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دسته بندی:
محاسبات کوانتومی - Quantum-Computing
سال انتشار:
2022
عنوان انگلیسی مقاله:
Quantum–Classical Image Processing for Scene Classification
ترجمه فارسی عنوان مقاله:
پردازش تصویر کوانتومی کلاسیک برای طبقه بندی صحنه
منبع:
ieee - ieee Sensors Letters;2022;6;6;10:1109/LSENS:2022:3173253
نویسنده:
Avinash Chalumuri; Raghavendra Kune; S. Kannan; B. S. Manoj
چکیده انگلیسی:
Deep-learning-based convolutional neural network (CNN) models are prominent in processing and analyzing
sensor signal data, such as images for classification. Data augmentation is a powerful technique used in training
such models to avoid overfitting and to improve accuracy. This letter proposes a data augmentation technique using
a quantum circuit for image data. The proposed quantum circuit is suitable to implement on real hardware provided by
the IBM Quantum Experience platform. In comparison with other classical data augmentation techniques, the proposed
technique increased the prediction accuracy of the CNN from 68.65 to 76.03%. However, CNN models for image
classification use many parameters during the training process. Quantum computers can efficiently handle large-scale
data inputs using qubits for information processing. Hence, we also propose a hybrid quantum–classical convolutional
neural network model (HQCNN) for scene classification. The proposed model uses a combination of CNN layers and
quantum layers to process images. The proposed HQCNN reduces parameters used for training due to the use of quantum
layers in the model. Our experimental results show that the proposed HQCNN can classify the scenes in the UC Merced
land-use dataset with an accuracy of 85.28% compared to the other models.
Index Terms—Sensor signal processing | hybrid model | quantum–classical computing | scene classification | sensor signal processing.
قیمت: رایگان
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