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

Shtwai Alsubai (), Abdullah Alqahtani, Adel Binbusayyis, Mohemmed Sha, Abdu Gumaei and Shuihua Wang
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Shtwai Alsubai: Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
Abdullah Alqahtani: Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
Adel Binbusayyis: Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
Mohemmed Sha: Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
Abdu Gumaei: Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
Shuihua Wang: Department of Mathematics, University of Leicester, Leicester LE1 7RH, UK

Mathematics, 2023, vol. 11, issue 11, 1-22

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.

Keywords: quantum computing; image classification; Modified National Institute of Standards and Technology; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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