Constructions of Pure Asymmetric Quantum Alternant Codes Based on Subclasses of Alternant Codes

In this paper, we construct asymmetric quantum error-correcting codes(AQCs) based on subclasses of Alternant codes. Firstly, We propose a new subclass of Alternant codes which can attain the classical Gilbert-Varshamov bound to construct AQCs. It is shown that when $d_x=2$, $Z$-parts of the AQCs can attain the classical Gilbert-Varshamov bound. Then we construct AQCs based on a famous subclass of Alternant codes called Goppa codes. As an illustrative example, we get three $[[55,6,19/4]],[[55,10,19/3]],[[55,15,19/2]]$ AQCs from the well known $[55,16,19]$ binary Goppa code. At last, we get asymptotically good binary expansions of asymmetric quantum GRS codes, which are quantum generalizations of Retter's classical results. All the AQCs constructed in this paper are pure

Chinese Organization Name Recognition Using Chunk Analysis

PACLIC 20 / Wuhan, China / 1-3 November, 200

A quantum system control method based on enhanced reinforcement learning

Traditional quantum system control methods often face different constraints, and are easy to cause both leakage and stochastic control errors under the condition of limited resources. Reinforcement learning has been proved as an efficient way to complete the quantum system control task. To learn a satisfactory control strategy under the condition of limited resources, a quantum system control method based on enhanced reinforcement learning (QSC-ERL) is proposed. The states and actions in reinforcement learning are mapped to quantum states and control operations in quantum systems. By using new enhanced neural networks, reinforcement learning can quickly achieve the maximization of long-term cumulative rewards, and a quantum state can be evolved accurately from an initial state to a target state. According to the number of candidate unitary operations, the three-switch control is used for simulation experiments. Compared with other methods, the QSC-ERL achieves close to 1 fidelity learning control of quantum systems, and takes fewer episodes to quantum state evolution under the condition of limited resources.Comment: 10 pages, 3 figure

Construction and Performance of Quantum Burst Error Correction Codes for Correlated Errors

© 2018 IEEE. In practical communication and computation systems, errors occur predominantly in adjacent positions rather than in a random manner. In this paper, we develop a stabilizer formalism for quantum burst error correction codes (QBECC) to combat such error patterns in the quantum regime. Our contributions are as follows. Firstly, we derive an upper bound for the correctable burst errors of QBECCs, the quantum Reiger bound (QRB). Secondly, we propose two constructions of QBECCs: one by heuristic computer search and the other by concatenating two quantum tensor product codes (QTPCs). We obtain several new QBECCs with better parameters than existing codes with the same coding length. Moreover, some of the constructed codes can saturate the quantum Reiger bounds. Finally, we perform numerical experiments for our constructed codes over Markovian correlated depolarizing quantum memory channels, and show that QBECCs indeed outperform standard QECCs in this scenario

Properties and Fabrication of Waterborne Polyurethane Superhydrophobic Conductive Composites with Coupling Agent-Modified Fillers

The addition of abundant fillers to obtain conductive and superhydrophobic waterborne polyurethane (WPU) composites generally results in increased interfaces in the composites, leading to reduced adhesion and poor corrosion resistance. Fillers such as Polytetrafluoroethylene (PTFE) and multi-walled carbon nanotubes (MWCNTs) were first treated by a coupling agent to reduce the contents of the fillers. Thus, in this work, WPU superhydrophobic conductive composites were prepared using electrostatic spraying (EsS). The polar groups (-OH and -COOH, etc.) on the WPU, PTFE, and MWCNTs were reacted with the coupling agent, making the WPU, PTFE, and MWCNTs become crosslinked together. Thus, the uniformity of the coating was improved and its curing interfaces were reduced, causing enhanced corrosion resistance. The dehydration reaction that occurred between the silane coupling agent and the polar surface of Fe formed -NH2 groups, increasing the adhesion of the coating to the steel substrate and then solving the problems of low adhesion, easy delamination, and exfoliation. With the increased content of the modified fillers, the conductivity and hydrophobic property of the composite were amplified, and its corrosion resistance and adhesion were first strengthened and then declined. The composite with the WPU, PTFE, MWCNTs, and KH-550 at a mass ratio of 7:1.5:0.1:0.032 held excellent properties; its volume resistivity and WCA were 1.5 × 104 Ω·cm and 155°, respectively. Compared with the pure WPU coating, its adhesive and anticorrosive properties were both better. This provides a foundation for the fabrication and application of anticorrosive and conductive waterborne composites

Chinese Organization Name Recognition Using Chunk Analysis

Abstract. A simplified N-best cascade model is put forward about Chinese organization names automatic recogni-tion. This model can do words segmentation, part-of-speech tag, chunks analysis and Chinese organization names recognition. The N-best cascade method can limit not only the errors propagation but also the search space. In the experiments, we integrate heuristic information and Organization name abbreviations processing into the model to achieve the better experiment results. The last precision and recall of Chinese organization names recognition are 92.31 % and 81.01 % in IEER99 newswire test set

Cascaded Hierarchical CNN for RGB-Based 3D Hand Pose Estimation

3D hand pose estimation can provide basic information about gestures, which has an important significance in the fields of Human-Machine Interaction (HMI) and Virtual Reality (VR). In recent years, 3D hand pose estimation from a single depth image has made great research achievements due to the development of depth cameras. However, 3D hand pose estimation from a single RGB image is still a highly challenging problem. In this work, we propose a novel four-stage cascaded hierarchical CNN (4CHNet), which leverages hierarchical network to decompose hand pose estimation into finger pose estimation and palm pose estimation, extracts separately finger features and palm features, and finally fuses them to estimate 3D hand pose. Compared with direct estimation methods, the hand feature information extracted by the hierarchical network is more representative. Furthermore, concatenating various stages of the network for end-to-end training can make each stage mutually beneficial and progress. The experimental results on two public datasets demonstrate that our 4CHNet can significantly improve the accuracy of 3D hand pose estimation from a single RGB image