Quantum computing is a computational process that utilizes quantum mechanics features, namely superposition, interference, and entanglement, in information processing, allowing computation to run in parallel. The advantage of quantum computing is that it solves complex problems whereas classical computing is impossible because it requires expensive computing costs. Object detection and recognition is a task of computer vision, where research in this field aims to improve the ability of computer algorithms to produce interpretations of visual information. Humans easily analyze and describe the visual information received. However, unlike computer systems, they must learn and explore using machine learning from the visual information received to provide correct interpretations of visual information. This paper presents a systematic review of papers published from 2012 to 2024 to answer how far quantum object detection and recognition research has been conducted. The methodology of this review follows a systematic literature review such as the method proposed by Kitchenham et al. The selected primary studies amounted to 29 papers from four source digital libraries. The application of quantum algorithms is more often used to improve the performance of classical computing. The quantum model category consists of 3 types, namely pure quantum, hybrid classical-quantum, and quantum-inspired ML. Hybrid classical-quantum is the most discussed model and Quantum Convolutional Neural Network is the most frequently discussed algorithm or model in image classification from 2012 to 2024. Quantum algorithms show good results and can improve the performance of classical algorithms, although currently, the ability of quantum computing is not fully optimal because the development of quantum computers is still in the noisy intermediate-scale quantum era. However, with the current limited quantum computing capabilities, it can already outperform the capabilities of classical computing. Based on this, studies on quantum object detection and recognition need to be carried out so that when the full potential of quantum computing can be utilized, the user's capacity is competent.
Quantum computing is a computational process that utilizes quantum mechanics features, namely superposition, interference, and entanglement, in information processing, allowing computation to run in parallel. The advantage of quantum computing is that it solves complex problems whereas classical computing is impossible because it requires expensive computing costs. Object detection and recognition is a task of computer vision, where research in this field aims to improve the ability of computer algorithms to produce interpretations of visual information. Humans easily analyze and describe the visual information received. However, unlike computer systems, they must learn and explore using machine learning from the visual information received to provide correct interpretations of visual information. This paper presents a systematic review of papers published from 2012 to 2024 to answer how far quantum object detection and recognition research has been conducted. The methodology of this review follows a systematic literature review such as the method proposed by Kitchenham et al. The selected primary studies amounted to 29 papers from four source digital libraries. The application of quantum algorithms is more often used to improve the performance of classical computing. The quantum model category consists of 3 types, namely pure quantum, hybrid classical-quantum, and quantum-inspired ML. Hybrid classical-quantum is the most discussed model and Quantum Convolutional Neural Network is the most frequently discussed algorithm or model in image classification from 2012 to 2024. Quantum algorithms show good results and can improve the performance of classical algorithms, although currently, the ability of quantum computing is not fully optimal because the development of quantum computers is still in the noisy intermediate-scale quantum era. However, with the current limited quantum computing capabilities, it can already outperform the capabilities of classical computing. Based on this, studies on quantum object detection and recognition need to be carried out so that when the full potential of quantum computing can be utilized, the user's capacity is competent.
Quantum physics has changed the way we understand our environment, and one of its branches, quantum mechanics, has demonstrated accurate and consistent theoretical results. Quantum computing is the process of performing calculations using quantum mechanics. This field studies the quantum behavior of certain subatomic particles (photons, electrons, etc.) for subsequent use in performing calculations, as well as for large-scale information processing. These advantages are achieved through the use of quantum features, such as entanglement or superposition. These capabilities can give quantum computers an advantage in terms of computational time and cost over classical computers. Nowadays, scientific challenges are impossible to perform by classical computation due to computational complexity (more bytes than atoms in the observable universe) or the time it would take (thousands of years), and quantum computation is the only known answer. However, current quantum devices do not have yet the necessary qubits and are not fault-tolerant enough to achieve these goals. Nonetheless, there are other fields like machine learning, finance, or chemistry where quantum computation could be useful with current quantum devices. This manuscript aims to present a review of the literature published between 2017 and 2023 to identify, analyze, and classify the different types of algorithms used in quantum machine learning and their applications. The methodology follows the guidelines related to Systematic Literature Review methods, such as the one proposed by Kitchenham and other authors in the software engineering field. Consequently, this study identified 94 articles that used quantum machine learning techniques and algorithms and shows their implementation using computational quantum circuits or ansatzs. The main types of found algorithms are quantum implementations of classical machine learning algorithms, such as support vector machines or the k-nearest neighbor model, and classical deep learning algorithms, like quantum neural networks. One of the most relevant applications in the machine learning field is image classification. Many articles, especially within the classification, try to solve problems currently answered by classical machine learning but using quantum devices and algorithms. Even though results are promising, quantum machine learning is far from achieving its full potential. An improvement in quantum hardware is required for this potential to be achieved since the existing quantum computers lack enough quality, speed, and scale to allow quantum computing to achieve its full potential.
Quantum computing is the process of performing calculations using quantum mechanics. This field studies the quantum behavior of certain subatomic particles for subsequent use in performing calculations, as well as for large-scale information processing. These capabilities can give quantum computers an advantage in terms of computational time and cost over classical computers. Nowadays, there are scientific challenges that are impossible to perform by classical computation due to computational complexity or the time the calculation would take, and quantum computation is one of the possible answers. However, current quantum devices have not yet the necessary qubits and are not fault-tolerant enough to achieve these goals. Nonetheless, there are other fields like machine learning or chemistry where quantum computation could be useful with current quantum devices. This manuscript aims to present a Systematic Literature Review of the papers published between 2017 and 2023 to identify, analyze and classify the different algorithms used in quantum machine learning and their applications. Consequently, this study identified 94 articles that used quantum machine learning techniques and algorithms. The main types of found algorithms are quantum implementations of classical machine learning algorithms, such as support vector machines or the k-nearest neighbor model, and classical deep learning algorithms, like quantum neural networks. Many articles try to solve problems currently answered by classical machine learning but using quantum devices and algorithms. Even though results are promising, quantum machine learning is far from achieving its full potential. An improvement in the quantum hardware is required since the existing quantum computers lack enough quality, speed, and scale to allow quantum computing to achieve its full potential.
Quantum Machine Learning (QML) has emerged as a promising interdisciplinary paradigm that integrates quantum computing and machine learning to address limitations of classical approaches in processing complex high-dimensional heterogeneous and imbalanced medical data. This study presents a systematic literature review of QML applications in healthcare based on 94 primary studies published between 2020 and April 2025. The review follows Kitchenham’s guidelines and the PRISMA framework to ensure rigor transparency and reproducibility. Seven research questions were formulated to examine publication trends sources datasets topics quantum model categories algorithms methods and evaluation metrics. Findings reveal a sharp increase in publication activity after 2023 indicating growing academic interest in healthcare-oriented QML. Medical imaging datasets were most frequently used followed by signal-based and tabular datasets with 86.5% of studies relying on public data. Hybrid classical-quantum approaches including CNN-QSVM Quantum Neural Networks and Variational Quantum Classifiers were dominant because they combine classical feature extraction with quantum-enhanced learning. However most studies were conducted in simulation environments rather than real hardware reflecting NISQ limitations including noise decoherence limited qubits and circuit depth constraints. Many results used small curated or imbalanced datasets raising concerns regarding generalizability reproducibility and clinical validity. Although numerous studies reported high accuracy standardized evaluation protocols external validation and uncertainty assessment remain limited. Overall QML in healthcare remains exploratory with substantial potential but significant barriers. This review highlights progress gaps and future directions emphasizing clinically representative datasets robust benchmarking interpretable noise-resilient models and real-hardware implementation to support trustworthy deployment in healthcare systems worldwide settings today.
Abstract Quantum convolutional neural networks (QCNNs) are gaining increasing attention as a new class of models in quantum machine learning, especially for image classification and other computer vision tasks. Recent advances include the development of hybrid classical quantum models, novel quantum coding schemes, and innovative circuit architectures that enable efficient processing of high-dimensional visual data under the constraints of Noisy Intermediate-Scale Quantum (NISQ) devices. Despite the progress in QCNN design, practical deployment of these models is hampered by the complexity of quantum circuit implementation. Circuit depth, gate count, qubit connectivity, and hardware noise pose significant challenges to scalability and performance. As a result, quantum circuit optimization has become a critical area of research aimed at improving the efficiency of QCNNs, reducing quantum resource requirements, and improving classification accuracy. Among various approaches, heuristic and metaheuristic optimization methods such as genetic algorithms and evolutionary strategies have shown notable promise in addressing the high-dimensional and non-convex nature of the optimization space. This study presents a systematic literature review of 40 key research papers published between 2014 and 2025, 1 selected through strict inclusion and quality assessment criteria. The review covers key aspects of QCNN development, including architectures, coding strategies, and application areas, followed by an in-depth analysis of optimization method-ologies, objectives, and evaluation metrics. The results reveal emerging trends in hybrid quantum-classical integration, the widespread use of metaheuristic algorithms for circuit tuning, and the importance of multi-objective optimization frameworks tailored to the constraints of quantum computing. This review highlights critical research gaps and outlines future directions for advancing QCNN models and their practical implementation on quantum devices in the near future.
Quantum convolutional neural networks (QCNNs) are emerging as promising models in quantum machine learning, particularly for image classification and computer vision tasks. Recent developments include hybrid classical–quantum architectures, advanced quantum encoding methods, and novel circuit designs that improve data processing on Noisy Intermediate-Scale Quantum (NISQ) devices. However, practical implementation remains challenging due to circuit complexity, gate count, qubit connectivity, and hardware noise, which limit scalability and performance. Consequently, quantum circuit optimization has become essential for reducing resource requirements and improving classification accuracy. This study presents a systematic literature review of 40 research papers published between 2014 and 2025. The review covers QCNNs together with closely related quantum neural network (QNN) models and quantum circuit optimization studies, since circuit-optimization techniques are frequently developed for QNNs more broadly rather than for QCNN architectures in isolation. Within this scope, it examines network architectures, encoding strategies, application domains, and optimization techniques, with particular attention to heuristic and metaheuristic approaches such as genetic algorithms and evolutionary strategies. The findings highlight growing trends in hybrid quantum–classical integration, the widespread adoption of metaheuristic optimization, and the importance of multi-objective frameworks adapted to quantum hardware constraints. Finally, the review identifies key research gaps and future directions for practical QCNN deployment on near-term quantum devices.
Quantum deep learning (QDL) for image classification has emerged as a rapidly growing research area at the intersection of quantum computing and computer vision. However, the literature remains fragmented across diverse model architectures, datasets, and evaluation settings, making it difficult to obtain a focused understanding of the field. This study presents a systematic review of QDL models for image classification published between 2020 and 2025. The review was conducted using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-based selection process supported by a mapping perspective. Relevant studies were identified from major academic databases, screened using predefined inclusion and exclusion criteria, and analyzed through a taxonomy-based framework. A total of 47 studies were included and organized into four main architectural categories: Quantum Neural Networks (QNNs), Quantum Convolutional Neural Networks (QCNNs), Quantum Vision Transformers (QViT), and Quantum Attention Mechanisms. The findings show that most existing approaches rely on hybrid quantum-classical designs, where quantum components are integrated selectively into specific stages of the learning pipeline rather than replacing the full classical architecture. Several studies report encouraging results, such as improved parameter efficiency, competitive ACC (accuracy), and robustness in specific settings. Current progress remains constrained by hardware noise, limited qubit availability, encoding overhead, scalability challenges, and inconsistent benchmarking practices. It remains a promising but still emerging direction, with future progress expected to depend on more scalable architectures, fairer evaluation protocols, and broader validation on realistic application.