Image classification is the process in which a computer can perform analysis on an image and identify the class in which that particular image falls under. In conventional classification models (Convolutional Neural Networks or CNN), data collection and processing takes a large amount of time in days or even weeks. CNN models also have less scalability and require a lot of resources. To overcome this problem, quantum computing can be used not just for enhancing the scalability but it also provides exponential speedups because they have the ability to perform massively parallel computations. The problem with the CNNs is that they need large numbers of parameters and run into an overfitting problem with small datasets. This work is an implementation of Quanvolutional Neural Networks (QNN), which is a quantum-hybrid model, to recognise English characters using EMNIST letters dataset for training. The results of this model are then compared to a CNN model in terms of accuracy and loss. The results show that the use of a quantum layer can significantly increase the accuracy as well as decrease the training time of a classification model.
Image classification is the process in which a computer can perform analysis on an image and identify the class in which that particular image falls under. In conventional classification models (Convolutional Neural Networks or CNN), data collection and processing takes a large amount of time in days or even weeks. CNN models also have less scalability and require a lot of resources. To overcome this problem, quantum computing can be used not just for enhancing the scalability but it also provides exponential speedups because they have the ability to perform massively parallel computations. The problem with the CNNs is that they need large numbers of parameters and run into an overfitting problem with small datasets. This work is an implementation of Quanvolutional Neural Networks (QNN), which is a quantum-hybrid model, to recognise English characters using EMNIST letters dataset for training. The results of this model are then compared to a CNN model in terms of accuracy and loss. The results show that the use of a quantum layer can significantly increase the accuracy as well as decrease the training time of a classification model.
In the field of image processing, a well-known model is the Convolutional Neural Network, or CNN. The unique benefit that sets this model apart is its exceptional ability to use the correlation information included in the data. Even with their amazing accomplishment, conventional CNNs could have trouble improving further in terms of generalization, accuracy, and computing economy. However, it could be challenging to train CNN correctly and process information quickly if the model or data dimensions are too large. This is since it will cause the data processing to lag. The Quantum Convolutional Neural Network, or QCNN for short, is a novel proposed quantum solution that might either enhance the functionality of an existing learning model or solve a problem requiring the combination of quantum computing with CNN. To highlight the flexibility and versatility of quantum circuits in improving feature extraction capabilities, this paper compares deep quantum circuit architecture designed for image-based tasks with classical Convolutional Neural Networks (CNNs) and a novel quantum circuit architecture. The covidx-cxr4 dataset was used to train quantum-CNN models, and their results were compared against those of other models. The results show that when paired with innovative feature extraction methods, the suggested deep Quantum Convolutional Neural Network (QCNN) outperformed the conventional CNN in terms of processing speed and recognition accuracy. Even though it required more processing time, QCNN outperformed CNN in terms of recognition accuracy. When training on the covidx-cxr4 dataset, this dominance becomes much more apparent, demonstrating how deeper quantum computing has the potential to completely transform image classification problems.
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.
The recognition of handwritten digits holds a significant place in the field of information processing. Recognizing such characters accurately from images is a complex task because of the vast differences in people's writing styles. Furthermore, the presence of various image artifacts such as blurring, intensity variations, and noise adds to the complexity of this process. The existing algorithm, convolution neural network (CNN) is one of the prominent algorithms in deep learning to handle the above problems. But there is a difficulty in handling input data that differs significantly from the training data, leading to decreased accuracy and performance. In this work, a method is proposed to overcome the aforementioned limitations by incorporating a quantum convolutional neural network algorithm (QCNN). QCNN is capable of performing more complex operations than classical CNNs. It can achieve higher levels of accuracy than classical CNNs, especially when working with noisy or incomplete data. It has the potential to scale more efficiently and effectively than classical CNNs, making them better suited for large-scale applications. The effectiveness of the proposed model is demonstrated on the modified national institute of standards and technology (MNIST) dataset and achieved an average accuracy of 91.08%.
This paper presents a hybrid Quantum Convolutional Neural Network (QCNN) designed for handwritten character classification using the EMNIST dataset. The model combines quantum convolution and pooling circuits with classical neural network layers. Various ansatz and pooling strategies were explored, with the Conv10 and PoolG combination achieving the highest accuracy of 81.38 percent. Several quantum data encoding methods were tested, where angle encoding on the X-axis outperformed others. Additionally, optimization methods were compared, and the Adam optimizer showed the best performance in terms of convergence and stability. Although the model performs well overall, it encounters difficulties when distinguishing visually similar characters such as “0” and “0.” The results support the feasibility of applying hybrid QCNN architectures to image classification tasks and suggest future improvements in data representation and circuit design.
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.
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.