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The engineering of quantum computers requires the reliable characterization of qubits, quantum operations, and even the entire hardware. Quantum tomography is an indispensable framework in quantum characterization, verification, and validation (QCVV), which has been widely accepted by researchers. According to the tomographic target, quantum tomography can be categorized into quantum state tomography (QST), quantum process tomography (QPT), gate set tomography (GST), process tensor tomography (PTT), and instrument set tomography (IST). Standard quantum tomography toolkits generally consist of basic linear inverse methods and statistical maximum likelihood estimation (MLE)-based methods. Furthermore, the performance of standard methods, including effectiveness and efficiency, has been further developed by exploiting Bayesian estimation, neural networks, matrix completion techniques, etc. In this review, we introduce the fundamental quantum tomography techniques, including QST, QPT, GST, PTT, and IST. We first introduce the details of basic linear inverse methods. Then, the framework of MLE methods with constraints is summarized. Finally, we briefly introduce recent further research in developing the performance of tomography, utilizing some symmetry properties of the target. This review provides a primary getting-start in developing quantum tomography, which promotes quantum computer development. [ABSTRACT FROM AUTHOR]

According to BESO's principle of binarizing continuous design variables and the excellent performance of the standard HPO algorithm in terms of solving continuous optimization problems, a discrete binary Hunter-prey optimization algorithm is introduced to construct an efficient topology optimization model. It was used to solve the problems that the BESO method of topology optimization has, such as easily falling into the local optimal value and being unable to obtain the optimal topology configuration; the metaheuristic algorithm was able to solve the topology optimization model's low computational efficiency and could easily produce intermediate elements and unclear boundaries. Firstly, the BHPO algorithm was constructed by discrete binary processing using the s-shape transformation function. Secondly, BHPO-BESO topology optimization theory was established by combining the BHPO algorithm with BESO topology optimization. Using the sensitivity information of the objective function and the updated principle of the meta-heuristic of the BHPO algorithm, a semi-random search for the optimal topology configuration was carried out. Finally, numerical simulation experiments were conducted by using the three typical examples of the cantilever beam, simply supported beam, and clamping beam as optimization objects and the results were compared with the solution results of BESO topology optimization. The experimental results showed that compared with BESO, BHPO-BESO could find the optimal topology configuration with lower compliance and maximum stiffness, and it has higher computational efficiency, which can solve the above problems. [ABSTRACT FROM AUTHOR]

In order to deal with the fast, large-angle attitude maneuver with flexible appendages, a finite-time attitude controller is proposed in this paper. The finite-time sliding mode is constructed by implementing the dynamic sliding mode method; the sliding mode parameter is constructed to be time-varying; hence, the system could have a better convergence rate. The updated law of the sliding mode parameter is designed, and the performance of the standard sliding mode is largely improved; meanwhile, the inherent robustness could be maintained. In order to ensure the system's state could converge along the proposed sliding mode, a finite-time controller is designed, and an auxiliary term is designed to deal with the torque caused by flexible vibration; hence, the vibration caused by flexible appendages could be suppressed. System stability is analyzed by the Lyapunov method, and the superiority of the proposed controller is demonstrated by numerical simulation. [ABSTRACT FROM AUTHOR]

In this paper, a high capacity reversible data hiding technique using a parametric binary tree labeling scheme is proposed. The proposed parametric binary tree labeling scheme is used to label a plaintext image's pixels as two different categories, regular pixels and irregular pixels, through a symmetric or asymmetric process. Regular pixels are only utilized for secret payload embedding whereas irregular pixels are not utilized. The proposed technique efficiently exploits intra-block correlation, based on the prediction mean of the block by symmetry or asymmetry. Further, the proposed method utilizes blocks that are selected for their pixel correlation rather than exploiting all the blocks for secret payload embedding. In addition, the proposed scheme enhances the encryption performance by employing standard encryption techniques, unlike other block based reversible data hiding in encrypted images. Experimental results show that the proposed technique maximizes the embedding rate in comparison to state-of-the-art reversible data hiding in encrypted images, while preserving privacy of the original contents. [ABSTRACT FROM AUTHOR]

Geometric feature extraction of 3D point clouds plays an important role in many 3D computer vision applications such as region labeling, 3D reconstruction, object segmentation, and recognition. However, hand-designed features on point clouds lack semantic information, so cannot meet these requirements. In this paper, we propose local feature extraction network (LFE-Net) which focus on extracting local feature for point clouds analysis. Such geometric features learning from a relation of local points can be used in a variety of shape analysis problems such as classification, part segmentation, and point matching. LFE-Net consists of local geometric relation (LGR) module which aims to learn a high-dimensional local feature to express the relation between points and their neighbors. Benefiting from the additional singular values of local points and hierarchical neural networks, the learned local features are robust to permutation and rigid transformation so that they can be transformed into 3D descriptors. Moreover, we embed prior spatial information of the local points into the sub-features for combining features from multiple levels. LFE-Net achieves state-of-the-art performances on standard benchmarks including ModelNet40, ShapeNetPart. [ABSTRACT FROM AUTHOR]

In this paper, we propose to present a novel technique for designing cryptographically strong substitution-boxes using cubic polynomial mapping. The proposed cubic polynomial mapping is proficient to map the input sequence to a strong 8 × 8 S-box meeting the requirements of a bijective function. The use of cubic polynomial maintains the simplicity of S-box construction method and found consistent when compared with other existing S-box techniques used to construct S-boxes. An example proposed S-box is obtained which is analytically evaluated using standard performance criteria including nonlinearity, bijection, bit independence, strict avalanche effect, linear approximation probability, and differential uniformity. The performance results are equated with some recently scrutinized S-boxes to ascertain its cryptographic forte. The critical analyses endorse that the proposed S-box construction technique is considerably innovative and effective to generate cryptographic strong substitution-boxes. [ABSTRACT FROM AUTHOR]