The challenge of quantum computing is to combine error resilience with universal computation. There are many finite sets of gates that are universal, and a standard choice is to augment the set of Clifford gates by a non-Clifford unitary such as the T gate. Given a CSS code, we introduce a method of synthesizing all possible diagonal physical gates that preserve the codespace and induce a target logical gate. We denote an 〚n, k = k1− k2, d〛 CSS code $\mathcal{C}$ by $\operatorname{CSS} \left( {X,{\mathcal{C}_2};Z,\mathcal{C}_1^ \bot } \right)$, where the [n, k2] binary code ${\mathcal{C}_2}$ determines the X-stabilizers in $\mathcal{C}$, and the [n, n−k1] binary code $\mathcal{C}_1^ \bot $ determines the Z-stabilizers in $\mathcal{C}$. The diagonal entries of a diagonal physical gate are indexed by binary vectors in $\mathbb{F}_2^n$. We show that a diagonal physical gate preserves the CSS codespace if and only if entries from the same coset of ${\mathcal{C}_2}$ in ${\mathcal{C}_1}$ (same X-logical) are identical. We also show that the target logical operator only specifies ${2^{{k_1}}}$ out of 2ndiagonal entries of the diagonal physical gate. The remaining degrees of freedom can be used to optimize implementation of the physical gate within a particular quantum computing infrastructure. This encompasses optimization with respect to locality of the physical gate, a criterion that is essential to fault tolerance. When the target logical operator is the identity, the physical gates that preserve the CSS code represent noise operators to which the codespace is oblivious. We illustrate our method by providing several examples of code-gate pairs for which the target logical gate is a non-Clifford unitary. The framework is extended to stabilizer codes in https://arxiv.org/abs/2109.13481.

Collaborative filtering (CF) is initiated by representing users and items as vectors and seeks to describe the relationship between users and items at a profound level, thus predicting users’ preferred behavior. To address the issue that previous research ignored higher-order geographical interactions hidden in users’ historical behaviors, this paper proposes a location-aware neural graph collaborative filtering model (LA-NGCF), which incorporates location information of items for improving prediction performance. The model characterizes the interactions between items based on spatial decay law from a graph perspective and designs two strategies to capture the interaction effects of users and items considering node heterogeneity. An optimized loss function with spatial distances of items is also developed in the model. Extensive experiments are conducted on three publicly available real-world datasets to examine the effectiveness of our model. Results show that LA-NGCF achieves competitive performances compared with several state-of-the-art models, which suggests that location information of items is beneficial for improving the performance of personalized recommendations. This paper offers an approach to incorporate weighted interactions between items into CF algorithms and enriches the methods of utilizing geographical information for artificial intelligence applications.

Physical platforms such as trapped ions suffer from coherent noise that does not follow a simple stochastic model. Stochastic errors in quantum systems occur randomly but coherent errors are more damaging since they can accumulate in a particular direction. We consider coherent noise acting transversally, giving rise to an effective error which is a$Z$-rotation on each qubit by some angle$\theta $. Rather than address coherent noise through active error correction, we investigate passive mitigation through decoherence free subspaces. In the language of stabilizer codes, we require the noise to preserve the code space, and to act trivially (as the logical identity operator) on the protected information. Thus, we develop necessary and sufficient conditions for all transversal$Z$-rotations to preserve the code space of a stabilizer code. These conditions require the weight-$2~Z$-stabilizers to cover all the qubits that are in the support of the$X$-component of some stabilizer. Furthermore, the weight-$2~Z$-stabilizers generate a direct product of single-parity-check codes with even block length. By adjusting the sizes of these components, we are able to construct a large family of QECC codes oblivious to coherent noise, one that includes the$[[4L^{2}, 1, 2L]]$Shor codes. The Shor codes are examples of constant excitation codes, where logical qubits are encoded as a code state that is a sum of physical states indexed by binary vectors with the same weight. Constant excitation codes are oblivious to coherent noise since a transversal$Z$-rotation acts as a global phase. We prove that a CSS code is oblivious to coherent noise if and only if it is a constant excitation code, and that if the code is error-detecting, then the (constant) weights in different cosets of the$X$-stabilizers are identical.

Physical platforms such as trapped ions suffer from coherent noise that does not follow a simple stochastic model. We view coherent errors as rotations about a particular axis, and observe that since they can accumulate coherently over time, they can be more damaging. It is natural to consider coherent noise acting transversally giving rise to an effective error, which is a$Z$-rotation on each qubit by some angle$\theta$. Rather than addressing coherent noise through active error correction, we instead investigate passive mitigation through decoherence free subspaces. In the language of stabilizer codes, we require the noise to preserve the code space, and to act trivially (as the logical identity operator) on the protected information. Thus, we develop necessary and sufficient conditions for all transversal$Z$-rotations to preserve the code space of a stabilizer code. These conditions require the existence of a large number of weight 2$Z$-stabilizers, and together, these weight 2$Z$-stabilizers generate a direct product of single-parity-check codes. By adjusting the size of these components, we are able to construct a large family of CSS codes, oblivious to coherent noise, that includes the$[[4L^{2}, 1,2L]]$Shor codes. Given$m$even and given any$[[n, k, d]]$CSS code, we can construct an$[[mn, k, d^{\prime}\geq d]]$CSS code that is oblivious to coherent noise. This result is generalized to stabilizer codes in [Hu, Liang, Rengaswamy, and Calderbank 2020]. The MacWilliams Identities play a central role in the technical analysis, and classical coding theorists may be interested in connections to classical codes with all weights divisible by some integer$d$.

Relation Extraction is concentrated on finding the unknown relational facts automatically from the unstructured texts. Most current methods, especially the distant supervision relation extraction (DSRE), have been successfully applied to achieve this goal. DSRE combines knowledge graph and text corpus to corporately generate plenty of labeled data without human efforts. However, the existing methods of DSRE ignore the noisy words within sentences and suffer from the noisy labelling problem; the additional knowledge is represented in a common semantic space and ignores the semantic-space difference between relations and entities. To address these problems, this study proposes a novel hierarchical attention model, named the Bi-GRU-based Knowledge Graph Attention Model (BG2KGA) for DSRE using the Bidirectional Gated Recurrent Unit (Bi-GRU) network. BG2KGA contains the word-level and sentence-level attentions with the guidance of additional knowledge graph, to highlight the key words and sentences respectively which can contribute more to the final relation representations. Further-more, the additional knowledge graph are embedded in the multi-semantic vector space to capture the relations in 1-N, N-1 and N-N entity pairs. Experiments are conducted on a widely used dataset for distant supervision. The experimental results have shown that the proposed model outperforms the current methods and can improve the Precision/Recall (PR) curve area by 8% to 16% compared to the state-of-the-art models; the AUC of BG2KGA can reach 0.468 in the best case.

Summary Vehicle Ad‐Hoc Networks (VANET) enable all components in intelligent transportation systems to be connected so as to improve transport safety, relieve traffic congestion, reduce air pollution, and enhance driving comfort. The vision of all vehicles connected poses a significant challenge to the collection, storage, and analysis of big traffic‐related data. Vehicular cloud computing, which incorporates cloud computing into vehicular networks, emerges as a promising solution. Different from conventional cloud computing platform, the vehicle mobility poses new challenges to the allocation and management of cloud resources in roadside cloudlet. In this paper, we study a virtual machine (VM) migration problem in roadside cloudlet‐based vehicular network and unfold that (1) whether a VM shall be migrated or not along with the vehicle moving and (2) where a VM shall be migrated, in order to minimize the overall network cost for both VM migration and normal data traffic. We first treat the problem as a static off‐line VM placement problem and formulate it into a mixed‐integer quadratic programming problem. A heuristic algorithm with polynomial time is then proposed to tackle the complexity of solving mixed‐integer quadratic programming. Extensive simulation results show that it produces near‐optimal performance and outperforms other related algorithms significantly. Copyright © 2015 John Wiley & Sons, Ltd.

Software defined wireless sensor network can be adapted to different application needs through dynamic programming. In this paper, we propose a signal acquisition and processing wireless sensor network (SAPSN). SAPSN consists of sampling nodes, processing nodes and remote controllers. At first, the sampling node completes the local signal sampling by analog-digital conversion. Next, according to the different demand from the remote controller, processing node completes time domain or frequency domain analysis of signal processing, and transfers the results back to the remote controller. Finally, the application in remote controller will display the results according to different user's needs. SAPSN is capable of time domain or frequency domain signal analysis and processing, depending on different application requirements. In this paper, we present the concept underlying SAPSN, its architecture. We also present preliminary experimental results.

In practical applications, the interference between the antenna and the metal structure nearby often cause deviation of the electromagnetic performance of the antenna. The factor of finite reflector should be considered in the evolutionary antenna to avoid the influence so that the optimal antenna structure can be better applicable to real-world conditions. Therefore, a kind of design model of evolutionary antenna with a finite reflector is proposed in this paper, which includes the corresponding chromosome coding and the design workflow. In this design model, the antenna is optimized with its finite reflector during the evolutionary process. Meanwhile aiming at the characteristics of the model, the method of invoking the simulation software and setting the finite reflector is discussed to evaluate the antenna individuals effectively. This design model with a finite reflector was applied in a practical design problem, and the experimental result shows that it is superior to the one with a infinite reflector both on gain and VSWR. It illustrates that it is very important to take the reflector factor into consideration in the design model of evolutionary antenna.

Multi-label classification is a generalization of single-label classification, and its samples belong to multiple labels. The K-nearest neighbor algorithm can solve this problem as an optimization problem. It finds the optimum solution by caculating the distance between each sample in general. But in fact, the distance of K-nearest neighbor algorithm may be miscalculated due to the caused by the redundant or irrelevant characteristic value. In order to solve this problem, in this paper, we propose a novel method that uses the particle swarm algorithm to optimize the feature weights to improve the accuracy of distance calculation. As a result, it can improve classification accuracy further. The experimental results show that applying particle swarm algorithm's optimization technique to improving K-nearest neighbor algorithm for multi-label classification problem, can improve the accuracy of classification effectively.

The rapid development of mobile platform leads to growing demand for network communications, which suffer from increasingly severe security threats as well. Considering the urgent security demand and the limitation of battery capacity, the power consumption of the encryption algorithms plays a pivotal role for mobile device. Aiming at the above problem, this paper proposes a performance evaluation system on Android platform, which can evaluate the state parameters of device battery. Our work provides elaborate analysis and exploration of 10 security algorithms' energy consumption through extensive simulation results. With the popularity of mobile network and the rapid increase of related applications, it is significant to reduce the power consumption of security encryption algorithms on mobile devices in both academic field and industry.