The convergence between artificial intelligence (AI) and quantum computing is ushering in a new era for problems requiring high levels of computational complexity, particularly in digital signal processing. This paper presents an overview and evaluation of the development trends in hybrid Quantum-AI models, focusing on applications for the recognition and classification of underwater acoustic signals—a challenging domain due to instability, high noise levels, and data scarcity. The study proposes a Hybrid Quantum-CNN model, utilizing CNN for feature extraction and dimensionality reduction, combined with a Variational Quantum Classifier for classification in Hilbert space. The results of the proposed model are compared with an equivalent CNN network, demonstrating that HQC achieves higher accuracy (an increase of 3,5%), while excelling in efficiency: faster convergence speed, and superior generalization capabilities on the same real underwater acoustic dataset. These findings highlight the benefits of quantum approaches not only in accuracy but also in constructing more efficient and robust models; hybrid models open promising avenues for applying quantum machine learning to complex signal reception systems in practical.
The convergence between artificial intelligence (AI) and quantum computing is ushering in a new era for problems requiring high levels of computational complexity, particularly in digital signal processing. This paper presents an overview and evaluation of the development trends in hybrid Quantum-AI models, focusing on applications for the recognition and classification of underwater acoustic signals—a challenging domain due to instability, high noise levels, and data scarcity. The study proposes a Hybrid Quantum-CNN model, utilizing CNN for feature extraction and dimensionality reduction, combined with a Variational Quantum Classifier for classification in Hilbert space. The results of the proposed model are compared with an equivalent CNN network, demonstrating that HQC achieves higher accuracy (an increase of 3,5%), while excelling in efficiency: faster convergence speed, and superior generalization capabilities on the same real underwater acoustic dataset. These findings highlight the benefits of quantum approaches not only in accuracy but also in constructing more efficient and robust models; hybrid models open promising avenues for applying quantum machine learning to complex signal reception systems in practical.
In the big data era, traditional machine learning algorithms like K-means face computational challenges in processing large, high-dimensional datasets, particularly for underwater acoustic signal classification. This study investigates the application of the quantum clustering algorithm Q-means, a quantum-enhanced variant of K-means, to real passive sonar datasets. The purpose is to evaluate Q-means' effectiveness in classifying propeller-driven ship signals under noisy conditions, leveraging quantum principles such as superposition and entanglement for exponential speedup. The research extends classical K-means to δ-k-means for robustness, implementing quantum subroutines including distance estimation, cluster assignment, and state tomography. Preprocessed features from power spectral density analysis of real datasets (from prior studies on varying ship speeds) are used as input to avoid issues with raw time-series data, such as high dimensionality and sensitivity to shifts. Simulations on 15,000 passive sonar samples demonstrate that Q-means achieves clustering quality comparable to K-means, with clear separation of three clusters and accurate centroids, while reducing complexity from O(n) to O(polylog(n)). This validates Q-means as a promising tool for large-scale, noisy acoustic data in national security applications, bridging quantum theory with practical machine learning.
Convolutional Neural Networks (CNNs) are extensively utilized for image classification due to their ability to exploit data correlations effectively. However, traditional CNNs encounter several challenges, including limitations in accuracy improvement, computational efficiency, explain ability, and generalization. As data dimensionality or model complexity increases, CNNs often become more difficult to train efficiently, leading to slower processing times. To overcome these challenges, Quantum Convolutional Neural Networks (QCNNs) offer a promising quantum-based approach to enhance existing models, particularly by improving training efficiency and reducing processing times. This paper provides a comparative analysis of classical CNNs and a novel quantum circuit architecture tailored for image-based tasks. The analysis emphasizes the adaptability and potential of quantum circuits to enhance feature extraction, recognition accuracy, and processing efficiency.We applied various QCNN architectures to the Chinese-MNIST dataset and compared their performance with traditional CNN models. Although QCNNs achieved slightly lower accuracy, they demonstrated significantly greater stability. These findings high- light QCNNs’ potential to overcome the computational limitations of conventional deep learning, offering a promising pathway for quantum-enhanced solutions in computer vision. Future work will focus on refining quantum circuit designs, developing hybrid quantum-classical models, and extending QCNN applications to larger and more diverse datasets.
This research explores quantum machine learning for image classification, comparing classical and quantum models. A classical convolutional neural network (CNN), based on the LeNet architecture, achieved 99.12% accuracy on MNIST, outperforming a quantum CNN, which attained 93.30%. Quantum encoders applied to Fashion-MNIST reached 85.60%, while quantum support vector machine (SVM), implemented for binary classification, obtained 71.34%. The study integrates hybrid quantum-classical models, leveraging quantum circuits for feature extraction. A quantum autoencoder-based classifier was introduced, demonstrating efficient data compression and classification without additional qubits. Feature encoding using RX gates mapped classical data into quantum states, and the impact of parameterized quantum circuit structures on classification accuracy was analyzed. Results indicate that specific ansatz structures yield superior accuracy, highlighting the significance of quantum circuit design. While classical models currently outperform quantum counterparts, quantum approaches reduce trainable parameters and computational complexity. Limitations in current quantum hardware, including noise and scalability, restrict performance. However, advancements in error mitigation, quantum gate efficiency, and scalable quantum architecture could bridge the gap with classical methods. These quantum techniques hold potential in aerospace applications, such as satellite image classification, anomaly detection in remote sensing, and real-time processing of high-dimensional image data. This study underscores the growing role of quantum circuits in machine learning, particularly in feature extraction and classification, suggesting that future quantum hardware could unlock new capabilities in end-to-end learning beyond hybrid approaches.
Background: The emergent need for scalable artificial intelligence (AI) models emphasizes the shortcomings of traditional machine learning in dealing with big, complicated datasets owing to computation and energy limitations. Objective: The architecture takes advantage of quantum encoding methods like amplitude and angle encoding for compact feature representation, with classical preprocessing providing compatibility and stability. Methods: To overcome these issues, this research introduces a Hybrid Quantum-Classical Machine Learning (HQML) framework that combines Quantum Variational Classifiers (QVCs) and Quantum Convolutional Neural Networks (QCNNs) with classical deep learning layers. Results: Experiments were performed on pre-curated datasets taken from the Scikit Machine Learning Repository, with hybrid models compared to classical Convolutional Neural Networks (CNNs). The implementation was done with PennyLane using TensorFlow Quantum for compatibility with noisy intermediate-scale quantum (NISQ) machines. The comparative analysis identified that the suggested HQML model achieves significant performance gains, as classification accuracy increased from 73 percent in the baseline classical CNN to 98% in the hybrid model. Precision also went up from 77 to 96 percent, recall from 81 to 100 percent, and F1-score from 79 to 98 percent. These findings confirm the ability of the model to leverage quantum benefits for extracting complicated data distributions without losing stability through classical components. The findings confirm the viability of HQML to real-world finance, healthcare, and intelligent automation applications where scalability and precision matter. Conclusion: This research establishes that quantum-classical integration not only minimizes training overhead but also attains near-perfect classification efficiency with constrained qubit resources. The research aids in filling the gap between theoretical quantum potential and deployable practical AI, opening the door for next-generation quantum-boosted intelligence.
Quantum computers are the latest dream of humans in solving nondeterministic polynomial problems. These problems include machine learning and object classification. However, commercial quantum computers are still not available. In the meantime, scientists developed hybrid models by combining Convolutional Neural Networks (CNN) and Quantum Neural Networks (QNN) to apply quantum computing to artificial intelligence (AI) applications. The hybrid model accelerates the processing of an AI machine by combining the advantage of CNN in feature extraction with the parallel computing capability of QNN. In this study, we optimized the existing hybrid models with a fewer number of qubits to improve the performance of QNN on digit recognition. The study also presents the implementation of the model using Qiskit, describes the training process, and analyzes the impact of the number of qubits and quantum gates on the classification results. Experiments show that Hybrid Quantum-Classical Neural Networks (H-QNN) have the potential to outperform pure CNN. Finally, the study suggests future improvements, including the optimization of quantum algorithms and extending experiments to more complex data sets.
Image classification has become a fundamental task in computer vision with applications in areas such as medical imaging, agriculture, environmental monitoring, and automated surveillance. Traditional machine learning techniques have achieved reasonable performance in classification tasks; however, they often struggle when dealing with high-dimensional and complex image datasets. Deep learning models, particularly Convolutional Neural Networks (CNNs), have significantly improved image classification performance by automatically learning hierarchical feature representations. Despite these advancements, classical deep learning models may still face challenges related to computational complexity and large-scale data processing.In recent years, quantum machine learning has emerged as a promising paradigm that combines principles of quantum computing with classical machine learning techniques to enhance computational efficiency and model performance. This study proposes a hybrid quantum–classical framework for image classification that integrates a deep residual network (ResNet-50) with a Quantum Support Vector Machine (QSVM). The ResNet-50 model is employed as a feature extraction mechanism to capture high-level visual representations from image data. The extracted features are then reduced in dimensionality using Principal Component Analysis (PCA) to simplify the feature space and improve computational efficiency.The reduced feature vectors are subsequently classified using a QSVM model that utilizes quantum feature maps to encode classical data into quantum states. Various quantum feature maps are explored to evaluate their impact on classification performance. Experimental results demonstrate that the hybrid quantum–classical approach achieves higher classification accuracy compared to conventional machine learning models such as Support Vector Machines and Random Forest classifiers. The proposed framework highlights the potential of combining classical deep learning architectures with quantum machine learning algorithms to address complex image classification challenges. This hybrid approach provides an efficient and scalable solution for advanced image analysis tasks and demonstrates the growing potential of quantum computing in artificial intelligence applications.