Edge Detection Quantumized: A Novel Quantum Algorithm For Image Processing

Quantum image processing is a research field that explores the use of quantum computing and algorithms for image processing tasks such as image encoding and edge detection. Although classical edge detection algorithms perform reasonably well and are quite efficient, they become outright slower when it comes to large datasets with high-resolution images. Quantum computing promises to deliver a significant performance boost and breakthroughs in various sectors. Quantum Hadamard Edge Detection (QHED) algorithm, for example, works at constant time complexity, and thus detects edges much faster than any classical algorithm. However, the original QHED algorithm is designed for Quantum Probability Image Encoding (QPIE) and mainly works for binary images. This paper presents a novel protocol by combining the Flexible Representation of Quantum Images (FRQI) encoding and a modified QHED algorithm. An improved edge outline method has been proposed in this work resulting in a better object outline output and more accurate edge detection than the traditional QHED algorithm.

Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation

Very recently, studies have shown that quantum neural networks surpass classical neural networks in tasks like image classification when a similar number of learnable parameters are used. However, the development and optimization of quantum models are currently hindered by issues such as qubit instability and limited qubit availability, leading to error-prone systems with weak performance. In contrast, classical models can exhibit high-performance owing to substantial resource availability. As a result, more studies have been focusing on hybrid classical-quantum integration. A line of research particularly focuses on transfer learning through classical-quantum integration or quantum-quantum approaches. Unlike previous studies, this paper introduces a new method to transfer knowledge from classical to quantum neural networks using knowledge distillation, effectively bridging the gap between classical machine learning and emergent quantum computing techniques. We adapt classical convolutional neural network (CNN) architectures like LeNet and AlexNet to serve as teacher networks, facilitating the training of student quantum models by sending supervisory signals during backpropagation through KL-divergence. The approach yields significant performance improvements for the quantum models by solely depending on classical CNNs, with quantum models achieving an average accuracy improvement of 0.80% on the MNIST dataset and 5.40% on the more complex Fashion MNIST dataset. Applying this technique eliminates the cumbersome training of huge quantum models for transfer learning in resource-constrained settings and enables re-using existing pre-trained classical models to improve performance.Thus, this study paves the way for future research in quantum machine learning (QML) by positioning knowledge distillation as a core technique for advancing QML applications.

Improving Malaria Parasite Detection from Red Blood Cell using Deep Convolutional Neural Networks

Malaria is a female anopheles mosquito-bite inflicted life-threatening disease which is considered endemic in many parts of the world. This article focuses on improving malaria detection from patches segmented from microscopic images of red blood cell smears by introducing a deep convolutional neural network. Compared to the traditional methods that use tedious hand engineering feature extraction, the proposed method uses deep learning in an end-to-end arrangement that performs both feature extraction and classification directly from the raw segmented patches of the red blood smears. The dataset used in this study was taken from National Institute of Health named NIH Malaria Dataset. The evaluation metric accuracy and loss along with 5-fold cross validation was used to compare and select the best performing architecture. To maximize the performance, existing standard pre-processing techniques from the literature has also been experimented. In addition, several other complex architectures have been implemented and tested to pick the best performing model. A holdout test has also been conducted to verify how well the proposed model generalizes on unseen data. Our best model achieves an accuracy of almost 97.77%.