Quantum Feature-Empowered Deep Classification for Fast Mangrove Mapping
- URL: http://arxiv.org/abs/2501.03360v1
- Date: Mon, 06 Jan 2025 19:59:14 GMT
- Title: Quantum Feature-Empowered Deep Classification for Fast Mangrove Mapping
- Authors: Chia-Hsiang Lin, Po-Wei Tang, Alfredo R. Huete,
- Abstract summary: mangrove mapping (MM) algorithm is an essential classification tool for environmental monitoring.
We show that quantum features provide radically new information for CNN to further upgrade the classification results.
The proposed quantum-empowered deep network (QEDNet) is very lightweight, so the improvement does come from the cooperation between CNN and QNN.
- Score: 1.8679829796354375
- License:
- Abstract: A mangrove mapping (MM) algorithm is an essential classification tool for environmental monitoring. The recent literature shows that compared with other index-based MM methods that treat pixels as spatially independent, convolutional neural networks (CNNs) are crucial for leveraging spatial continuity information, leading to improved classification performance. In this work, we go a step further to show that quantum features provide radically new information for CNN to further upgrade the classification results. Simply speaking, CNN computes affine-mapping features, while quantum neural network (QNN) offers unitary-computing features, thereby offering a fresh perspective in the final decision-making (classification). To address the challenging MM problem, we design an entangled spatial-spectral quantum feature extraction module. Notably, to ensure that the quantum features contribute genuinely novel information (unaffected by traditional CNN features), we design a separate network track consisting solely of quantum neurons with built-in interpretability. The extracted pure quantum information is then fused with traditional feature information to jointly make the final decision. The proposed quantum-empowered deep network (QEDNet) is very lightweight, so the improvement does come from the cooperation between CNN and QNN (rather than parameter augmentation). Extensive experiments will be conducted to demonstrate the superiority of QEDNet.
Related papers
- CTRQNets & LQNets: Continuous Time Recurrent and Liquid Quantum Neural Networks [76.53016529061821]
Liquid Quantum Neural Network (LQNet) and Continuous Time Recurrent Quantum Neural Network (CTRQNet) developed.
LQNet and CTRQNet achieve accuracy increases as high as 40% on CIFAR 10 through binary classification.
arXiv Detail & Related papers (2024-08-28T00:56:03Z) - Parallel Proportional Fusion of Spiking Quantum Neural Network for Optimizing Image Classification [10.069224006497162]
We introduce a novel architecture termed Parallel Proportional Fusion of Quantum and Spiking Neural Networks (PPF-QSNN)
The proposed PPF-QSNN outperforms both the existing spiking neural network and the serial quantum neural network across metrics such as accuracy, loss, and robustness.
This study lays the groundwork for the advancement and application of quantum advantage in artificial intelligent computations.
arXiv Detail & Related papers (2024-04-01T10:35:35Z) - QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional
Networks [124.7972093110732]
We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations.
To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections.
Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
arXiv Detail & Related papers (2022-11-09T21:43:16Z) - Quantum-inspired Complex Convolutional Neural Networks [17.65730040410185]
We improve the quantum-inspired neurons by exploiting the complex-valued weights which have richer representational capacity and better non-linearity.
We draw the models of quantum-inspired convolutional neural networks (QICNNs) capable of processing high-dimensional data.
The performance of classification accuracy of the five QICNNs are tested on the MNIST and CIFAR-10 datasets.
arXiv Detail & Related papers (2021-10-31T03:10:48Z) - QDCNN: Quantum Dilated Convolutional Neural Network [1.52292571922932]
We propose a novel hybrid quantum-classical algorithm called quantum dilated convolutional neural networks (QDCNNs)
Our method extends the concept of dilated convolution, which has been widely applied in modern deep learning algorithms, to the context of hybrid neural networks.
The proposed QDCNNs are able to capture larger context during the quantum convolution process while reducing the computational cost.
arXiv Detail & Related papers (2021-10-29T10:24:34Z) - Quantum convolutional neural network for classical data classification [0.8057006406834467]
We benchmark fully parameterized quantum convolutional neural networks (QCNNs) for classical data classification.
We propose a quantum neural network model inspired by CNN that only uses two-qubit interactions throughout the entire algorithm.
arXiv Detail & Related papers (2021-08-02T06:48:34Z) - The Mind's Eye: Visualizing Class-Agnostic Features of CNNs [92.39082696657874]
We propose an approach to visually interpret CNN features given a set of images by creating corresponding images that depict the most informative features of a specific layer.
Our method uses a dual-objective activation and distance loss, without requiring a generator network nor modifications to the original model.
arXiv Detail & Related papers (2021-01-29T07:46:39Z) - Branching Quantum Convolutional Neural Networks [0.0]
Small-scale quantum computers are already showing potential gains in learning tasks on large quantum and very large classical data sets.
We present a generalization of QCNN, the branching quantum convolutional neural network, or bQCNN, with substantially higher expressibility.
arXiv Detail & Related papers (2020-12-28T19:00:03Z) - Toward Trainability of Quantum Neural Networks [87.04438831673063]
Quantum Neural Networks (QNNs) have been proposed as generalizations of classical neural networks to achieve the quantum speed-up.
Serious bottlenecks exist for training QNNs due to the vanishing with gradient rate exponential to the input qubit number.
We show that QNNs with tree tensor and step controlled structures for the application of binary classification. Simulations show faster convergent rates and better accuracy compared to QNNs with random structures.
arXiv Detail & Related papers (2020-11-12T08:32:04Z) - Decentralizing Feature Extraction with Quantum Convolutional Neural
Network for Automatic Speech Recognition [101.69873988328808]
We build upon a quantum convolutional neural network (QCNN) composed of a quantum circuit encoder for feature extraction.
An input speech is first up-streamed to a quantum computing server to extract Mel-spectrogram.
The corresponding convolutional features are encoded using a quantum circuit algorithm with random parameters.
The encoded features are then down-streamed to the local RNN model for the final recognition.
arXiv Detail & Related papers (2020-10-26T03:36:01Z) - On the learnability of quantum neural networks [132.1981461292324]
We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme.
We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
arXiv Detail & Related papers (2020-07-24T06:34:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.