This paper examines the applicability of quantum machine learning methods to optical character recognition (OCR). The paper's abstract is given below: This paper builds on previous research demonstrating the effectiveness of quantum machine learning methods on limited or restricted data sets. A simulated quantum circuit is used to determine the validity of quantum machine learning methods for optical character recognition (OCR). OCR models are typically implemented using Convolutional Neural Networks (CNN), which can require large data sets to be trained effectively. A demonstrated ability to produce accurate OCR results with quantum machine learning would enable OCR-like models to be used when only limited data is available. This paper compares the effectiveness of quantum machine learning and classical CNN models on restricted samples of the MNIST data set, implemented in Python using IBM's Qiskit library and TensorFlow. Due to better generalization and feature space expressivity, the quantum model outperformed the CNN by an average of 6.33% on training data sizes of 10 samples to 300 samples. Both predictions had equal accuracy at $N=300$ samples, suggesting the CNN will either overtake or remain equal to the accuracy rate of the QSVC for larger data sample sizes. The space used by each model was explicitly measured, with the QSVC utilizing approximately 86% less memory space than the CNN. Upon evaluation of kernel heatmaps and eigenvalue spectra, performance improvements were minimal past $N>200$ samples for training data. The time complexity of this QSVC model has a minimum of $\Omega(N^2)$, so the time cost outweighs any performance benefit. The ability to run OCR models with reliable accuracy for small data sets creates possibilities for on-device OCR models, launching new opportunities for highly specialized edge models. This will evaluated in future research.
This paper examines the applicability of quantum machine learning methods to optical character recognition (OCR). The paper's abstract is given below: This paper builds on previous research demonstrating the effectiveness of quantum machine learning methods on limited or restricted data sets. A simulated quantum circuit is used to determine the validity of quantum machine learning methods for optical character recognition (OCR). OCR models are typically implemented using Convolutional Neural Networks (CNN), which can require large data sets to be trained effectively. A demonstrated ability to produce accurate OCR results with quantum machine learning would enable OCR-like models to be used when only limited data is available. This paper compares the effectiveness of quantum machine learning and classical CNN models on restricted samples of the MNIST data set, implemented in Python using IBM's Qiskit library and TensorFlow. Due to better generalization and feature space expressivity, the quantum model outperformed the CNN by an average of 6.33% on training data sizes of 10 samples to 300 samples. Both predictions had equal accuracy at N=300 samples, suggesting the CNN will either overtake or remain equal to the accuracy rate of the QSVC for larger data sample sizes. The space used by each model was explicitly measured, with the QSVC utilizing approximately 86% less memory space than the CNN. Upon evaluation of kernel heatmaps and eigenvalue spectra, performance improvements were minimal past N>200 samples for training data. The time complexity of this QSVC model has a minimum of Ω(N2), so the time cost outweighs any performance benefit. The ability to run OCR models with reliable accuracy for small data sets creates possibilities for on-device OCR models, launching new opportunities for highly specialized edge models. This will evaluated in future research.
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.
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 rapid growth of computer vision and increasingly complex image recognition tasks has exposed fundamental computational limitations of classical machine learning models, motivating the exploration of quantum computing as an emerging new paradigm. This paper presents a comprehensive benchmarking study of classical and quantum machine learning models for image recognition on the MNIST handwritten digit dataset, evaluating both traditional models, a Classical Support Vector Machine (CSVM) and a Quantum Support Vector Machine (QSVM), and deep neural network models, a Classical Convolutional Neural Network (CCNN) and a Quantum Convolutional Neural Network (QCNN), across four performance dimensions: classification accuracy, computational runtime, parameter count, and memory requirements. Experiments are conducted as functions of both feature dimensionality and sample size, and across CPU and GPU execution environments, providing a controlled, multidimensional comparison to address gaps in prior work. For the SVM-based models, QSVM consistently outperforms CSVM in accuracy, reaching ∼ 0.90 versus ∼ 0.85 at 1,000 samples, with a higher computational cost. A feature count of 10 qubits and a sample size in the range of 200 -- 500 emerge as practical operating points that balance accuracy and runtime. For the neural network models, CCNN and QCNN achieve comparable classification accuracy, both exceeding 0.96 at 64 features and 60,000 samples, yet QCNN offers superior parameter and memory efficiency at higher feature counts, while incurring higher runtime. Across both model families, quantum models consistently outperform classical models by greater margins in accuracy as feature dimensionality or sample size increases.
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.
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.
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%.