) studied this question.
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