Quantum mechanism, which has received widespread attention, is in continuous evolution rapidly. The powerful computing power and high parallel ability of quantum mechanism equip the quantum field with broad application scenarios and brand-new vitality. Inspired by nature, intelligent algorithm has always been one of the research hotspots. It is a frontier interdisciplinary subject with a perfect integration of biology, mathematics and other disciplines. Naturally, the idea of combining quantum mechanism with intelligent algorithms will inject new vitality into artificial intelligence system. This paper lists major breakthroughs in the development of quantum domain firstly, then summarizes the existing quantum algorithms from two aspects: quantum optimization and quantum learning. After that, related concepts, main contents and research progresses of quantum optimization and quantum learning are introduced respectively. At last, experiments are conducted to prove that quantum intelligent algorithms have strong competitiveness compared with traditional intelligent algorithms and possess great potential by simulating quantum computing.

Automatically searching for optimal hyper parameters is of crucial importance for applying machine learning algorithms in practice. However, there are concerns regarding the tradeoff between efficiency and effectiveness of current approaches when faced with the expensive function evaluations. In this paper, a novel efficient hyper-parameter optimization algorithm is proposed (called MARSAOP), in which multivariate spline functions are used as surrogate and dynamic coordinate search approach is employed to generate the candidate points. Empirical studies on benchmark problems and machine-learning models (e.g., SVM, RF, and NN) demonstrate that the proposed algorithm is able to find relatively high-quality solutions for benchmark problems and excellent hyper-parameter configurations for machine-learning models using a limited computational budget (few function evaluations).

With the development of deep learning, more and more neural networks have been used in polarimetric synthetic aperture radar (PolSAR) image classification and obtain good results. As we all know, the performances of neural networks highly depend on well-designed neural architectures. Besides, the features input to neural networks also have a huge impact on the classification results. Both architecture design and feature selection are time-consuming and require human expertise. So, in this paper, we propose a neural architecture search method with feature selection (Pol-NAS) for PolSAR image classification. It can automatically search and obtain a good architecture including intra-cell, inter-cell structure and the number of layers in the search stage. Meanwhile, all the features commonly used in PolSAR data interpretation, rather than part of them, are input to the model in order to avoid selecting the size of optimal feature subset, which is a hyper-parameter and usually different for different models. Then, we propose the Feature Attention block (FA block) and redesign the stem layers by combining the FA block and the original stem layers. Thus, Pol-NAS can adaptively find the importance of each feature in the training stage by using the redesigned stem layers. With the help of Pol-NAS, we only need to prepare the data and wait for the classification results. Experimental results on three real PolSAR datasets show that the performance of Pol-NAS is better than that of state-of-the-art PolSAR image classification models. The code is available at https://github.com/guangyuanLiu/Pol-NAS .

The visual question answering (VQA) method applied to remote sensing images (RSIs) can complete the interaction of image information and text information, which avoids professional barriers in different RSIs processing fields. The current methods face challenges in both fully using the global and local information of the image to interact with the question information and addressing the issue of interclass interference. To address these challenges, this letter proposes a remote sensing visual question answering (RSVQA) mask-based dual-stream feature mutual attention network (MADNet). First, the dual-stream feature extraction module of the image is used to obtain image features, and the deep and shallow layer feature encoding module is used to obtain question features. Second, the attention mechanism is introduced and combined with the pointwise multiplication method to use the dual-stream features that were extracted in the earlier step. Finally, an answer relevance modulation module based on a binary mask vector is implemented to filter out irrelevant answers. In the experiments, the performance of the proposed strategy is evaluated using two datasets collected by aerial and Sentinel-2 sensors. In our study, we propose a model that outperforms previous approaches, achieving a 6.89% increase in overall accuracy (OA) over the baseline. This enhancement is notable for its persistence, even when the training data are reduced by half, as evidenced by our experiments on the low-resolution (LR) dataset.