Quantum Machine Learning: Towards Hybrid Quantum-Classical Vision Models

Syed Muhammad Abuzar Rizvi, Usama Inam Paracha, Uman Khalid, Kyesan Lee () and Hyundong Shin ()
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Syed Muhammad Abuzar Rizvi: Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Republic of Korea
Usama Inam Paracha: Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Republic of Korea
Uman Khalid: Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Republic of Korea
Kyesan Lee: Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Republic of Korea
Hyundong Shin: Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Republic of Korea

Mathematics, 2025, vol. 13, issue 16, 1-14

Abstract: The emergence of deep vision models such as convolutional neural networks and vision transformers has revolutionized computer vision, enabling significant advancements in image classification, object detection, and segmentation. In parallel, the rapid development of quantum computing has spurred interest in quantum machine learning (QML), which integrates the strengths of quantum computation with the representational power of deep learning. In QML, parameterized quantum circuits offer the potential to capture complex image features, define complex decision boundaries, and provide other computational advantages. This paper investigates hybrid quantum-classical vision architectures, with a focus on hybrid quantum-classical convolutional neural networks and hybrid quantum-classical vision transformers. These hybrid models explore both quantum pre-processing and post-processing of data, respectively, where quantum circuits are strategically integrated into the data pipeline to enhance model performance. Our results suggest that these hybrid models can enhance accuracy and computational efficiency in vision-related tasks, even with the constraints of current noisy intermediate-scale quantum devices.

Keywords: convolutional neural network; image classification; neural networks; quantum computing; vision transformer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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