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.
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.
Quantum Computing (QC) has arisen as a possible solution to the computational problems arising in the background of modern Electrical Engineering (EE) applications when processing high-dimensional data generated by smart grids, ultra-high-speed communications, and other dense sensor networks. This review assesses specifically the intersection of Quantum Signal Processing (QSP) and Quantum Machine Learning (QML) in the EE context. Here, 30 research articles published between 2017 and 2025 are analyzed. Research articles are selected via structured database searches based on relevance, depth of technical detail, and connection to EE domains. Via quantum models (e.g., gate-based, annealing), base types of signal processing tasks (e.g., filtering and Fourier transforms), and ML use cases (e.g., classification, regression, and clustering), the review classifies the methods. Moreover, the review highlights the use of quantum-enhanced algorithms, which exhibit theoretical benefits to reduce complexity for large-scale signal processing and learning problems, but have limited use cases in practice. Findings from Quantum-enhanced algorithms indicate theoretical potential for reducing complexity in large-scale signal processing and Machine Learning (ML), though real-world use is limited by hardware, noise, and scalability issues. Key gaps remain in benchmarking against classical methods and developing hybrid models. While still emerging, QSP and QML offer promising solutions for future EE challenges, highlighting the need for continued research and collaboration.
Quantum algorithms have emerged as a transformative approach to address the computational challenges of high-dimensional image processing tasks. This research investigates the integration of quantum computing principles, such as Quantum Fourier Transform (QFT) and Quantum Principal Component Analysis (QPCA), to optimize processes like edge detection, noise reduction, and image segmentation. These quantum-enhanced techniques promise significant improvements in computational efficiency and accuracy compared to classical methods, especially when handling complex visual data in real-time applications like medical imaging, satellite imagery, and autonomous systems. Additionally, the study explores hybrid quantum-classical architectures to bridge current limitations in noisy intermediate-scale quantum (NISQ) devices, ensuring resource optimization and scalability. The potential of quantum algorithms to provide faster, more precise solutions is evaluated alongside challenges like error mitigation and algorithmic stability. This research highlights the future role of quantum computing in revolutionizing image processing, paving the way for breakthroughs in computer vision and artificial intelligence.
The rapid emergence of quantum computing offers the potential to revolutionize numerous domains, promising computational advantages over classical counterparts. This study aimed to evaluate the performance, efficiency, and robustness of selected quantum algorithmsQuantum Variational Eigensolver (VQE), Quantum Fourier Transform (QFT), and Quantum Phase Estimation (QPE)on near-term quantum devices. Our benchmarking revealed that, despite promising theoretical benefits, the practical deployment of these algorithms remains challenged by noise, error rates, and hardware limitations. The VQE showed promise in molecular modeling, while the utility of QFT and QPE in cryptography and optimization became evident. Nevertheless, their practical efficiency is contingent upon specific quantum hardware and employed error mitigation techniques. The findings underscore the transformative potential of quantum computing, but also emphasize the ongoing challenges that need addressing to make quantum computing practically advantageous.
This paper explores the convergence of quantum computing and artificial intelligence (AI), examining how their integration may redefine computational paradigms. Quantum computing, with its unique properties of superposition and entanglement, has the potential to exponentially accelerate AI processes, particularly in optimization, machine learning, and data analysis. We investigate quantum algorithms, such as the quantum Fourier transform and Grover’s algorithm, highlighting their application to AI models and machine learning tasks that require vast computational resources. The paper further delves into hybrid quantum-classical approaches, which leverage the strengths of both domains to address real-world problems. Challenges, such as quantum error correction, scalability, and the need for specialized hardware, are also discussed. We provide an analysis of ongoing advancements in quantum AI, including quantum-enhanced neural networks and reinforcement learning, and their implications for fields like natural language processing and predictive analytics. This research emphasizes the transformative potential of quantum AI while acknowledging the significant technical hurdles that remain. The integration of quantum computing and AI promises to unlock unprecedented computational capabilities, paving the way for breakthroughs in scientific research, industry applications, and complex problem-solving.
Quantum computing represents a transformative shift in computational technology, leveraging the principles of quantum mechanics to process information in ways that surpass the limits of classical systems.By exploiting qubits, superposition, entanglement, and quantum parallelism, quantum computers offer the potential to solve certain classes of problems-such as optimization, cryptography, molecular simulation, and complex machine learning tasks-significantly faster than classical algorithms.With rapid advancements in quantum hardware and the emergence of Noisy Intermediate-Scale Quantum (NISQ) devices, quantum computing has evolved from a theoretical concept to a practical field with growing industrial and scientific relevance.Although current systems face challenges related to error rates, decoherence, and scalability, ongoing developments in quantum error correction, hybrid quantum-classical algorithms, and software frameworks like Qiskit, Cirq, and PennyLane continue to push the field forward.This paper provides an introductory overview of quantum computing, outlining its foundational principles, current technological landscape, key algorithms, and future research directions.The goal is to offer a clear understanding of how quantum computing is poised to reshape the future of computation.
Quantum computing represents a fundamental shift in computation, promising to solve problems currently intractable for classical computers. However, misconceptions persist; quantum computers are not universally faster, nor do they replace classical systems. Current Noisy Intermediate-Scale Quantum (NISQ) devices face significant challenges, including decoherence, high error rates, and limited scalability. Additionally, leveraging quantum systems requires rethinking traditional computing principles, adopting different programming models, and addressing the complexities of integrating quantum and classical workflows, making their practical use in computer science both demanding and non-trivial. This article provides a system-level perspective on quantum computing for computer scientists, software engineers, and IT professionals, examining its current capabilities, key architectural and software challenges, and potential paths toward quantum advantage in real-world applications within hybrid quantum-classical environments.