Inspired by the quantum density matrix, we argue that the quantum density matrix can enhance the image feature information and the relationship between the features for the classical image classification. Specifically, (i) we combine density matrices and CNN to design a new mechanism; (ii) we ...
The results show that the proposed QCNN with the proposed feature extraction methods outperformed the classic CNN in terms of recognition accuracy. It is interesting to find that image bit-plane slicing has a similar internal mechanism to that of the Ising phase transition. This observation ...
mage classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural network (CNN) to extract features from the i...
Image classification is an important application for deep learning. With the advent of quantum technology, quantum neural networks (QNNs) have become the focus of research. Traditional deep learning-based image classification involves using a convolutional neural network (CNN) to extract features from ...
Quantum-classical hybrid convolutional neural network for classical image classification quantum-computingimage-classificationconvolutional-neural-networksquantum-algorithmsquantum-machine-learning UpdatedMar 4, 2023 Python PennyLaneAI/pennylane-lightning Star101 ...
2.3. Convolutional Neural Networks (CNNs) CNNs are one of the most widely used types of deep neural networks for image classification [17]. It consists of convolutional, pooling, and fully connected layers. The convolutional layer applies multiple filters to the input to create feature maps. Th...
For humans, the difficulty of analyzing quantum optical experiments goes far beyond that of other deep learning problems like, e.g., image classification. The network performs well even on unseen data beyond the training distribution, proving its extrapolation capabilities. This paves the way for ...
The feature map ϕCNN for the kernel kCNN is a nonlinear mapping that extracts all local properties of x35. In quantum mechanics, similarly a kernel function can be defined using the native geometry of the quantum state space \(\left|x\right\rangle \). For example, we can define the ...
Based on this link, we discuss two approaches for building a quantum model for classification. In the first approach, the quantum device estimates inner products of quantum states to compute a classically intractable kernel. This kernel can be fed into any classical kernel method such as a ...
We propose approaches to overcome two hurdles for high resolution image classification on a quantum processing unit (QPU): the required number and the binary nature of the model states. With this novel method we successfully transfer a pretrained convolutional neural network to the QPU. By taking ...