Abstract Mental stress detection through electroencephalography (EEG) signals faces difficulties because EEG signals display non-stationary behavior and are vulnerable to artifact interference. The research introduces a hybrid deep-quantum learning framework which combines a one-dimensional convolutional neural network (1D-CNN) with a quantum support vector classifier (QSVC) to differentiate between stress and calm states. The CNN functions as a trainable feature extractor which converts raw EEG time-series data into compressed latent representations that undergo mapping to a quantum Hilbert space through ZZFeatureMap before being classified by a quantum kernel. The research demonstrates through experiments on a multi-subject EEG dataset that the hybrid model reaches its highest accuracy of (81.00%) (F1: 0.81) under optimal configuration using 8-dimensional features with MinMax scaling to [0,1] and 1000 training samples because it surpasses both the classical SVM (76.80%) and end-to-end CNN (78.85%) baselines. The analysis demonstrates that quantum kernel performance depends on feature dimensionality and scaling because lower dimensions (F-4) lack expressivity while higher dimensions (F-12) reach a performance limit and scaling to [-1,1] causes accuracy loss of 16 percentage points when compared to [0,1] scaling. The quantum classifier shows good sample efficiency because it gains more from additional training data compared to the traditional model. The research demonstrates that hybrid deep-quantum architectures function in the NISQ era while creating a performance standard for quantum-based physiological signal processing.
Abstract Mental stress detection through electroencephalography (EEG) signals faces difficulties because EEG signals display non-stationary behavior and are vulnerable to artifact interference. The research introduces a hybrid deep-quantum learning framework which combines a one-dimensional convolutional neural network (1D-CNN) with a quantum support vector classifier (QSVC) to differentiate between stress and calm states. The CNN functions as a trainable feature extractor which converts raw EEG time-series data into compressed latent representations that undergo mapping to a quantum Hilbert space through ZZFeatureMap before being classified by a quantum kernel. The research demonstrates through experiments on a multi-subject EEG dataset that the hybrid model reaches its highest accuracy of (81.00%) (F1: 0.81) under optimal configuration using 8-dimensional features with MinMax scaling to [0,1] and 1000 training samples because it surpasses both the classical SVM (76.80%) and end-to-end CNN (78.85%) baselines. The analysis demonstrates that quantum kernel performance depends on feature dimensionality and scaling because lower dimensions (F-4) lack expressivity while higher dimensions (F-12) reach a performance limit and scaling to [-1,1] causes accuracy loss of 16 percentage points when compared to [0,1] scaling. The quantum classifier shows good sample efficiency because it gains more from additional training data compared to the traditional model. The research demonstrates that hybrid deep-quantum architectures function in the NISQ era while creating a performance standard for quantum-based physiological signal processing.
Applications in behavioural research, human-computer interaction, and mental health depend on the ability to recognize emotions. In order to improve the accuracy of emotion recognition using electroencephalography (EEG) data, this work presents a hybrid quantum deep learning technique. Conventional EEG-based emotion recognition techniques are limited by noise and high-dimensional data complexity, which make feature extraction difficult. To tackle these issues, our method combines traditional deep learning classification with quantum-enhanced feature extraction. To identify important brain wave patterns, Bandpass filtering and Welch method are used as preprocessing techniques on EEG data. Intricate inter-band interactions that are essential for determining emotional states are captured by mapping frequency band power attributes (delta, theta, alpha, and beta) to quantum representations. Entanglement and rotation gates are used in a hybrid quantum circuit to maximize the model's sensitivity to EEG patterns associated with different emotions. Promising results from evaluation on a test dataset indicate the model's potential for accurate emotion recognition. The model will be extended for real-time applications and multi-class categorization in future study, which could improve EEG-based mental health screening instruments. This method offers a promising tool for applications in adaptive human-computer systems and mental health monitoring by showcasing the possibilities of fusing traditional deep learning with quantum processing for reliable, scalable emotion recognition.
Stress and emotion monitoring via EEG is a critical task in human-centered neurotechnology, whereas existing machine learning systems often face challenges regarding high computational cost and limited explainability. In this paper, an Explainable Quantum EEG Analysis (EQEA) framework is presented for real-time stress detection by seamlessly integrating quantum-inspired feature modeling with interpretable classification. Multi-channel EEG data are encoded using a Quantum Variational Fusion Network that captures nonlinear spectral-temporal relationships via variational quantum circuits to produce compact feature embeddings. These representations are then classified by the Dual-Stage Hybrid Quantum Classifier that aggregates quantum decision boundary with lightweight deep-learning refinement in order to reduce the computational overhead. The explainability module, Explainable Entangled Feature Attribution, adopts SHAP-based entanglement attribution to identify critical EEG channels and frequency bands that contribute to stress prediction. Experimental results on multi-subject EEG datasets showed 94.8% accuracy, 4 times reduction in feature dimensionality, and 32% lower computation time compared to classical CNN and SVM models. The proposed EQEA framework delivers an interpretable and scalable solution for real-time stress assessment in wearable neuro-monitoring and brain-computer interface applications.
Deep learning has achieved significant success in pattern recognition, with convolutional neural networks (CNNs) serving as a foundational architecture for extracting spatial features from images. Quantum computing provides an alternative computational framework, a hybrid quantum-classical convolutional neural networks (QCCNNs) leverage high-dimensional Hilbert spaces and entanglement to surpass classical CNNs in image classification accuracy under comparable architectures. Despite performance improvements, QCCNNs typically use fixed quantum layers without incorporating trainable quantum parameters. This limits their ability to capture non-linear quantum representations and separates the model from the potential advantages of expressive quantum learning. In this work, we present a hybrid quantum-classical-quantum convolutional neural network (QCQ-CNN) that incorporates a quantum convolutional filter, a shallow classical CNN, and a trainable variational quantum classifier. This architecture aims to enhance the expressivity of decision boundaries in image classification tasks by introducing tunable quantum parameters into the end-to-end learning process. Through a series of small-sample experiments on MNIST, F-MNIST, and MRI tumor datasets, QCQ-CNN demonstrates competitive accuracy and convergence behavior compared to classical and hybrid baselines. We further analyze the effect of ansatz depth and find that moderate-depth quantum circuits can improve learning stability without introducing excessive complexity. Additionally, simulations incorporating depolarizing noise and finite sampling shots suggest that QCQ-CNN maintains a certain degree of robustness under realistic quantum noise conditions. While our results are currently limited to simulations with small-scale quantum circuits, the proposed approach offers a potentially promising direction for hybrid quantum learning in near-term applications.
Abstract Neuro-imaging data is necessary for timely and correct diagnosis of the disease Alzheimer’s (AD). Early clinical intervention is challenging due to the high-dimensional nature of the images, making it computationally difficult to work on representations, sparse annotated datasets and inter-subject variance. Deep convolutional VGG, ResNet, DenseNet, MobileNet and other neural networks (CNNs) have shown good performance. Diagnostic performance, but they are based on a high number of parameters and require highly trained personnel. Data can often times restrict resilience to data-skimy clinical settings. To address these challenges, the current paper presents a hybrid Quantum Machine Learning (QML) architecture for Alzheimer’s disease Detection and classification based on a combination of classical CNN-based feature extraction and Variational quantum classifier. In the suggested method, diseased, relevant, and compact features are extracted using pre-trained CNN backbones. Data from structural MRI scans is later translated into quantum states and computed. With parameterised variational quantum circuits optimised with a hybrid quantum-classical learning loop. The proposed method has been experimentally evaluated on benchmark datasets of MRI scans of Alzheimer’s patients. The hybrid CNNQML model has a mean classification accuracy of about 93% which is similar to deep CNN models, including ResNet and DenseNet (94%), and much more. Beating lightweight models like MobileNet and shallow CNNs by six percentage points. It is worth noting that the hybrid QML model performs better in conditions with limited training data. Performance degradation is observed in accuracy and AUC for all CNN variants, with up to 5% lower performance to deep CNNs. Cross-validation, confidence interval, paired significance test, and statistical validation. Effect size analysis proves the fact that the hybrid model produces statistically significant performance, as well as cases of clinically meaningful improvements over lightweight CNN baselines. Similar to the state-of-the-art deep networks with significantly reduced trainable parameters in the classification stage. These findings show that hybrid QML is a data-efficient and powerful substitute in the diagnosis of Alzheimer’s. On the whole, this is a work that sets a practical way to integrate quantum machine learning into clinical neuro-imaging pipelines and hybrid settings. QML has supplementary deep-learning enhancement to classics in next-generation Alzheimer’s Decision-support systems.
Diagnosing multimodal medical conditions necessitates seamlessly integrating the insights of both medical images and physiological signals, aiming to boost disease detection accuracy and efficiency in clinical decision making. However, existing deep learning models struggle to exploit sophisticated cross-modal relations and higher-order feature interactions efficiently and computationally. To overcome such shortcomings, we propose a Quantum-Deep Hybrid Architecture to jointly learn the healthcare signal and image features by incorporating Variational Quantum Circuits (VQCs) into a combined architecture based on Convolutional Neural Networks (CNNs) and 1D-CNNs. In our approach, medical image data and physiological signal data pass through distinct CNN and 1D-CNN branches to extract spatial and temporal features, respectively. These encoded, lower-dimensional feature vectors are encoded into quantum states via angle encoding and learn quantum-enhanced features via a Variational Quantum Circuit. The derived quantum and classical features are fused via fully connected layers and a softmax activation function for classification. Moreover, we exploit the Bayesian optimization to select the optimum hyper-parameters and integrate both privacy-preserving feature encoding and secure data processing into the framework. Empirical evaluation results reveal that the developed model achieves overall classification accuracy, precision, recall, F1-score of 94.8%, 95.1%, 94.3%, 94.7%, respectively, with an average AUC of 0.968, surpassing the contemporary CNN and multimodal deep learning baselines. Our proposed hybrid model also shows competitive performance under varying levels of noise and demonstrates excellent stability. The results also suggest the developed framework offers an efficient, robust and scalable approach to next-generation multimodal healthcare analytics and intelligent clinical decision-making systems.
Timely clinical intervention at the early stage for detecting widespread psychological health issues such as depression and anxiety is essential. Conventional diagnostic approaches that are generally based on subjective questionnaires face limitations which can result in delays as well as imprecisions. Current data-driven methodologies employ features that are extracted from multiple sources (behavioural surveys, clinical attributes and physiological signals excluding EEG and ECG). Although known classical ML models such as RF and LR are effective, the limitations of these models on capturing extremely complex non-linear issues result in their performance being limited. This performance upper bound is validated by the combined classical benchmark accuracy of 86.59%. This paper introduces a new quantum hybrid support vector machine (QSVM) framework, and shows how it can dramatically improve prediction accuracy on such mental health conditions. The system uses a hybrid design, in which the classical processing part computes and sends out a processed feature map to be quantum mapped into a Q exponentially larger-dimensional Hilbert space through quantum kernel methods (Quantum Feature Maps). By taking advantage of the quantum effects, the QSVM implicitly computes kernels that are difficult for classical machines and allows achieving better classification boundaries. The accuracy of QSVM is critically evaluated against the RF and LR benchmarks. Thus The Hybrid QSVM shows preliminary signs of quantum advantage, particularly in precision and kernel separability, although accuracy remains comparable to classical baselines. Validation on real quantum hardware will be required to establish practical quantum advantage.