Quantum science just winning the 2022 Nobel Prize in Physics must lead future development of remote sensing technologies. However, given the very limited number of entangled quantum bits (qubits) even in the most advanced quantum computers, is processing remotely sensed hyperspectral image (featured by its large data volume) using quantum computer technically feasible? Even if the quantum image state can be well processed to the quantum state of the target image (QSTI), it cannot be perfectly retrieved/output as the QSTI will collapse to some eigenstate once it is measured. Owing to these challenges, current quantum image processing technologies can only achieve classification-level applications requiring just a few output qubits. We design a hyperspectral quantum deep network (HyperQUEEN) to encode the hyperspectral information using very few qubits, as well as to learn the mapping from some measuring statistics (associated with the collapsed-QSTI) to the target image (instead of directly retrieving the unobservable QSTI), thereby solving the challenges. HyperQUEEN is the first quantum architecture that makes a breakthrough to blindly reconstruct NASA’s damaged hyperspectral images, which means a lot for the upcoming space era. As the immature quantum facility nowadays does not yet allow us to fully exhibit its high potential, we are not aiming at developing state-of-the-art methods, but are demonstrating the feasibility of quantum hyperspectral remote sensing. Mathematical analysis guiding our design toward the low-rank quantum deep network, together with comprehensive experiments, will also be reported.
Quantum science just winning the 2022 Nobel Prize in Physics must lead future development of remote sensing technologies. However, given the very limited number of entangled quantum bits (qubits) even in the most advanced quantum computers, is processing remotely sensed hyperspectral image (featured by its large data volume) using quantum computer technically feasible? Even if the quantum image state can be well processed to the quantum state of the target image (QSTI), it cannot be perfectly retrieved/output as the QSTI will collapse to some eigenstate once it is measured. Owing to these challenges, current quantum image processing technologies can only achieve classification-level applications requiring just a few output qubits. We design a hyperspectral quantum deep network (HyperQUEEN) to encode the hyperspectral information using very few qubits, as well as to learn the mapping from some measuring statistics (associated with the collapsed-QSTI) to the target image (instead of directly retrieving the unobservable QSTI), thereby solving the challenges. HyperQUEEN is the first quantum architecture that makes a breakthrough to blindly reconstruct NASA’s damaged hyperspectral images, which means a lot for the upcoming space era. As the immature quantum facility nowadays does not yet allow us to fully exhibit its high potential, we are not aiming at developing state-of-the-art methods, but are demonstrating the feasibility of quantum hyperspectral remote sensing. Mathematical analysis guiding our design toward the low-rank quantum deep network, together with comprehensive experiments, will also be reported.
Considering that there are many NP-hard problems in remote sensing (e.g., Craig simplex computation in hyperspectral un-mixing), it is natural to introduce quantum computing into space remote sensing. However, current quantum computers have some practical limitations, requiring complementary techniques to support quantum computing. Specifically, we are introducing artificial intelligence (AI) to solve the quantum collapse effect and the phenomenon of insufficient quantum bits (qubits). We thereby propose the hyperspectral quantum deep network (HyperQUEEN) for satellite remote sensing. HyperQUEEN is the first quantum technology that successfully outputs a complete hyperspectral image, given the very limited qubit resources. Existing quantum image processing methods can only achieve classification-level tasks or simple geometry transforms, and are far from being applicable to advanced satellite missions like restoration of damaged hyperspectral images, which HyperQUEEN has successfully achieved for the first time. Remarkable computational efficiency and restoration performances achieved by the radically new quantum AI system—HyperQUEEN—will be reported.
Hyperspectral images (HSIs) have drawn considerable attention over the last decade thanks to their high spectral resolution. However, as hardware limitations often cause the low spatial resolution of HSIs compared to traditional RGB and multispectral images, the hyperspectral image super-resolution (HSR) problem becomes crucial in facilitating the subsequent material identification applications. Recently, we developed hyperspectral quantum deep network (HyperQUEEN) for satellite data restoration, thereby proving that quantum features do play some role in developing high-performance inverse imaging algorithms. Before HyperQUEEN, which elegantly performs the hyperspectral matrix/tensor factorization via the so-called low-rank module, other existing quantum image processing tasks can only achieve basic geometry transforms or simple classification-level tasks. In this work, we go a step further by investigating the quantum implementation of multi-scale feature fusion (MF) transformer and applying the HyperQUEEN-MF to solve the challenging inverse problem known as single HSR (SHSR). By learning multi-scale quantum features, the proposed HyperQUEEN-MF does show outstanding SHSR performances, while the experimental results also show the feasibility of quantum-driven hyperspectral super-resolution. This creates a door for solving other advanced challenging inverse problems via quantum feature information extraction and processing.
Adversarial learning is of paramount importance in numerous recent computing areas, while quantum entanglement has also revealed its role in efficient computing. This motivated us to bring quantum computing into adversarial learning. Though there are some preliminary theoretical studies of quantum adversarial learning (QAL), there has no demonstration of advanced real-world applications. For the first time, we design and implement a QAL framework, and demonstrate the feasibility of QAL-based hyperspectral image processing for remote sensing. This research line aims to lay the foundation for the QAL in hyperspectral remote sensing, including to solve the limited qubit resources, to mitigate the quantum collapse effect, to design a quantum discriminator with comparable ability, as well as to ensure the quantum expressibility of the generator, to name a few.
Quantum machine intelligence starts showing its impact on satellite remote sensing (SRS). Also, recent literature exhibits that quantum generative intelligences encompass superior potential than their classical counterpart, motivating us to develop quantum generative adversarial networks (GANs) for SRS. However, existing quantum GANs are restricted by the limited quantum bit (qubit) resources of current quantum computers and process merely a small 2 × 2 grayscale image, far from being applicable to SRS. Recently, the novel concept of hybrid quantum-classical GAN, a quantum generator with a classical discriminator, has upgraded the order to 28 × 28 (still grayscale), whereas it is still insufficient for SRS. This motivates us to design a radically new hybrid framework, where both generator and discriminator are hybrid architectures. We demonstrate this feasibility, leading to a breakthrough of processing 128×128 hyperspectral images for SRS. Specifically, we design the quantum part with mathematically provable quantum full expressibility (FE) to address core signal processing tasks, wherein the FE property allows the quantum network to realize any valid quantum operator with appropriate training. The classical part, composed of convolutional layers, treats the read-in (compressing the optical information into limited qubits) and read-out (addressing the quantum collapse effect) procedures. The proposed innovative hybrid quantum GAN, named “Hyperspectral Knot-like IntelligeNt dIscrimiNator and Generator” (HyperKING), where “knot” partly symbolizes the quantum entanglement and partly the compressed quantum domain in the central part of the network architecture. HyperKING significantly surpasses the classical approaches in hyperspectral tensor completion, mixed noise removal (about 3dB improvement), and blind source separation results.
Hyperspectral image (HSI) classification is a critical task in the field of remote sensing. Existing deep learning models have made significant progress in HSI classification, but often suffer from parameter redundancy and insufficient noise robustness when handling the high-dimensional nonlinear spatial-spectral information. Recently, quantum computing have garnered significant attention in modeling higher-order correlations with remarkable parameter efficiency. Inspired by this, we propose a QUantum-Enhanced nEural Network (QueenNet) for HSI classification. QueenNet comprises three primary modules, including Quantum Encoding (QE), Quantum Convolution (QC), and Quantum Measurement (QM). Specifically, QE maps HSI into an hyper high-dimensional Hilbert space, encoding it as a quantum states. QC performs HSI feature extraction and transformation using quantum circuits composed of parameterized quantum gate sequences, which effectively enhances the model's ability to capture joint spatial-spectral features while suppressing spectral noise. QM introduces nonlinear feature extraction operations and decodes the HSI features of quantum states back into classic interpretable probability distributions for classification. QueenNet effectively alleviate the limitations of existing HSI classification deep learning models by leveraging the high-dimensional feature representation advantages of quantum computing, introducing a novel paradigm to HSI classification. Extensive experiments on three benchmark datasets demonstrate that QueenNet achieves an order-of-magnitude reduction in model parameters while surpassing the classification performance of state-of-the-art methods.
Abstract: Deep learning algorithms have shown promising results for different image processing tasks, particularly in remote sensing & image recognition. Till now many studies have been carried out on image processing, which brings a new paradigm of innovative capabilities under the umbrella of intelligent remote sensing and computer vision. Accordingly, quantum processing algorithms have proved to efficiently solve some issues that are undetectable to classical algorithms and processors. Keeping that in mind, a Quantum Convolutional Neural Network (QCNN) architecture along with Hybrid Quantum filters would be utilized supported by cloud computing infrastructures and data centers to provide a broad range of complex AI services and high data availability. This research summaries the conventional techniques of Classical and Quantum Deep Learning and it’s research progress on realworld problems in remote sensing image processing as a comparative demonstration. Last but not least, we evaluate our system by training on Street View House Numbers datasets in order to highlight the feasibility and effectiveness of using Quantum Deep Learning approach in image recognition and other similar applications. Upcoming challenges and future research areas on this spectrum are also discussed.