Driven by the development of machine learning (ML) and deep learning techniques, prognostics and health management (PHM) has become a key aspect of reliability engineering research. With the recent rise in popularity of quantum computing algorithms a...
Privacy is a major concern in the Internet Healthcare of Things (IoHT), where threat actors may intrude systems to access personally identifiable data. Federated Learning (FL) is a well suited Machine Learning (ML) approach to preserve confidentialit...
In recent advancements in agricultural technology, quantum mechanics and deep learning integration have shown promising potential to revolutionize rice yield forecasting methods. This research introduces a novel Hybrid Quantum Deep Learning model tha...
The rapid development of the Internet of Things (IoT) poses significant problems in securing heterogeneous, massive, and high-volume network traffic against cyber threats. Traditional intrusion detection systems (IDSs) are often found to be poorly sc...
Quantum machine learning (QML) is advancing rapidly, yet it remains unclear which artificial intelligence (AI) capabilities benefit from quantum approaches, how strong the supporting evidence is, and whether reported gains justify claims of practical...
This research introduces a quantum machine learning (QML) approach for predicting heart disease (HD). The method combines preprocessing of data with quantum feature map (QFM) and quantum classification techniques. In the method, clinical data of HD a...
Network intrusion detection systems (IDS) operate in high-dimensional feature spaces under evolving attack patterns and asymmetric misclassification costs, where false negatives represent a critical security risk. Reinforcement learning (RL) offers a...
Image reconstruction and generation have become fundamental tasks in computer vision and quantum-inspired machine learning. Existing quantum-inspired variational autoencoders have demonstrated promising capabilities; however, they often suffer from l...
Quantum machine learning is often proposed for richer feature representations, yet most evidence rests on idealized simulation rather than real noisy intermediate-scale quantum (NISQ) hardware. This research presents a controlled comparison of classi...
Reliable attribution of artificial intelligence (AI)-generated text to a specific large language model (LLM) matters increasingly as LLMs proliferate, yet where quantum machine learning actually stands on this task has, to our knowledge, never been m...
The Rio Santiago basin is one of the most environmentally stressed river systems in Mexico, with persistent organic, nutrient, microbial, surfactant, and metal contamination. This study develops a near-term quantum machine learning workflow for envir...
We tackle the problem of secure and private feature selection by homomorphically evaluating features’ information gains over the encrypted data of horizontally partitioned private datasets owned by edge IoT (Internet of Things) devices. In the...
Leukocytes, also referred to as white blood cells (WBCs), are a crucial component of the human immune system. Abnormal proliferation of leukocytes in the bone marrow leads to leukemia, a fatal blood cancer. Classification of various subtypes of WBCs...
This study introduces a data obfuscation technique that leverages the exponential map of Lie-group generators. Originating from quantum machine learning frameworks, the method injects controlled noise into these generators, deliberately breaking symm...
The rapid growth of digital communication has intensified spam-related threats, including phishing and malware, which employ advanced evasion tactics. Traditional filtering methods struggle to keep pace, driving the need for sophisticated machine lea...
Classical approaches often treat interaction as engineered product terms or as emergent patterns in flexible models, offering little control over how synergy or antagonism arises. We take a quantum-inspired view: following the Born rule (probability...
A novel multi-class classification method for bacteria detection termed quantum-behaved particle swarm optimization-based kernel extreme learning machine (QPSO-KELM) based on an electronic nose (E-nose) technology is proposed in this paper. Time and...
Electroencephalography (EEG)-based emotion recognition remains challenging under subject-independent evaluation because of substantial inter-subject variability. This study investigates the feasibility of state-fidelity-based quantum-kernel learning...
Brain tumor detection at early stages can increase the chances of the patient’s recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral p...
Diabetic foot ulcer (DFU) classification increasingly relies on learning frameworks that exploit medical image representations without centralizing raw data. This study presents FedQDFU, a feature-based federated quantum learning benchmark evaluating...
Hardware noise and finite sampling perturb the fidelity estimates forming a quantum-kernel Gram matrix. We measured how far three hardware-reconstructed Gram matrices depart from an exact statevector reference for one frozen four-qubit ZZ feature map...
The minimum accuracy heuristic provides a training-free way to evaluate quantum feature maps, but its original formulation assumes balanced datasets, requires an exhaustive Pauli-axis scan, and lacks a formal lower-bound interpretation. In this work,...
Quantum Machine Learning (QML) has recently emerged as a promising approach for cybersecurity applications due to its ability to encode data into high-dimensional Hilbert spaces. However, most QML-based intrusion detection systems rely on a single qu...
Background: Quantum Machine Learning (QML) has attracted significant attention in recent years. With quantum computing achievements in computationally costly domains, discovering its potential in improving the performance and efficiency of deep learn...
Reliable exercise recognition across schools requires models that remain accurate when acquisition conditions change. This study compares classical machine learning, pose-sequence deep learning, and quantum machine learning to classify four standardi...
Transformer architectures, originally developed for language modeling, have been increasingly applied to time-series forecasting, but deploying them on sensitive data such as patient vital signs, financial records, or energy use raises privacy risks...
Kidney and renal pelvic cancer are a significant cause of cancer-related deaths, with the most common malignant kidney tumor being renal cell carcinoma (RCC). Chromophobe renal cell carcinoma is a rarer form of RCC that poses significant challenges t...
Accurate day-ahead forecasting of photovoltaic (PV) power and electric load is essential for reliable power system operation under growing renewable penetration and demand complexity. Both series, however, are strongly nonstationary, with multiscale...
Image classification is a fundamental task in deep learning, and recent advances in quantum computing have generated significant interest in quantum neural networks. Traditionally, Convolutional Neural Networks (CNNs) are employed to extract image fe...
Deep Neural Networks have offered numerous innovative solutions to brain-related diseases including Alzheimer’s. However, there are still a few standpoints in terms of diagnosis and planning that can be transformed via quantum Machine Learning (QML)....
Federated learning enables multiple organizations to collaboratively analyze data while retaining local control over their datasets, making it attractive for applications such as healthcare. Distributed quantum computing provides a natural framework...
Quantum computing holds great promise for a number of fields including biology and medicine. A major application in which quantum computers could yield advantage is machine learning, especially kernel-based approaches. A recent method termed quantum...
Hybrid quantum–classical machine learning offers a promising direction for advancing automated quality control in industrial settings. In this study, we investigate two hybrid quantum–classical approaches for classifying defects in alumin...
Medical image segmentation is crucial for computer-aided diagnosis, providing pixel-level delineation of lesions and directly impacting subsequent quantitative analysis and clinical decisions. With the rapid progress of quantum computing, an increasi...
Molecular spins have shown interesting quantum features, which make them potential candidates for the implementation of quantum information processing. New challenges related to possible applications in the broader class of quantum technologies are c...
The growing vulnerability of computer vision models to adversarial attacks has raised great concerns about the reliability of generative models in security-sensitive applications. Quantum generative models show strong potential for learning complex d...
The classical neural network has provided remarkable results to diagnose neurological disorders against neuroimaging data. However, in terms of efficient and accurate classification, some standpoints need to be improved by utilizing high-speed comput...
Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does n...
In quantum machine learning, feature vectors are encoded into quantum states. Measurements for the discrimination of states are useful tools for classification problems. Classification algorithms inspired by quantum state discrimination have recently...
The earlier prediction of heart diseases and appropriate treatment are important for preventing cardiac failure complications and reducing the mortality rate. The traditional prediction and classification approaches have resulted in a minimum rate of...
Quantum kernels hold significant promise for achieving computational advantages in quantum machine learning (QML), yet their effectiveness critically depends on the design of expressive and hardware-compatible feature maps, a challenge that is partic...
Deep learning techniques have significantly enhanced medical image classification for disease diagnosis. However, traditional deep learning models face several challenges, such as dependence on large annotated datasets, complex architectural layers,...
The implementation of congestion-aware routing in MANETs and WSNs is challenging due to factors such as dynamic topology, unpredictable traffic, unreliable connections, mobile nodes, and limited energy resources. Equipped with relational Siamese repr...
Drug repositioning is a less expensive and time-consuming method than the traditional method of drug discovery. It is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indicat...
Our research introduces a framework that integrates edge computing, quantum transfer learning, and federated learning to revolutionize pain level assessment through ECG signal analysis. The primary focus lies in developing a robust, privacy-preservin...
We study a minimal quantum pre-processing filter for image feature extraction built from angle embeddings and two Control-NOT (CNOT) gates. Our goal is to assess whether such a lightweight quantum front-end can benefit classical classifiers and to in...
The emergence of deep vision models such as convolutional neural networks and vision transformers has revolutionized computer vision, enabling significant advancements in image classification, object detection, and segmentation. In parallel, the rapi...
In this work, we present a training method for a Fully Quantum Neural Network (FQNN) based entirely on quantum circuits. The model processes data exclusively through quantum operations, without incorporating classical neural network layers. In the pr...
In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the variational quantum circuit and the quantum kernel estimator (QKE). We provid...
Image classification is an important application for deep learning. With the advent of quantum technology, quantum neural networks (QNNs) have become the focus of research. Traditional deep learning-based image classification involves using a convolu...
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