Hybrid Quantum-Classical Convolutional Neural Network Model for Image Classification

Abstract

Image classification plays an important role in remote sensing. Earth observation (EO) has inevitably arrived in the big data era, but the high requirement on computation power has already become a bottleneck for analyzing large amounts of remote sensing data with sophisticated machine learning models. Exploiting quantum computing might contribute to a solution to tackle this challenge by leveraging quantum properties. This article introduces a hybrid quantum-classical convolutional neural network (QC-CNN) that applies quantum computing to effectively extract high-level critical features from EO data for classification purposes. Besides that, the adoption of the amplitude encoding technique reduces the required quantum bit resources. The complexity analysis indicates that the proposed model can accelerate the convolutional operation in comparison with its classical counterpart. The model's performance is evaluated with different EO benchmarks, including Overhead-MNIST, So2Sat LCZ42, PatternNet, RSI-CB256, and NaSC-TG2, through the TensorFlow Quantum platform, and it can achieve better performance than its classical counterpart and have higher generalizability, which verifies the validity of the QC-CNN model on EO data classification tasks.

Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment

“…They can be embedded in qubits without the constraint of their neighbors, making processing less resource-intensive [43]. For instance, one QML model known as a quantum convolutional neural network (QCNN) requires approximately 4, 000 quantum gates only to embed the element R 64×64×12 in the Eurosat dataset and roughly 60, 000 quantum gates for embedding the multispectral image R 300×290×3 illustrated in Figure 3 in the input qubits [59]. Hence, multispectral images are not viable for deploying QCNNs on today's quantum machines, even on future quantum machines.…”

A quantum synthetic aperture radar image denoising algorithm based on grayscale morphology

“…Based on quantum image representation models, many quantum image processing algorithms have been proposed. Such as geometric transformation of quantum image, 10 , 11 quantum image steganography based on least significant bit (LSB), 12 feature extraction of quantum image, 8 quantum image scaling, 13 quantum image matching, 14 quantum image edge detection, 15 , 16 , 17 , 18 , 19 , 20 quantum image segmentation, 21 , 22 , 23 , 24 , 25 , 26 quantum image filtering, 27 , 28 , 29 , 30 quantum image recognition and classification, 31 , 32 , 33 , 34 etc.…”

Design of a novel hybrid quantum deep neural network in INEQR images classification

“…A hybrid quantum-classical convolutional neural network was designed in Ref. [15], to conduct experiments on different earth observation benchmarks, proving that it is more general and has faster convolution operations than the classical counterpart. Additionally, in Ref.…”