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 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.
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.
Abstract Quantum Machine Learning has emerged as a promising field at the intersection of Quantum Computing and Artificial Intelligence, offering the potential to achieve computational advantages over classical machine learning approaches. While classical ML has matured into a robust ecosystem with broad applications, QML remains nascent, constrained by hardware limitations, yet fueled by rapid theoretical and algorithmic developments. This paper presents a comprehensive comparative review of QML and classical ML, focusing on their foundations, complexity, and applications across domains. We analyze where QML models have shown advantages, where they lag behind, and try to provide possible explanations for these results. By synthesizing evidence across neural networks, kernel methods, generative models, representation learning, and clustering, we highlight patterns that indicate when quantum models tend to outperform or underperform classical baselines. We also review real-world applications of QML across diverse domains such as materials science, energy forecasting, healthcare and bioinformatics, transportation, and finance. Finally, we identify open challenges including state preparation, scalability, and benchmarking standards, and propose a roadmap for the development of QML into a field with demonstrable advantages.
Recent advances in Quantum Machine Learning (QML) bring together Quantum Computing and Machine Learning with the aim of enhancing the advantages of Machine Learning to solve problems efficiently. The properties of quantum systems (superposition, entanglement, etc.) to represent multiple states of information at once, QML can potentially offer significant advantages when solving complex high-dimensional (multidimensional) computational problems. This paper reviews the current state-of-art regarding QML in general through an organized compilation and assessment of selected studies recently published in scientific literature that were found via large electronic library catalogs based on peer-reviewed sources according to algorithms used, optimization approaches employed, and areas of applied study. This review provides comparative analyses of each selected studies for purposes of identifying major trends in QML research/practice as well as opportunities for methodological improvements and challenges still existing within QML. Finally, this review presents an overview of Quantum Computing and Quantum Data Encoding Principles utilized to translate Classical Data into Quantum States as a high-level evaluation of several popular Quantum Learning Algorithms (e.g., Quantum Neural Networks, Quantum Support Machines, and Variational Quantum Circuit). The survey describes the optimization strategies such as gradient based techniques, Bayesian Optimization and evolutionary techniques being used to improve the stability of the quantum machine learning (QML) training process. Then, it provides examples of application areas for such QML, where healthcare analytics, environmental modelling, manufacturing optimization and analysis of biologic data are included. The comparative review of the recent academic research has identified several limitations and challenges regarding the use of QML that researchers. Although QML is believed to be capable of solving complex computational problems, issues such as noise in hardware, limited numbers of qubits and scalability present significant challenges for researchers working with this type of machine learning application. Overall, it appears that this survey provides a broad overview of contemporary developments and future trends for developing high-performance quantum learning systems.
Quantum computing has emerged as one of the most promising technologies capable of solving computational problems beyond the capabilities of classical computers. When integrated with Artificial Intelligence (AI), Quantum Machine Learning (QML) offers new possibilities for accelerating data processing, optimizing complex models, and improving predictive performance. Unlike conventional machine learning algorithms that rely solely on classical computation, QML leverages quantum phenomena such as superposition, entanglement, and quantum interference to perform computations more efficiently. This paper presents a comprehensive review of Quantum Machine Learning, discussing its architecture, core algorithms, applications, benefits, limitations, and future research directions. The study highlights the potential of QML in healthcare, cybersecurity, financial analytics, scientific research, and optimization problems. Additionally, the paper examines the challenges associated with quantum hardware, scalability, error correction, and algorithm development. The findings suggest that although Quantum Machine Learning remains in its early stages, it represents a transformative technology that may redefine intelligent computing over the coming decades.
Quantum computing is a revolutionary field that promises to provide computational advantages that classical computers cannot achieve. This article delves into the intersection of quantum computing and its potential applications in machine learning (ML) and data science. By leveraging quantum mechanics, quantum computers can solve complex problems faster and more efficiently than traditional machines. The article explores key quantum algorithms, including Grover's algorithm and Shor's algorithm, and their potential to enhance the performance of ML models and data processing tasks. Furthermore, it provides a critical review of the ongoing research in quantum-enhanced machine learning (QML) and quantum data science (QDS), highlighting the challenges and future opportunities for integration into practical applications.
As the limits of classical machine learning (ML) become increasingly evident—especially in terms of computational scalability and data processing—quantum computing emerges as a promising frontier capable of addressing these challenges. Quantum Machine Learning (QML) represents the intersection of quantum computing and traditional ML, aiming to leverage quantum phenomena such as superposition, entanglement, and quantum parallelism to enhance learning capabilities, accelerate training, and solve complex problems that are intractable for classical algorithms. This paper provides a comprehensive overview of the evolving field of QML, beginning with the theoretical foundations of quantum computing and its synergy with classical machine learning principles. We explore key QML algorithms, including quantum support vector machines (QSVM), variational quantum classifiers (VQC), quantum neural networks (QNN), and hybrid quantum-classical approaches that exemplify the practical integration of both paradigms. We further examine real-world applications of QML across domains such as drug discovery, finance, climate modeling, and optimization, where quantum enhancements have begun to show early but promising results. Despite its theoretical potential, QML faces substantial limitations, including quantum hardware instability, limited qubit scalability, noise sensitivity, and unresolved challenges in quantum data encoding and interpretability. In synthesizing recent advancements and persistent obstacles, this paper highlights the importance of continued interdisciplinary research, algorithmic refinement, and hardware evolution. We also outline future directions that could lead QML from experimental promise to practical utility, including developments in quantum error correction, benchmarking standards, and application-specific quantum architectures