The classification of Autism Spectrum Disorder (ASD) using neuroimaging techniques is still not an easy task, as the features have a high dimension, inter-site variability, and a small number of labeled samples. In this paper, HybridVQC, a quantum-inspired hybrid neural architecture, incorporates mathematical concepts of quantum circuits into a fully classical, learning architecture that can be executed on a GPU. ABIDE Structural MRI slices are fed through a pretrained backbone of ResNet-18 to obtain deep representations, and the deep representations are further shrunk to 16 principal components in Principal Component Analysis (PCA). The low features are subsequently applied to a special QuantumLikeLayer, which uses trigonometric encoding and dense mixing of features in order to simulate quantum rotation and entanglement effects on ordinary CUDA. The results of experiments with 1,693 structural MRI slices reveal that the maximum validation and test accuracy is 80.63 and 75.0, respectively, versus 56% of a classical SVM baseline using the same parameters. The findings point to quantum-inspired non-linear transformations have the potential to enhance feature separability and training stability in neuroimaging classification, and do not use quantum simulators or physical quantum systems.
The classification of Autism Spectrum Disorder (ASD) using neuroimaging techniques is still not an easy task, as the features have a high dimension, inter-site variability, and a small number of labeled samples. In this paper, HybridVQC, a quantum-inspired hybrid neural architecture, incorporates mathematical concepts of quantum circuits into a fully classical, learning architecture that can be executed on a GPU. ABIDE Structural MRI slices are fed through a pretrained backbone of ResNet-18 to obtain deep representations, and the deep representations are further shrunk to 16 principal components in Principal Component Analysis (PCA). The low features are subsequently applied to a special QuantumLikeLayer, which uses trigonometric encoding and dense mixing of features in order to simulate quantum rotation and entanglement effects on ordinary CUDA. The results of experiments with 1,693 structural MRI slices reveal that the maximum validation and test accuracy is 80.63 and 75.0, respectively, versus 56% of a classical SVM baseline using the same parameters. The findings point to quantum-inspired non-linear transformations have the potential to enhance feature separability and training stability in neuroimaging classification, and do not use quantum simulators or physical quantum systems.
The classification of Autism Spectrum Disorder (ASD) using neuroimaging techniques is still not an easy task, as the features have a high dimension, inter-site variability, and a small number of labeled samples. In this paper, HybridVQC, a quantum-inspired hybrid neural architecture, incorporates mathematical concepts of quantum circuits into a fully classical, learning architecture that can be executed on a GPU. ABIDE Structural MRI slices are fed through a pretrained backbone of ResNet-18 to obtain deep representations, and the deep representations are further shrunk to 16 principal components in Principal Component Analysis (PCA). The low features are subsequently applied to a special QuantumLikeLayer, which uses trigonometric encoding and dense mixing of features in order to simulate quantum rotation and entanglement effects on ordinary CUDA. The results of experiments with 1,693 structural MRI slices reveal that the maximum validation and test accuracy is 80.63 and 75.0, respectively, versus 56% of a classical SVM baseline using the same parameters. The findings point to quantum-inspired non-linear transformations have the potential to enhance feature separability and training stability in neuroimaging classification, and do not use quantum simulators or physical quantum systems.
Early and accurate identification of autism spectrum disorder (ASD) remains a central challenge in clinical neuroscience due to the subjective nature and high resource demands of traditional diagnostic methods. This paper presents a hybrid classical–quantum learning framework for ASD detection, combining the representational power of quantum circuits with the reliability of classical machine learning. The proposed model, a Variational Quantum Classifier (VQC) implemented using PennyLane and PyTorch, processes structured behavioural and demographic screening data from child, adolescent, and adult cohorts. Features are classically preprocessed, encoded into quantum states through angle embedding, and refined using strongly entangling layers, before being post-processed through a classical dense network. Experimental results show perfect classification performance with 100% accuracy, precision, recall, and F1-score, outperforming comparable classical approaches. The training phase exhibited smooth convergence across 70 epochs, confirming model stability and learning efficiency. Although evaluated in simulation, this study establishes a proof-of-concept for leveraging quantum machine learning in medical diagnostics, laying groundwork for future integration with real quantum hardware in clinical screening 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.
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose diagnosis relies heavily on subjective behavioral assessments. Electroencephalography (EEG) provides an inexpensive and non-invasive method of objective biomarker detection but is plagued by high dimensionality and noise that complicates automated analysis. This article introduces a hybrid Transformer–Quantum Neural Network (QNN) model that combines deep learning and quantum computing to enhance P300 targeted ASD responses from non-targeted responses. The model uses transformer capabilities captures cross-domain correlations and feature-level dependencies across heterogeneous feature sources and the quantum heads introduced via parameterized quantum circuits detects high-order, nonlinear relations in a Hilbert space via entanglement and superposition. Experimental results on the BCIAUT-P300 dataset better classical alternatives with higher diagnostic accuracy at higher accuracy (0.921) and F1-score (0.915). Statistical comparisons confirm that the hybrid approach yields significant improvements in discriminative reliability and diagnostic odds ratio, justifying quantum feature embedding’s expressivity and efficiency. The results bear witness to the feasibility and potential of hybrid quantum–classical systems as potential future intelligent diagnostic devices for ASD responses from EEG signals.
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.
Given the recent prominence of Noisy Intermediate Scale Quantum (NISQ) devices, hybrid quantum-classical neural networks have recently drawn a lot of excitement as a potential solution to improving learning efficiency. Our current paper describes a Hybrid Quantum-Classical Convolutional Neural Network (HQCNN) which is defined as a pretrained backbone of ResNet-18 and a small quantum-enhanced feature-transformation layer of Qiskit. The convolutional high-dimensional features are reduced to the classical eight-dimensional one, which in turn is encoded into a four-qubit parameterized quantum circuit. The resultant quantum expectation values are combined with the compressed features to come up with a hybridized representation of twelve dimensions, used to classify multi-class dataset. Experimental analysis of CIFAR10 data has shown that the suggested HQCNN achieves a test accuracy of 94.74% and improves class-wise precision, recall, and F1-scores than a number of existing hybrid models while using a significantly smaller number of latent features. More investigation into the quantum module indicates that it is predictable about scalability to batch, resistant to perturbations in features, and efficiently performed by evaluating circuit vectors. These results indicate that compact and expressive representations of images that can be helpful in the NISQ era can be acquired using hybrid architectures, thus providing a practical way to forward the benefits of quantum to modern deep-learning pipelines.