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Short text representation is one of the basic and key tasks of NLP. The traditional method is to simply merge the bag-of-words model and the topic model, which may lead to the problem of ambiguity in semantic information, and leave topic information sparse. We propose an unsupervised text representation method that involves fusing word embeddings and extended topic information. Following this, two fusion strategies of weighted word embeddings and extended topic information are designed: static linear fusion and dynamic fusion. This method can highlight important semantic information, flexibly fuse topic information, and improve the capabilities of short text representation. We use classification and prediction tasks to verify the effectiveness of the method. The testing results show that the method is valid.

Compared with traditional computers, quantum computers can provide exponential acceleration for certain critical fields. However, the coupling of quantum systems with the environment, along with the intrinsic characteristics of quantum systems, has collectively introduced quantum noise, which has emerged as a significant impediment to the development of quantum computing. Quantum error mitigation (QEM) has been proposed as an alternative solution in the noisy intermediate-scale quantum era. In recent years, with the rise of artificial intelligence, machine learning-based QEM technology has received attention from the industry. However, the latest machine learning-based QEM techniques have limitations, especially their inability to mitigate errors in the quantum circuits whose number of qubits exceeds the number of qubits in the training set, and their tendency to amplify noise when constructing feature sets. This paper proposes QEMOS, a novel random forest-based machine learning model that utilizes a new feature dataset incorporating quantum computer backend properties, with feature dimensionality reduction enabling decoupling from the number of qubits. The model is trained and tested using six different simulators from Qiskit and a real quantum computer tianyan-176. It is worth noting that this model overcomes the limitation of sensitivity to the number of qubits, which was the main problem of previous methods. When trained on 5–9 qubit circuits, the model achieves a probability of correct mitigation of 86.38% on 2–13 qubit circuits, though this efficacy is observed primarily for circuits exhibiting high-probability outputs and decreases as all output probabilities approach zero. Compared to the baseline, the model demonstrates a 31.74% error reduction on test sets with more qubits than the training set. On real quantum computer, testing shows an average error reduction of 67.5%.

Sequential Monte Carlo (SMC) methods represent a classical set of techniques to simulate a sequence of probability measures through a simple selection/mutation mechanism. However, the associated selection functions and mutation kernels usually depend on tuning parameters that are of first importance for the efficiency of the algorithm. A standard way to address this problem is to apply adaptive sequential Monte Carlo (ASMC) methods, which consist in exploiting the information given by the history of the sample to tune the parameters. This article is concerned with variance estimation in such ASMC methods. Specifically, we focus on the case where the asymptotic variance coincides with the one of the “limiting” sequential Monte Carlo algorithm as defined by Beskos et al. (Ann. Appl. Probab. 26 (2016) 1111–1146). We prove that, under natural assumptions, the estimator introduced by Lee and Whiteley (Biometrika 105 (2018) 609–625) in the nonadaptive case (i.e., SMC) is also a consistent estimator of the asymptotic variance for ASMC methods. To do this, we introduce a new estimator that is expressed in terms of coalescent tree-based measures, and explain its connection with the previous one. Our estimator is constructed by tracing the genealogy of the associated interacting particle system. The tools we use connect the study of particle Markov chain Monte Carlo methods and the variance estimation problem in SMC methods. As such, they may give some new insights when dealing with complex genealogy-involved problems of interacting particle systems in more general scenarios.

The effect of direct quenched (DQ) and tempering temperature on the microstructure and mechanical properties of high strength steel were studied by means of SEM, EBSD, TEM and mechanical properties test. The results showed that in DQ state, the tensile strength could reach 1420 Mpa, the yield strength could be 1050 Mpa, and the elongation was about 9.0%, impact energy at −20 °C was 59 J. High density entangled dislocation was distributed inside the lath and at the boundary. A small amount of Nb and Ti carbide was precipitated at the dislocation and lath boundary, and a small amount (about 2.15%) of residual austenite distributed between the lath. With the tempering temperature rising from 500 °C to 720 °C, the tensile strength of the experimental steel decreased from 1220 MPa to 840 MPa, the yield strength decreased from 1190 MPa to 780 MPa, the elongation increased from 10% to 13%, and the impact energy at −20 °C increased from 84 J to 153 J. When the tempering temperature rised from 500 °C to 640 °C, the structure was mainly composed of lath martensite and a large number of dislocations were still distributed inside the lath. The size of carbides precipitated inside and on the boundary of the lath was about 20-30 nm. When tempered at 680 °C, the structure was mainly composed of martensite and a small amount of polygonal ferrite. There were still a large number of entangled dislocations inside and on the boundary of martensite. The carbide precipitate at the matrix boundary and dislocation line was obviously coarsening (70-80 nm). When tempered at 720 °C, the microstructure was mainly polygonal ferrite, the dislocation density in the matrix significantly decreased, and the carbide precipitated at the matrix boundary and dislocation line significantly coarsened (about 100 nm). With the tempering temperature rising from 500 °C to 720 °C, the proportion of small-angle grain boundary was gradually decreased from 88.64% to 70.50%.

Topic recognition technology has been commonly applied to identify different categories of news topics from the vast amount of web information, which has a wide application prospect in the field of online public opinion monitoring, news recommendation, and so on. However, it is very challenging to effectively utilize key feature information such as syntax and semantics in the text to improve topic recognition accuracy. Some researchers proposed to combine the topic model with the word embedding model, whose results had shown that this approach could enrich text representation and benefit natural language processing downstream tasks. However, for the topic recognition problem of news texts, there is currently no standard way of combining topic model and word embedding model. Besides, some existing similar approaches were more complex and did not consider the fusion between topic distribution of different granularity and word embedding information. Therefore, this paper proposes a novel text representation method based on word embedding enhancement and further forms a full-process topic recognition framework for news text. In contrast to traditional topic recognition methods, this framework is designed to use the probabilistic topic model LDA, the word embedding models Word2vec and Glove to fully extract and integrate the topic distribution, semantic knowledge, and syntactic relationship of the text, and then use popular classifiers to automatically recognize the topic categories of news based on the obtained text representation vectors. As a result, the proposed framework can take advantage of the relationship between document and topic and the context information, which improves the expressive ability and reduces the dimensionality. Based on the two benchmark datasets of 20NewsGroup and BBC News, the experimental results verify the effectiveness and superiority of the proposed method based on word embedding enhancement for the news topic recognition problem.

As cloud quantum computing gains broader acceptance, a growing quantity of researchers are directing their focus towards this domain. Nevertheless, the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity, which in turn hampers users from achieving optimal satisfaction. Therefore, cloud quantum computing service providers require a unified analysis and scheduling framework for their quantum resources and user jobs to meet the ever-growing usage demands. This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment. The framework addresses the issue of limited quantum computing resources in cloud environments and ensures a satisfactory user experience. It introduces three innovative designs: 1) Our framework automatically allocates tasks to different quantum backends while ensuring fairness among users by considering both the cloud-based quantum resources and the user-submitted tasks. 2) Multi-programming mechanism is employed across different quantum backends to enhance the overall throughput of the quantum cloud. In comparison to conventional task schedulers, our proposed framework achieves a throughput improvement of more than two-fold in the quantum cloud. 3) The framework can balance fidelity and user waiting time by adaptively adjusting scheduling parameters.

As the demand for computing power continues to rise, it is difficult for a single type of computing device or architecture to satisfy the current situation. Diversity and heterogeneity are becoming more and more popular. Seamlessly integrating the realms of high performance computing and quantum computing, and harnessing their collective potential, has emerged as a consensus approach to effectively address the pressing need for increased computing power. In the emerging computing scenario, various different types of computing devices have super-heterogeneous characteristics, and there are significant differences in computational principles, programming models, parallelism, etc. Effectively harnessing these disparate resources and achieving a unified programming paradigm have become urgent imperatives. To address the above problems, this paper introduces QCCP, a taskflow programming model that enables efficient collaborative computing between classical computers and quantum computers. QCCP establishes a unified programming abstraction, shields the super-heterogeneous characteristics of the underlying network and hardware, and supports flexible scheduling for different computational backends. The experimental results indicate that QCCP can support the processing of hybrid classical-quantum applications with diverse program structures. In particular, QCCP reveals its immense potential and superiority in tackling real-world challenges, specifically in the realm of quantum circuit cutting and reconstruction.

Quantum circuit fidelity is a crucial metric for assessing the accuracy of quantum computation results and indicating the precision of quantum algorithm execution. The primary methods for assessing quantum circuit fidelity include direct fidelity estimation and mirror circuit fidelity estimation. The former is challenging to implement in practice, while the latter requires substantial classical computational resources and numerous experimental runs. In this paper, we propose a fidelity estimation method based on Layer Interleaved Randomized Benchmarking, which decomposes a complex quantum circuit into multiple sublayers. By independently evaluating the fidelity of each layer, one can comprehensively assess the performance of the entire quantum circuit. This layered evaluation strategy not only enhances accuracy but also effectively identifies and analyzes errors in specific quantum gates or qubits through independent layer evaluation. Simulation results demonstrate that the proposed method improves circuit fidelity by an average of 6.8% and 4.1% compared to Layer Randomized Benchmarking and Interleaved Randomized Benchmarking methods in a thermal relaxation noise environment, and by 40% compared to Layer RB in a bit-flip noise environment. Moreover, the method detects preset faulty quantum gates in circuits generated by the Munich Quantum Toolkit Benchmark, verifying the model’s validity and providing a new tool for faulty gate detection in quantum circuits.

Sequential Monte Carlo (SMC) methods represent a classical set of techniques to simulate a sequence of probability measures through a simple selection/mutation mechanism. However, the associated selection functions and mutation kernels usually depend on tuning parameters that are of first importance for the efficiency of the algorithm. A standard way to address this problem is to apply Adaptive Sequential Monte Carlo (ASMC) methods, which consist in exploiting the information given by the history of the sample to tune the parameters. This article is concerned with variance estimation in such ASMC methods. Specifically, we focus on the case where the asymptotic variance coincides with the one of the \"limiting\" Sequential Monte Carlo algorithm as defined by Beskos et al. (2016). We prove that, under natural assumptions, the estimator introduced by Lee and Whiteley (2018) in the nonadaptive case (i.e., SMC) is also a consistent estimator of the asymptotic variance for ASMC methods. To do this, we introduce a new estimator that is expressed in terms of coalescent tree-based measures, and explain its connection with the previous one. Our estimator is constructed by tracing the genealogy of the associated Interacting Particle System. The tools we use connect the study of Particle Markov Chain Monte Carlo methods and the variance estimation problem in SMC methods. As such, they may give some new insights when dealing with complex genealogy-involved problems of Interacting Particle Systems in more general scenarios.

We present new insights into causal inference in the context of Heterogeneous Treatment Effects by proposing natural variants of Random Forests to estimate the key conditional distributions. To achieve this, we recast Breiman's original splitting criterion in terms of Wasserstein distances between empirical measures. This reformulation indicates that Random Forests are well adapted to estimate conditional distributions and provides a natural extension of the algorithm to multivariate outputs. Following the philosophy of Breiman's construction, we propose some variants of the splitting rule that are well-suited to the conditional distribution estimation problem. Some preliminary theoretical connections are established along with various numerical experiments, which show how our approach may help to conduct more transparent causal inference in complex situations.