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Hybrid quantum-classical 3D object detection using multi-channel quantum convolutional neural network

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Abstract

3D object detection has recently shown remarkable progress in the computer vision field, enabling advanced understanding of the surrounding environment by identifying objects’ shape, position, and depth. However, processing high-dimensional data using classical convolutional neural networks (CNNs) introduces considerable computational challenges. This paper proposes a novel hybrid quantum-classical 3D object detection (HQCOD) approach, integrating a multi-channel quantum convolutional neural network (MC-QCNN) to significantly reduce computational complexity by leveraging quantum computing advantages. Additionally, knowledge distillation (KD) is applied to enhance detection accuracy and model robustness. Experimental evaluations using the Karlsruhe institute of technology and Toyota technological institute (KITTI) dataset validate the scalability and effectiveness, demonstrating the HQCOD as a practical quantum-assisted solution. This study establishes a foundation for extending quantum-enhanced 3D computer vision methods into real-world applications.

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No datasets were generated or analyz0ed during the current study.

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Funding

This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government [MSIT (Ministry of Science and ICT (Information and Communications Technology))] (RS-2024-00439803, SW Star Lab) for Quantum AI Empowered Second-Life Platform Technology.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Korea University, Seoul, 02841, Republic of Korea

    Emily Jimin Roh, Joo Yong Shim & Joongheon Kim

  2. Division of Computer Science, Sookmyung Women’s University, Seoul, 04310, Republic of Korea

    Soohyun Park

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  1. Emily Jimin Roh
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  2. Joo Yong Shim
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Contributions

Emily Jimin Roh is a main investigator for this work (including idea investigation, writing, and performance evaluation). Joo Yong Shim and Soohyun Park are for the discussions of the idea and proof-reading. Joongheon Kim is a principal investigator for this research and supervision for the entire process.

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Correspondence to Joo Yong Shim, Joongheon Kim or Soohyun Park.

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Roh, E.J., Shim, J.Y., Kim, J. et al. Hybrid quantum-classical 3D object detection using multi-channel quantum convolutional neural network. J Supercomput 81, 455 (2025). https://doi.org/10.1007/s11227-025-06968-7

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