A metaheuristic is a heuristic algorithm that is more or less an efficient optimization algorithm to real-world problems. Hybrid metaheuristics refer to a proper and judicious combination of several other metaheuristics and machine learning algorithms. A Quantum Genetic Algorithm can be evolved to perform an automatic feature Cancer Modelling and Simulation selection in order to select only those features that has a strong influence on a Support Vector Machine-based classifier. A Hybrid Quantum-Convolutional Neural Network method for atherosclerosis detection in XCA images can be envisaged. It includes a Quantum Convolutional Layer used as a preprocessing to improve the atherosclerosis detection performance of typical CNNs. A novel Multiobjective Quantum-inspired Salp Swarm Algorithm with Delta potential-well model presentation, a better alternative than the binary presentation for the multiobjective optimization problems, can be envisaged. The proposed algorithm has been compared with its classical counterpart. All the experiments have been performed over six Berkeley gray scale images.
A metaheuristic is a heuristic algorithm that is more or less an efficient optimization algorithm to real-world problems. Hybrid metaheuristics refer to a proper and judicious combination of several other metaheuristics and machine learning algorithms. A Quantum Genetic Algorithm can be evolved to perform an automatic feature Cancer Modelling and Simulation selection in order to select only those features that has a strong influence on a Support Vector Machine-based classifier. A Hybrid Quantum-Convolutional Neural Network method for atherosclerosis detection in XCA images can be envisaged. It includes a Quantum Convolutional Layer used as a preprocessing to improve the atherosclerosis detection performance of typical CNNs. A novel Multiobjective Quantum-inspired Salp Swarm Algorithm with Delta potential-well model presentation, a better alternative than the binary presentation for the multiobjective optimization problems, can be envisaged. The proposed algorithm has been compared with its classical counterpart. All the experiments have been performed over six Berkeley gray scale images.
Hybrid quantum systems have shown promise in image classification by combining the strengths of both classical and quantum algorithms. These systems leverage the parallel processing power of quantum computers to perform complex computations while utilizing classical algorithms to handle the vast amounts of data involved in imaging. The hybrid approach is intended to improve accuracy and speed compared to traditional classical methods. Further research and development in this area can revolutionize the way medical images are classified and help improve patient diagnosis and treatment. The use of Conventional Neural Networks (CNN) for the classification and diagnosis of medical images using big datasets requires, in most cases, the use of special high-performance computing machines, which are very expensive and hard to access by most researchers. A new form of Machine Learning (ML), Quantum machine learning (QML), is being introduced as an emerging strategy to overcome this problem. A hybrid quantum-classical CNN uses both quantum and classical convolution layers designed to use a parameterized quantum circuit. This means that the computing model utilizes a quantum circuits approach to construct QML algorithms, which are then used to transform the quantum state to extract image hidden features. This computational acceleration is expected to achieve better algorithm performance than classical CNNs. This study intends to evaluate the performance of a Hybrid Quantum CNN (HQCNN) against a conventional CNN. This is followed by some optimizer modifications for both proposed and classical CNN methods to investigate the possible further improvement of their performance. The optimizer modification is based on forcing the optimizer to be directly adaptive to model accuracy. The optimizer adaptiveness is based on the development of an optimizer with a loss base adaptive momentum. Several algorithms are developed to achieve the above-mentioned goals, including CNN, QCNN, CNN with the adaptive optimizer, and QCNN with the Adaptive optimizer. The four algorithms are tested against a Kaggle brain dataset containing over 7000 samples. The test results show the hybrid quantum circuit algorithm outperformed the conventional CNN algorithm. The performance of both algorithms was further improved by using a fully adaptive SGD optimizer.
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.
We propose a new hybrid system for automatically generating and training quantum-inspired classifiers on grayscale images by using multiobjective genetic algorithms. We define a dynamic fitness function to obtain the smallest possible circuit and highest accuracy on unseen data, ensuring that the proposed technique is generalizable and robust. We minimize the complexity of the generated circuits in terms of the number of entanglement gates by penalizing their appearance. We reduce the size of the images with two dimensionality reduction approaches: principal component analysis (PCA), which is encoded in the individual for optimization purpose, and a small convolutional autoencoder (CAE). These two methods are compared with one another and with a classical nonlinear approach to understand their behaviors and to ensure that the classification ability is due to the quantum circuit and not the preprocessing technique used for dimensionality reduction.
Coronary artery disease is the leading cause of mortality rate worldwide. This condition is caused by an abnormal narrowing or occlusion of the artery lumen, known as atherosclerosis (a specific type of stenosis). Currently, many imaging techniques have been tested; nevertheless, conventional X-ray Coronary Angiography (XCA) remains the gold standard for coronary artery disease diagnosis, such as atherosclerosis. This study presents a Hybrid Quantum-Convolutional Neural Network to detect coronary artery atherosclerosis in XCA images automatically. It employs a Quantum Convolutional Layer (QCL) that resembles a typical Convolutional Layer. However, unlike the convolution operation where a classical convolutional filter is applied, a QCL transforms the input using a quantum circuit. This QCL generates a multichannel image from a single channel XCA that feeds a traditional Convolutional Neural Network (CNN). The CNN was trained and optimized using two different architectures previously employed for atherosclerosis detection, viz. a DenseNet-based and a VGG-based architectures. Additionally, a comparison of Stochastic Gradient Descent (SGD) and SGD with Momentum (SGDM) were conducted. These architectures were analyzed and compared using a real dataset composed of 250 real XCA images (split into 125 images for testing and 125 for training) regarding their training without the QCL preprocessing and SGDM. The results showed that the CNNs trained with the outcome of the QCL surpasses the baseline CNNs performance. Herein, the best atherosclerosis detection reached an accuracy of 83.36%, while in other metrics such as precision, sensitivity, specificity, and F1-score, achieved an 81.52%, 86.67%, 83.99%, and 80.00%, respectively.
Quantum-inspired metaheuristics are solvers that incorporate principles inspired from quantum mechanics into classical-approximate algorithms using non-quantum machines. Due to the uniqueness of quantum principles, the inspiration of quantum phenomena and the way it is done in fundamentally different non-quantum systems rather than real or simulated quantum computers raise important questions about these algorithms’ design and the reproducibility of their results in real or simulated quantum devices. Thus, this work’s contribution stands in a first step towards answering those questions as an attempt to identify key findings in the existing literature that should be considered or adapted in order to build hybrid or fully-quantum algorithms that can be used in quantum machines. This is done by proposing and studying four inspired, simulated and real quantum cellular genetic algorithms that, as far as the authors’ knowledge, are the first quantum structured metaheuristics studied in the three quantum realms using a quantum simulator with 32 quantum bits and a real quantum machine employing 15 superconducting quantum bits. The users’ mobility management in cellular networks is taken as a validation problem using 13 real-world instances. The comparisons have been made against 6 diverse algorithms using 9 comparison metrics. Thorough statistical tests and parameters’ sensitivity analysis have been also conducted. The experiments allowed answering several questions, including how quantum hardware influences the studied-algorithms’ search process. They also enabled opening new perspectives in quantum metaheuristics’ design.
Breast cancer continues to be among the most common causes of cancer death globally, with early and precise diagnosis playing a pivotal role in enhancing patient survival. Traditional machine learning (ML) techniques have shown great promise in the field of medical imaging and diagnosis; however, the emergence of quantum machine learning (QML) offers new avenues for the improvement of pattern discovery and diagnostic accuracy in high-dimensional medical data. This work describes a thorough benchmark comparison of quantum and classical machine learning methods for breast cancer diagnosis over the Wisconsin Diagnostic Breast Cancer data and mammographic image data. We compare variational quantum classifiers (VQC), quantum kernel methods (QKM), quantum convolutional neural networks (QCNNs), and hybrid quantum-classical neural structures with traditional classical baselines like support vector machines (SVM) and convolutional neural networks (CNN). QKM techniques perform better in high-dimensional feature spaces and better generalize on external validation sets. This implementation framework describes in depth how to develop, train, and deploy QML models in the clinical workflow, including optimization approaches, code structures, and deployment issues. The work demonstrates that it is possible to streamline breast cancer screening and diagnosis using QML with more accurate and more efficient solutions, which would lead to a huge impact on patient outcomes.