In the machine learning domain of image classification, computational capability stands as a significant constraining factor. However, harnessing the robust computational power of quantum computers presents a viable solution. This paper reviews the application of QML in image classification, with quantum convolutional neural network (QCNN) and quantum K-nearest neighbor algorithm (QKNN) as the core content. Primarily, a compendium of data embedding methodologies, commonly enlisted within the domain of quantum machine learning, is elucidated. Subsequently, an intricate exploration ensues into the present state of QKNN and QCNN deployment in image classification, thereby delineating the latent and meritorious attributes underpinning QML’s efficacy within the purview of image classification. Ultimately, a prospective trajectory for the future advancement of QML within the precincts of image classification is delineated.
In the machine learning domain of image classification, computational capability stands as a significant constraining factor. However, harnessing the robust computational power of quantum computers presents a viable solution. This paper reviews the application of QML in image classification, with quantum convolutional neural network (QCNN) and quantum K-nearest neighbor algorithm (QKNN) as the core content. Primarily, a compendium of data embedding methodologies, commonly enlisted within the domain of quantum machine learning, is elucidated. Subsequently, an intricate exploration ensues into the present state of QKNN and QCNN deployment in image classification, thereby delineating the latent and meritorious attributes underpinning QML’s efficacy within the purview of image classification. Ultimately, a prospective trajectory for the future advancement of QML within the precincts of image classification is delineated.
Two of the most disruptive technologies of the 21st century are quantum computing and artificial intelligence. Their intersection has led to the emergence of a new discipline referred to as Quantum Machine Learning (QML), which aims to enhance the capabilities of classical machine learning by leveraging the computational advantages of quantum devices. This paper provides a survey of the most advanced Quantum Machine Learning (QML) algorithms, including Quantum Support Vector Machines (QSVMs), Quantum k-nearest Neighbours (QkNN), Quantum Principal Component Analysis (QPCA), Quantum Neural Networks (QNNs), and Quantum Reinforcement Learning (QRL). The theoretical and practical status, as well as the empirical performance, of these algorithms, were summarised using a structured review method. The findings reveal a potential for speed-ups in classification, clustering, and optimisation among a range of applications, particularly for perfect quantum systems. However, hardware constraints, software irregularities, and training issues, such as barren plateaus, have limited the practical utility of this approach. Applications of QML in areas such as disaster preparedness and management, drug discovery, environmental sustainability, urban planning methodology, NLP, and finance demonstrate both the potential and current limitations of QML, with most applications still at the proof-of-concept level. In this review, we conclude that QML could be revolutionary, but its feasibility ultimately relies on improvements in physical hardware, the robustness of algorithms, and the standardisation of benchmarks.
Classification is a fundamental aspect of leveraging big data for decision-making across domains such as engineering, medicine, economics, and beyond. This systematic review explores the application of quantum machine learning (QML) to address the classification challenges. Despite the rising popularity of QML, challenges persist regarding its practical efficacy and applicability relative to classical methods. This study seeks to classify and analyse the application of QML across diverse domains. Employing a systematic methodology, a total of 23 research articles were chosen after applying rigorous selection criteria between the years 2013 to 2023. At first, 520 research articles were identified, and narrowed down to 296 papers, the abstracts of the papers have been read, 61 full-text papers have been studied, and ultimately 23 papers have been selected for review. A methodological approach was employed, involving the selection of relevant papers from reputable databases, evaluation of these papers, and the subsequent derivation of insights into the performance of QML in practical classification applications. Findings indicate that QML classifiers mostly achieved superior performance compared to conventional methods across accuracy, high-dimensional data management, and computational efficiency. However, challenges remain, including the limitations of noisy quantum hardware and the requirement for hybrid and innovative quantum algorithms. Analysis of current trends reveals significant progress in hybrid quantum-classical computational methods. To conclude, this analysis reveals QML’s revolutionary capabilities while noting its current shortcomings. This study provides a thorough overview for researchers investigating QML’s application to real-world classification problems, and it offers guidelines for future research endeavors.
Quantum computing (QC) has emerged as a revolutionary technology that can solve computationally rigid problems significantly faster than classical computers, potentially facilitating the development of Machine Learning (ML). This paper provides a deeper review of Quantum Machine Learning (QML), covering basic principles, quantum-classical techniques and real-world healthcare applications. The review paper evaluates the latest developments in quantum-enhanced ML models, including quantum support vector (QSVMs), quantum neural networks (QNNs), quantum principal component (QPCAs), quantum convolutional neural networks (QCNNs) and quantum K-nearest neighbor (QKNNs) techniques. Additionally, the review paper discusses complex challenges such as low computational power, huge, overfitting risk, high-dimensional and noisy datasets in classical ML techniques. Finally, the review paper summarizes the effectiveness of QML techniques compared with classical ML classification techniques based on their accuracy values. This review serves as a comprehensive resource for researchers exploring the classification of various non-communicable diseases (NCDs) on the intersection of quantum computing and machine learning.
Quantum Machine Learning (QML) is an emerging, powerful paradigm at the intersection of quantum computing and artificial intelligence, with the potential to enhance medical image analysis. We compared their performance with classical counterparts, while critically examining the gap between theoretical quantum advantages and the practical constraints of current Noisy Intermediate-Scale Quantum (NISQ) implementations. This review explored the current landscape of QML-based methodologies applied to key medical imaging modalities - Magnetic Resonance Imaging (MRI), Computed Tomography (CT), X-rays, and histopathology. We systematically analyzed how quantum-enhanced models were employed for critical tasks such as image classification, segmentation, and reconstruction, and we compared their performance with that of classical counterparts. The review delved into the architectural innovations that enabled these quantum approaches, including hybrid quantum-classical frameworks, variational quantum circuits, and quantum convolutional neural networks. Particular emphasis was given to the clinical relevance of QML applications, from early diagnostics to real-time surgery planning, and how quantum advantage might influence data efficiency, interpretability, along computational scalability. Finally, we highlighted current challenges in data encoding, noise resilience, and hardware limitations, and discussed promising future directions that could enable the practical deployment of QML in healthcare settings. This comprehensive review aimed to serve as a foundational reference for researchers and practitioners looking to harness quantum computing in medical imaging and precision medicine.
The combination of Quantum Computing with Machine Learning approaches is refered as Quantum Machine Learning (QML). Recently, there have been significant advancements in performance and computational speed to quantum computing techniques. Although traditional algorithms have shown promising outcomes, their low processing efficiency and limited labelled data are common problems. To address these issues, a great deal of research has brought machine learning and quantum computing together, which has led to the development of increasingly complex algorithms. Notable gains in parameter optimisation, execution efficiency, and error rate reduction have resulted from these developments. The incorporation of quantum machine learning has yielded novel perspectives into the transdisciplinary domain of quantum technology and medical image analysis, indicating noteworthy advancements to be made in this area going forward. QML is becoming known for its unique superposition and entanglement capabilities, which make it a cutting-edge approach to challenges involving categorization. Its uses in fields such as pattern identification, password cracking, and medical image analysis have attracted a lot of attention. Because of this, a growing number of academics are using QML-based algorithms to analyse medical data. This research review's key goal is to present an in-depth analysis of how QML techniques are used and involved in the medical field. This article includes a synopsis of the description and classification of quantum machine learning in addition to a rundown of the many QML techniques and how they have been used in medical image investigation during the previous years.
Quantum Machine Learning (QML) is an interdisciplinary field combining Quantum Computing (QC) and Machine Learning (ML). It has gained increased attention due to advances in near-term hardware implementations of quantum devices. The use of QML has proven to result in a significant improvement in performance and computational speed. Consequently, QML has become an effective technique for data processing and classification. Researchers have recently proposed various QML solutions in the medical image analysis field to gain an advantage of quantum supremacy. The main objective of this work is to present a holistic review of current leading-edge published works in the quantum medical image analysis field with a focus on supervised learning using artificial neural networks. A comparative study is used to pinpoint the potential of existing techniques, the most promising techniques, and the future of research in this area.