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Quantum-enhanced deep neural network architecture for image scene classification

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Abstract

Deep learning algorithms gained prominence in analyzing images for real-time applications such as detection of objects, segmentation of instances, semantic segmentation, and classification of image scenes. However, deep learning models for image classification, such as convolutional neural networks, require extensive computational facilities. Also, training such models with multiple layers becomes complex as many trainable parameters are to be optimized. Quantum computing emerged as a research area to handle complex problems using quantum-mechanical properties for computation on a quantum computer. In this work, we primarily focus on designing a hybrid quantum-classical deep learning model for image scene classification. We propose a novel hybrid architecture that uses quantum computation for feature extraction and classical computation for scene classification. In the hybrid architecture, we use quantum measurement-based features to obtain the quantum representations of images. The obtained quantum representations of images are used to train and build a classical deep learning model for image scene classification. Our experiments performed on ibm_santiago quantum computer show that the proposed model is suitable for implementation on noisy intermediate scaled quantum computers. Our experimental results show that the proposed model can classify data efficiently using trainable parameters \(\approx \) 27–30% less than the state-of-the-art models on satellite image datasets. Hence, the complexity of training the deep learning models reduces as the number of parameters to be optimized reduces. Using the proposed architecture, the deep learning model can classify data with an overall accuracy of 95.89%, 86.13%, and 79.32% on UC Merced Land-Use, AID, and NWPU-RESISC45 datasets, respectively, for image scene classification.

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Acknowledgements

This work is funded in parts by IIT Palakkad Technology IHub Foundation Doctoral Fellowship IPTIF/HRD/DF/032.

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Authors and Affiliations

  1. Indian Institute of Space Science and Technology, Thiruvananthapuram, India

    Avinash Chalumuri, S. Kannan & B. S. Manoj

  2. Advanced Data Processing Research Institute, Secunderabad, India

    Raghavendra Kune

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  1. Avinash Chalumuri
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Correspondence to B. S. Manoj.

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Chalumuri, A., Kune, R., Kannan, S. et al. Quantum-enhanced deep neural network architecture for image scene classification. Quantum Inf Process 20, 381 (2021). https://doi.org/10.1007/s11128-021-03314-7

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