A Quantum Computing-Based Accelerated Model for Image Classification Using a Parallel Pipeline Encoded Inception Module

Abstract

Image classification is typically a research area that trains an algorithm for accurately identifying subjects in images that have never been seen before. Training a model to recognize images within a dataset is significant as image classification generally has several applications in medicine, face detection, image reconstruction, etc. In spite of such applications, the main difficulty in this area involves the computation in the classification process, which is vast, leading to slow speed of classification. Moreover, as conventional image classification approaches have fallen short in terms of attaining high accuracy, an optimal model is needed. To resolve this, quantum computing has been developed. Due to their parallel computing ability, quantum-based algorithms could accomplish the classification of vast amounts of image data. This has theoretically confirmed the feasibility and advantages of incorporating a quantum computing-based system with traditional image classification methodologies. Considering this, the present study quantizes the layers of the proposed parallel encoded Inception module to improvise the network performance. This study exposes the flexibility of DL (deep learning)-based quantum state computational methodologies for missing computations by creating a pipeline for denoising, state estimation, and imputation. Furthermore, controlled parameterized rotations are regarded for entanglement, a vital component in quantum perceptron structure. The proposed approach not only possesses the unique features of quantum mechanics, but it also maintains the weight sharing of the kernel. Finally, the MNIST (Modified National Institute of Standards and Technology) and Fashion MNIST image classification outcomes are attained by measuring the quantum state. Overall performance is assessed to prove its effectiveness in image classification.