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Quantum Information Processing

Quantum-enhanced deep neural network architecture for image scene classification

Avinash Chalumuri, Raghavendra Kune, Suresh Kannan, B. S. B. S. Manoj

Published Nov 1, 2021• 34 citations
Publisher
DOI
10.1007/s11128-021-03314-7

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This paper presents algorithms, simulations, and results on the use of optical and Synthetic Aperture Radar (SAR) quantum image fusion and machine learning algorithms for radar and remote sensing applications. We propose novel quantum image fusion techniques that extract meaningful features for identifying and classifying scenes obtained from the BigEarthNet dataset. Specifically, this work focuses on multi-label scene classification using quantum image fusion of C-band SAR and optical imagery from the BigEarthNet dataset. The proposed fusion techniques enhance and sharpen structural and textural details that are not readily observable in raw SAR imagery, thereby improving downstream classification performance. To enable quantum image fusion, we design new quantum circuits that employ unitary and Pauli gates for phase encoding of pixel intensity values. These circuits allow optical and SAR image information to be encoded and fused within a quantum processing framework. Three fusion strategies are investigated, resulting in three distinct quantum image fusion models. Comparative evaluation of these models demonstrates that pixel-averaging–based quantum fusion yields improved classification accuracy relative to alternative fusion approaches. The fused images are subsequently processed using deep classical neural networks to perform scene classification. Our neural network study begins by customizing the Visual Geometry Group 16 (VGG16) architecture for multi-label remote sensing classification tasks. In addition, we examine the use of a Residual Network 50 (ResNet-50) architecture and provide comparative performance analysis between the two models. The effects of quantum noise on the fusion and classification pipeline are also studied to assess robustness under realistic quantum processing conditions. To evaluate classification performance, we employ standard metrics such as F1 and F2 scores, and additionally introduce false discovery rate and false negative rate metrics to provide a more comprehensive assessment of classification accuracy, particularly in multi-label settings. Overall, the results demonstrate the effectiveness of optical and SAR quantum image fusion in enhancing scene classification performance and highlight the potential of integrating quantum image processing with deep learning for advanced remote sensing applications.
We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantum circuit, thereby propose a novel hybrid quantum deep neural network (HQDNN) used for image classification. After bilinear interpolation reduces the original image to a suitable size, an improved novel enhanced quantum representation (INEQR) is used to encode it into quantum states as the input of the HQDNN. Multi-layer parameterized quantum circuits are used as the main structure to implement feature extraction and classification. The output results of parameterized quantum circuits are converted into classical data through quantum measurements and then optimized on a classical computer. To verify the performance of the HQDNN, we conduct binary classification and three classification experiments on the MNIST (Modified National Institute of Standards and Technology) data set. In the first binary classification, the accuracy of 0 and 4 exceeds 98%. Then we compare the performance of three classification with other algorithms, the results on two datasets show that the classification accuracy is higher than that of quantum deep neural network and general quantum convolutional neural network.
Image classification is a fundamental task in deep learning, and recent advances in quantum computing have generated significant interest in quantum neural networks. Traditionally, Convolutional Neural Networks (CNNs) are employed to extract image features, while Multilayer Perceptrons (MLPs) handle decision making. However, parameterized quantum circuits offer the potential to capture complex image features and define sophisticated decision boundaries. In this paper, we present a novel Hybrid Quantum–Classical Neural Network (H-QNN) for image classification, and demonstrate its effectiveness using the MNIST dataset. Our model combines quantum computing with classical supervised learning to enhance classification accuracy and computational efficiency. In this study, we detail the architecture of the H-QNN, emphasizing its capability in feature learning and image classification. Experimental results demonstrate that the proposed H-QNN model outperforms conventional deep learning methods in various training scenarios, showcasing its effectiveness in high-dimensional image classification tasks. Additionally, we explore the broader applicability of hybrid quantum–classical approaches in other domains. Our findings contribute to the growing body of work in quantum machine learning, and underscore the potential of quantum-enhanced models for image recognition and classification.