The Search for Extraterrestrial Intelligence Institute (SETI) was established to gather and analyze radio signals from space in an effort to detect signs of advanced civilizations that may possibly exist elsewhere in the cosmos. A two-dimensional spectrogram is created using time-series raw data obtained from the Allen telescope. Rigorous data analysis has revealed that the SETI image data set is highly non-separable and challenging. In this paper, we suggest the use of a Hybrid Quantum-Classical convolutional Neural network (HQCNN) using a Quantum node serving to extract intricate features of SETI radio images that are being classified with classical convolutional Neural Network. We also generalized the combination of quantum gates in the Quantum node to improvise the performance of HQCNN. Quantum superposition and entanglement properties of Quantum computers render it feasible to comprehend the basic relationships in the data set more precisely than with traditional Machine Learning and Deep Learning techniques. In this context, with the notion of leveraging the computational benefit of an HQCNN, we, therefore, treated this challenging task as binary classification and filtered any two classes from the primary data set. The experimental analysis was executed in the Google Collaboratory platform with the Python packages such as Pennylane, Keras, and TensorFlow. To validate the outcomes, the HQCNN model is contrasted with various classical Deep learning models in terms of diverse performance metrics. The proposed model outperforms the other models in categorizing Allen Telescope data with the highest accuracy of 90.19% while utilizing a very small portion of the original data set, which motivates further research into the use of Quantum Machine Learning algorithms for signal processing tasks.
The Search for Extraterrestrial Intelligence Institute (SETI) was established to gather and analyze radio signals from space in an effort to detect signs of advanced civilizations that may possibly exist elsewhere in the cosmos. A two-dimensional spectrogram is created using time-series raw data obtained from the Allen telescope. Rigorous data analysis has revealed that the SETI image data set is highly non-separable and challenging. In this paper, we suggest the use of a Hybrid Quantum-Classical convolutional Neural network (HQCNN) using a Quantum node serving to extract intricate features of SETI radio images that are being classified with classical convolutional Neural Network. We also generalized the combination of quantum gates in the Quantum node to improvise the performance of HQCNN. Quantum superposition and entanglement properties of Quantum computers render it feasible to comprehend the basic relationships in the data set more precisely than with traditional Machine Learning and Deep Learning techniques. In this context, with the notion of leveraging the computational benefit of an HQCNN, we, therefore, treated this challenging task as binary classification and filtered any two classes from the primary data set. The experimental analysis was executed in the Google Collaboratory platform with the Python packages such as Pennylane, Keras, and TensorFlow. To validate the outcomes, the HQCNN model is contrasted with various classical Deep learning models in terms of diverse performance metrics. The proposed model outperforms the other models in categorizing Allen Telescope data with the highest accuracy of 90.19% while utilizing a very small portion of the original data set, which motivates further research into the use of Quantum Machine Learning algorithms for signal processing tasks.
Significant advances have been made in quantum computing, enabling researchers to create hybrid quantum- classical learning models for various tasks, including medical image classification. The classical approach to deep learning often faces challenges with limited data availability, high computational costs, and poor generalization. The hybrid Quantum- Classical Convolutional Neural Networks (HQCNNs) provide a new direction for overcoming the challenges faced by classical approaches. The hybrid approach enables the use of quantum circuits for feature encoding in higher-dimensional space, improving discrimination between patterns and reducing overfitting. In this paper, a hybrid quantum-classical convolutional neural network is developed for medical image classification. The hybrid approach is expected to capture non-linear relationships within medical image data while maintaining a lightweight network structure. The hybrid approach is found to have significant potential for efficient operation with limited data availability and noisy data. The classical approach is often outperformed by the hybrid approach. The focus of this paper is on architectural design and analysis of the developed hybrid quantum-classical convolutional neural network. The advantages of the developed hybrid quantum-classical convolutional neural network are de- lineated, including current challenges with quantum hardware.
Classifying astrophysical bodies like stars, galaxies, and black holes plays an underlying role in understanding the evolution of the universe. Effective analysis of a huge number of astronomical objects captured by telescopes is a challenge. For the classification of astronomical objects, quantum computing coupled with artificial intelligence proposes a good solution. Quantum machine learning (QML) utilizes the concepts of quantum computing to process huge datasets with greater accuracy and efficiency. In the current study, quantum features extraction with a convolutional neural network (CNN), named the AstroNet model, is proposed for the classification of astronomical objects. Quantum features extraction involves the conversion of raw images of astrophysical objects into quantum states for further processing. This is achieved by encoding the values of pixels in the amplitudes of quantum states using qubits. The AstroNet model consists of two phases. We first construct a quantum circuit. Entanglement is introduced by randomlayers, which employ cnot gates along with parameterized rotations. All quantum operations are implemented using the pennylane library in Python on a simulator. This enables efficient representation and processing of complex image data, setting the stage for quantum-powered classification in the second phase. These quantum features are passed to the proposed CNN model, which is designed from scratch, having seven layers, including two convolutional, two dense, two max pooling, and one flatten. The AstroNet is trained, based on the selected hyperparameters like the Adam optimizer, Sparse Categorical Cross-entropy, 32 batch-size, 0.0001 learning rate, and 10 epochs. The capability of the AstroNet model is examined based on the five top astrophysical object classification datasets including Galaxy Zoo, Galaxy Zoo 2, SpaceNet, Star-galaxy, and six classes of astronomical objects. The AstroNet model achieves up to 0.99 classification performance, which is much better than the previously reported methods in this field.
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, and millions of trainable parameters, which can increase computational load and cause overfitting. Although quantum computing offers potential for parallel processing and improved feature representation, its practical use is limited by the current capabilities of Noisy Intermediate-Scale Quantum (NISQ) hardware. To overcome these issues, Hybrid Quantum–Classical Convolutional Neural Networks (HQCNNs) have been developed, using quantum properties like superposition and entanglement to improve feature extraction and classification of medical images. This review offers a thorough analysis of 50 peer-reviewed studies published from 2021 to 2026 and introduces a taxonomy of HQCNN architectures. It classifies existing approaches into three categories: Quantum-to-Classical, Classical-to-Quantum, and Hybrid Quantum Transfer Learning. The studies reviewed encompass a diverse range of medical imaging applications, including binary and multi-class disease classification using X-ray, CT, MRI, ultrasound, dermoscopic, retinal, and mammographic images. Additionally, the review compares HQCNNs with their classical CNN counterparts, exploring how factors like quantum circuit design, dataset size, class imbalance, task complexity, evaluation protocols, and hardware limitations influence classification performance. It concludes with an analysis of the key limitations of current HQCNN models, discusses challenges in deploying NISQ devices practically, and proposes future research directions to bridge the gap between experimental HQCNN frameworks and reliable clinical diagnostic systems.
The growing complexity and scale of image processing tasks challenge classical convolutional neural networks (CNNs) with high computational costs. Hybrid quantum-classical convolutional neural networks (HQCNNs) show potential to improve performance by accelerating processing speed, enhancing classification accuracy, and reducing model parameters, though studies have primarily focused on the RGB color space. However, the effectiveness of HQCNNs in non-RGB color spaces, such as Lab, YCrCb, and HSV, remains largely unexplored. We propose an HQCNN to evaluate image classification across diverse color spaces. The HQCNN integrates parameterized quantum circuits (PQCs) with a classical CNN, leveraging quantum entanglement and trainable gates to enhance expressiveness across varied color representations. We assess performance on MNIST, CIFAR-10, EuroSAT, and SAT-4 datasets. Experimental results demonstrate that the HQCNN outperforms the classical CNN across all tested color spaces for the ten-class MNIST task, achieving a best accuracy of 94.3% in Lab compared to 92.8% in RGB for the CNN, with superior performance on other datasets in various color spaces. These findings highlight the potential of non-RGB color spaces and optimized PQC designs to improve classification performance. We provide new insights for advancing hybrid quantum-classical computer vision through optimized PQC architectures and diverse color space applications.
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.
Abstract Quantum Neural Networks (QNNs) present a promising research direction in image recognition, demonstrating significant potential for various applications. However, QNNs with a fixed circuit topology structure are prone to encountering the barren plateaus during training when processing exponential number of qubits, which limit their performance and scalability. In this paper, a novel Hybrid Quantum-Classical Inception-inspired Convolutional Neural Networks (HQCNN) is proposed. The HQCNN enhances feature learning capability and mitigates the barren plateau issue by integrating quantum convolutional kernels with diverse circuit topologies across multiple channels. The architecture employs an amplitude encoding layer to map multi-channel classical data into quantum states, followed by diverse quantum convolutional kernels that extract feature representations. Furthermore, Hadamard gates are used to intergrate multi-channel features and incorporate quantum pooling to reduce feature dimensionality, with a fully connected layer mapping the quantum-enhancing features into class labels for classification tasks. Finally, by analyzing the influence of the circuit structures and quantum gate combinations on the models' performance, the optimal HQCNN architecture is constructed. Experimental results demonstrate that HQCNN achieves 98.5% accuracy on the MNIST-4 classification task and 92.5% accuracy on the MNIST-10 classification task, which indicates that the proposed model outperforms state-of-the-art HQCNN models and classical CNN in image classification with about 10% parameters. This study provides an effective approach for alleviating the barren plateau issue and designing multi-channel quantum convolutional neural networks for processing big classic data with fewer quantum resources.