Quantum continual learning (QCL) has recently emerged as a promising paradigm, enabling quantum models to incrementally acquire knowledge across tasks while mitigating catastrophic forgetting. Yet, existing approaches face two persistent obstacles: (i) quantum architectures scale poorly to datasets of practical size, and (ii) continuous learning strategies are often entangled with specific quantum models, reducing their adaptability. To address these challenges, we introduce a twofold design. First, we propose hybrid quantum-classical (HQC) networks tailored to different data regimes: HQC1, optimized for Split-MNIST, achieves a$\mathbf{1 3 3. 5 \%}$average accuracy improvement over pure quantum baselines, while HQC2 is the first to demonstrate QCL on CIFAR-100, boosting accuracy by 14.66% over a comparable ResNet-18. Second, we present a decoupled QCL framework that separates model architecture from learning strategy, enabling flexible alignment and yielding more than double the average accuracy relative to existing pairings. Together, these contributions establish a scalable and adaptable foundation for practical quantum continuous learning on large, complex datasets.
Quantum continual learning (QCL) has recently emerged as a promising paradigm, enabling quantum models to incrementally acquire knowledge across tasks while mitigating catastrophic forgetting. Yet, existing approaches face two persistent obstacles: (i) quantum architectures scale poorly to datasets of practical size, and (ii) continuous learning strategies are often entangled with specific quantum models, reducing their adaptability. To address these challenges, we introduce a twofold design. First, we propose hybrid quantum-classical (HQC) networks tailored to different data regimes: HQC1, optimized for Split-MNIST, achieves a133.5%average accuracy improvement over pure quantum baselines, while HQC2 is the first to demonstrate QCL on CIFAR-100, boosting accuracy by 14.66% over a comparable ResNet-18. Second, we present a decoupled QCL framework that separates model architecture from learning strategy, enabling flexible alignment and yielding more than double the average accuracy relative to existing pairings. Together, these contributions establish a scalable and adaptable foundation for practical quantum continuous learning on large, complex datasets.
Quantum computing promises computational advantages for solving complex optimization problems; however, current Noisy Intermediate-Scale Quantum (NISQ) devices suffer from limited qubit counts, short coherence times, and high error rates. Fully fault-tolerant quantum algorithms remain impractical in the near term, necessitating hybrid approaches that integrate classical and quantum computation. This paper proposes a Hybrid Quantum–Classical Machine Learning Framework (HQC-MLF) for large-scale combinatorial and continuous optimization tasks. The framework leverages parameterized quantum circuits (PQCs) embedded within classical optimization loops, enabling efficient search in high-dimensional spaces while mitigating hardware constraints. We introduce adaptive ansatz selection, classical preconditioning, error mitigation strategies, and resource-aware scheduling to enhance scalability. Experimental evaluations on logistics routing, portfolio optimization, and energy load balancing demonstrate improved convergence speed and solution quality compared to purely classical baselines under realistic NISQ simulations. The proposed architecture provides a practical pathway toward near-term quantum advantage in real-world optimization problems
Quantum machine learning has emerged as a promising approach to enhance computational efficiency by leveraging the principles of quantum computing. However, the limitations of current Noisy Intermediate-Scale Quantum (NISQ) devices, such as noise, limited qubit availability, and circuit depth constraints, restrict the implementation of fully quantum models. To address these challenges, this study focuses on the design, implementation, and performance evaluation of hybrid quantum-classical machine learning (HQML) models. The proposed approach integrates parameterized quantum circuits with classical optimization techniques to enable efficient learning within NISQ environments. The study employs standard benchmark datasets, including Iris, Breast Cancer, and MNIST, to evaluate the performance of the hybrid model. The results indicate that the HQML model achieves competitive accuracy on small and medium-sized datasets while maintaining balanced precision, recall, and F1-score. However, performance declines for complex datasets due to hardware limitations and noise effects. Additionally, the hybrid model demonstrates a lower number of parameters compared to classical deep learning models but requires higher training time due to iterative quantum-classical optimization. The findings highlight that hybrid quantum-classical models provide a practical and scalable approach for utilizing quantum computing in the current technological landscape. Although challenges related to noise, scalability, and computational overhead persist, advancements in quantum hardware and algorithm design are expected to improve performance. This study contributes to the growing field of quantum machine learning by providing a systematic framework for evaluating hybrid models on NISQ devices and identifying key areas for future research.
This study presents a systematic comparison between hybrid quantum-classical neural networks and purely classical models across three benchmark datasets (MNIST, CIFAR100, and STL10) to evaluate their performance, efficiency, and robustness. The hybrid models integrate parameterized quantum circuits with classical deep learning architectures, while the classical counterparts use conventional convolutional neural networks (CNNs). Experiments were conducted over 50 training epochs for each dataset, with evaluations on validation accuracy, test accuracy, training time, computational resource usage, and adversarial robustness (tested with ε=0.1 perturbations).Key findings demonstrate that hybrid models consistently outperform classical models in final accuracy, achieving {99.38\% (MNIST), 41.69\% (CIFAR100), and 74.05\% (STL10) validation accuracy, compared to classical benchmarks of 98.21\%, 32.25\%, and 63.76\%, respectively. Notably, the hybrid advantage scales with dataset complexity, showing the most significant gains on CIFAR100 (+9.44\%) and STL10 (+10.29\%). Hybrid models also train 5--12× faster (e.g., 21.23s vs. 108.44s per epoch on MNIST) and use 6--32\% fewer parameters} while maintaining superior generalization to unseen test data.Adversarial robustness tests reveal that hybrid models are significantly more resilient on simpler datasets (e.g., 45.27\% robust accuracy on MNIST vs. 10.80\% for classical) but show comparable fragility on complex datasets like CIFAR100 ($\sim$1\% robustness for both). Resource efficiency analyses indicate that hybrid models consume less memory (4--5GB vs. 5--6GB for classical) and lower CPU utilization (9.5\% vs. 23.2\% on average).These results suggest that hybrid quantum-classical architectures offer compelling advantages in accuracy, training efficiency, and parameter scalability, particularly for complex vision tasks.
In recent years, machine learning has achieved remarkable success in tasks such as image classification and solving complex real-world problems. The achievements of deep neural networks (DNNs) have led to the widespread adoption of block-based architectures. However, as the demand for Explainable AI (XAI) grows, there is a need for models that provide greater transparency and interpretability. Our work addresses this need by exploring the potential of quantum-classical hybrid models. By integrating classical neural networks (ClaNNs) with quantum neural networks (QNNs), we aim to develop AI systems that are not only powerful but also interpretable. We propose a framework that combines parameterized quantum circuits (PQCs) with classical layers to create a hybrid block design for QNNs. By adding a linear layer after the PQCs, the flexibility of the block design is increased. This architecture enhances the adaptability of QNN design and facilitates the integration of XAI principles through the comparison of the loss function planes. Our training method, inspired by knowledge distillation, addresses existing challenges in PQC-based QNN designs, providing insights into the optimization landscape. We validate the effectiveness of our hybrid block through experiments on standard datasets, demonstrating its potential to achieve comparable accuracy to traditional DNNs while providing improved interpretability.
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 Quantum Machine Learning (QML) has emerged as a promising paradigm that leverages the principles of quantum mechanics—such as superposition and entanglement—to overcome the limitations of classical machine learning in handling complex, high-dimensional data. However, practical challenges such as limited coherence times of qubits and the integration of quantum circuits with classical architectures have hindered the full realization of QML’s potential. To address these challenges, this paper proposes a novel hybrid architecture, termed the Classical-Quantum Hybrid Convolutional Neural Network (CQHCNN), which integrates quantum-inspired layers into conventional deep learning frameworks. These quantum layers utilize quantum information processing to extract complex features, while the classical components perform feature extraction and classification. This paper evaluates the performance of our model on several benchmark datasets, including MNIST, Fashion-MNIST, CIFAR-10 and COVID-19 Radiography, demonstrating its ability to outperform some other Quantum Convolutional Neural Networks (QCNNs) in terms of many metrics. Experimental results indicate that the hybrid model achieves significant improvements in classification accuracy and computational efficiency, highlighting the potential of quantum-inspired techniques to enhance classical machine learning models.