Handwritten digit recognition in regional scripts, such as Devanagari, is crucial for multilingual document digitization, educational tools, and the preservation of cultural heritage. The script's complex structure and limited annotated datasets pose significant challenges to conventional models. This paper introduces the first hybrid quantum-classical architecture for Devanagari handwritten digit recognition, combining a convolutional neural network (CNN) for spatial feature extraction with a 10-qubit variational quantum circuit (VQC) for quantum-enhanced classification. Trained and evaluated on the Devanagari Handwritten Character Dataset (DHCD), the proposed model achieves a state-of-the-art test accuracy for quantum implementation of 99.80% and a test loss of 0.2893, with an average per-class F1-score of 0.9980. Compared to equivalent classical CNNs, our model demonstrates superior accuracy with significantly fewer parameters and enhanced robustness. By leveraging quantum principles such as superposition and entanglement, this work establishes a novel benchmark for regional script recognition, highlighting the promise of quantum machine learning (QML) in real-world, low-resource language settings.
Handwritten digit recognition in regional scripts, such as Devanagari, is crucial for multilingual document digitization, educational tools, and the preservation of cultural heritage. The script's complex structure and limited annotated datasets pose significant challenges to conventional models. This paper introduces the first hybrid quantum-classical architecture for Devanagari handwritten digit recognition, combining a convolutional neural network (CNN) for spatial feature extraction with a 10-qubit variational quantum circuit (VQC) for quantum-enhanced classification. Trained and evaluated on the Devanagari Handwritten Character Dataset (DHCD), the proposed model achieves a state-of-the-art test accuracy for quantum implementation of 99.80% and a test loss of 0.2893, with an average per-class F1-score of 0.9980. Compared to equivalent classical CNNs, our model demonstrates superior accuracy with significantly fewer parameters and enhanced robustness. By leveraging quantum principles such as superposition and entanglement, this work establishes a novel benchmark for regional script recognition, highlighting the promise of quantum machine learning (QML) in real-world, low-resource language settings.
Abstract Handwritten Bangla character recognition poses a significant computer vision challenge for over 250 million native speakers. The script's morphological complexity, which includes a large set of basic characters, numerals, modifiers, and intricate compound conjuncts, demands computationally robust models. While classical Convolutional Neural Networks (CNNs) have shown high accuracy, they are often demanding and struggle with subtle character variations. This paper fills a critical research gap by designing and evaluating, to our knowledge, the first Hybrid Quantum-Classical Convolutional Neural Network (HQCNN) for this task. We propose a novel architecture that integrates a quatum convolutional layer, implemented using Random Quantum Circuits (RQCs) simulated with PennyLane, as a high-dimensional feature extractor. To isolate the impact of the quantum layer, we conducted a rigorous comparative analysis of our HQCNN against a structurally identical classical CNN baseline across seven distinct experiments on four public datasets: NumtaDB, CMATERdb 3.1.2, Ekush, and BanglaLekha-Isolated. The HQCNN consistently outperformed the classical baseline in all seven tasks, achieving a peak accuracy of 99.45% on the Ekush numerical dataset. Notably, the most significant "quantum advantage" was observed in the classification of structurally complex compound characters (E6), where the HQCNN achieved 97.16% accuracy versus the baseline's 95.52% (a 1.64% improvement). Furthermore, the HQCNN demonstrated superior learning efficiency and stability. The quantum-derived features allowed the classical backbone to converge 27–43% faster (e.g., 162.78 vs. 223.37 minutes of classical backbone training time on the 84-class mixed dataset) and with a more stable validation loss. These results provide strong evidence that the quantum convolutional layer captures more expressive feature representations, demonstrating that the static RQC layer that the static RQC layer, by operating in a high-dimensional Hilbert space, provides a more expressive feature representation than a classical kernel, making the classification task easier for the classical backbone. While classical simulation of the quantum circuits adds significant computational overhead, our findings on faster convergence strongly suggest that an implementation on native quantum hardware would offer a substantial wall-clock speedup, presenting a viable and highly efficient paradigm for advancing OCR systems for Bangla and other complex Indic scripts.
Handwritten character recognition for Tamil characters is challenging because of the characters displaying high level of similarity between them and also due to the variations in different handwriting styles. Quantum CNN models have been explored recently in character recognition tasks especially when the amount of training data is limited. Quantum models have the ability to encode and process the information in higher dimensional spaces using quantum properties like superposition and entanglement. In this paper, a hybrid quantum-classical framework has been proposed which is used for classification of ten handwritten Tamil characters. This hybrid framework consists of a Scalable Quantum Convolutional Neural Network which does patch wise local feature extraction and a Bayesian-optimized Variational Quantum Circuit which is used as a global feature extractor and then final classification is done using a classical fully connected layer. This hybrid model achieves better classification accuracy for Tamil handwritten characters while reducing the total number of trainable parameters required to train the model.
Handwritten character recognition for Tamil characters is challenging because of the characters displaying high level of similarity between them and also due to the variations in different handwriting styles. Quantum CNN models have been explored recently in character recognition tasks especially when the amount of training data is limited. Quantum models have the ability to encode and process the information in higher dimensional spaces using quantum properties like superposition and entanglement. In this paper, a hybrid quantum-classical framework has been proposed which is used for classification of ten handwritten Tamil characters. This hybrid framework consists of a Scalable Quantum Convolutional Neural Network which does patch wise local feature extraction and a Bayesian-optimized Variational Quantum Circuit which is used as a global feature extractor and then final classification is done using a classical fully connected layer. This hybrid model achieves better classification accuracy for Tamil handwritten characters while reducing the total number of trainable parameters required to train the model.
Accurate and computationally efficient classification of brain tumors from MRI scans is critical for deployment in resource-constrained clinical settings, where conventional deep CNN architectures-despite achieving strong accuracy-often require large parameter counts that limit scalability. This work proposes a hybrid quantum-classical deep learning framework that combines a classical convolutional feature extractor with a variational quantum circuit (VQC) for multi-class tumor classification. Spatial features extracted by the CNN backbone will be dimensionality-reduced and encoded into quantum states using angle encoding; a parameterized quantum circuit will then process these features in a higher-dimensional Hilbert space, leveraging superposition and entanglement to capture non-linear feature correlations. Measurement outputs from the quantum layer will be passed to a classical fully connected layer for classification into glioma, meningioma, pituitary tumor, and no-tumor categories. The proposed framework will be evaluated on a publicly available benchmark MRI dataset and benchmarked against state-of-the-art classical CNN architectures in terms of accuracy, precision, recall, F1-score, and parameter efficiency. This study aims to demonstrate that quantum-enhanced layers can achieve competitive classification performance while substantially reducing trainable parameters, offering a practical direction for efficient, quantum-assisted medical image analysis.
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.
Handwritten digit recognition (HWR) is a technology designed to enable mobile devices and computers to interpret handwritten inputs, encompassing both online and offline sources, such as images, scanned documents and digitised content. Various sectors, including insurance, banking, retail, healthcare and logistics, have increasingly adopted HWR. Despite the use of methods such as optical characteristic recognition (OCR), connectionist temporal classification (CTC) and machine learning (ML), their accuracy is compromised by interpretational limitations and significant error probabilities, leading to unreliable results. In order to address these shortcomings, this study advocates the collaborative use of deep ResNet and Inception models, combined with a variational quantum circuit (VQC) for HWR. The investigation utilises the MNIST dataset, which comprises 60,000 training set images and 10,000 testing set images of handwritten digits, each with a size of 28 × 28 pixels. Two selected samples, namely, Sample-1 (1, 0) and Sample-2 (3, 6), are drawn from this dataset. The hybrid approach of ResNet-Inception, which integrates a quantum encoding layer and VQC, is designed to enhance computational precision and digit recognition. ResNet improves accuracy through residual blocks with skip connections that mitigate vanishing and exploding gradients, while VQC enhances efficiency for high-velocity, large-scale datasets. The framework is evaluated using accuracy, precision, recall, and F1-score.