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Abstract As a generalized quantum machine learning model, parameterized quantum circuits (PQC) have been found to perform poorly in terms of classification accuracy and model scalability for multi-category classification tasks. To address this issue, we propose a scalable parameterized quantum circuits classifier (SPQCC), which performs per-channel PQC and combines the measurements as the output of the trainable parameters of the classifier. By minimizing the cross-entropy loss through optimizing the trainable parameters of PQC, SPQCC leads to a fast convergence of the classifier. The parallel execution of identical PQCs on different quantum machines with the same structure and scale reduces the complexity of classifier design. Classification simulations performed on the MNIST Dataset show that the accuracy of our proposed classifier far exceeds that of other quantum classification algorithms, achieving the state-of-the-art simulation result and surpassing/reaching classical classifiers with a considerable number of trainable parameters. Our classifier demonstrates excellent scalability and classification performance.

Abstract In recent years, deep learning has been widely used in vulnerability detection with remarkable results. These studies often apply natural language processing (NLP) technologies due to the natural similarity between code and language. Since NLP usually consumes a lot of computing resources, its combination with quantum computing is becoming a valuable research direction. In this paper, we present a Recurrent Quantum Embedding Neural Network (RQENN) for vulnerability detection. It aims to reduce the memory consumption of classical models for vulnerability detection tasks and improve the performance of quantum natural language processing (QNLP) methods. We show that the performance of RQENN achieves the above goals. Compared with the classic model, the space complexity of each stage of its execution is exponentially reduced, and the number of parameters used and the number of bits consumed are significantly reduced. Compared with other QNLP methods, RQENN uses fewer qubit resources and achieves a 15.7% higher accuracy in vulnerability detection.

Abstract Quantum algorithms have shown their superiority in many application fields. However, a general quantum algorithm for numerical integration, an indispensable tool for processing sophisticated science and engineering issues, is still missing. Here, we first proposed a quantum integration algorithm suitable for any continuous functions that can be approximated by polynomials. More impressively, the algorithm achieves quantum encoding of any integrable functions through polynomial approximation, then constructs a quantum oracle to mark the number of points in the integration area and finally converts the statistical results into the phase angle in the amplitude of the superposition state. The quantum algorithm introduced in this work exhibits quadratic acceleration over the classical integration algorithms by reducing computational complexity from O(N) to O(√N). Our work addresses the crucial impediments for improving the generality of quantum integration algorithm, which provides a meaningful guidance for expanding the superiority of quantum computing.

Abstract In Noisy Intermediate-Scale Quantum (NISQ) era, the scarcity of qubit resources has prevented many quantum algorithms from being implemented on quantum devices. Circuit cutting technology has greatly alleviated this problem, which allows us to run larger quantum circuits on real quantum machines with currently limited qubit resources at the cost of additional classical overhead. However, the classical overhead of circuit cutting grows exponentially with the number of cuts and qubits, and the excessive postprocessing overhead makes it difficult to apply circuit cutting to large scale circuits. In this paper, we propose a fast reconstruction algorithm based on Hamiltonian Monte Carlo (HMC) sampling, which samples the high probability solutions by Hamiltonian dynamics from state space with dimension growing exponentially with qubit. Our algorithm avoids excessive computation when reconstructing the original circuit probability distribution, and greatly reduces the circuit cutting post-processing overhead. The improvement is crucial for expanding of circuit cutting to a larger scale on NISQ devices.

The brick-and-mortar biomimetic structure Ti/Al3Ti metal-intermetallic laminate (BMS-MIL) composites were prepared by vacuum hot pressing. BMS-MIL composites with various Ti volume fractions from 45–71% were obtained by controlling the high-temperature diffusion time of Al–Ti. The phase composition of the BMS-MIL composites was analyzed, and the influence of Ti volume fraction on the microstructure was quantitatively characterized. Quasi-static compression and three-point bending tests were carried out to study the mechanical properties and fracture mechanism of the BMS-MIL composites. The results indicated that, in BMS-MIL composites, Al3Ti appears as polygonal platelets, and Ti fills the gaps between the platelets. With decreasing Ti volume fraction, the size of Al3Ti platelets increased, the number of platelets decreased, and the aspect ratio of platelets first increased and then decreased. The decrease of Ti volume fraction leads to the decrease of mechanical properties of composites. However, as a reasonable and efficient material design strategy, the brick-and-mortar structure ensured that the specific strength of the BMS-MIL composites with load perpendicular to the layer remained stable, with specific strength maintained between 345 and 353 kN m/kg. The compression properties with load parallel to the layer are significantly affected by the platelet's aspect ratio. The sample with 65% volume fraction Ti exhibited the optimal strength, specific strength, and failure strain when compressed parallel to the layer, with a strength of 1347.3 MPa, specific strength of 330.2 kN m/kg, and a failure strain of 6.32%.

In order to build a composite application scenario for the synergistic development of clean energy generation and agricultural production, this research designs a solar chimney equipped with a rectangular collection system according to the structural characteristics of agricultural greenhouses. With collector efficiency as the primary objective and theoretical power as the secondary objective, the infusion angle is designed for the connecting section between the collection system and the chimney from the perspective of reducing the local resistance loss, and the transition factor ζ is introduced to define it accurately. Response surface methodology was then used to analyze the effect of the level of key system characteristics on collector efficiency and static pressure, and a regression model was developed to determine the best combination within the target optimization range. Results show that the infusion angle prevents too much solar energy from being lost in the vortex by reducing the local resistance loss, which increases the collector efficiency of the system. And when the transition factor is 0, the divergence angle is 2.86°, and the inlet height is 0.05 m, the collector efficiency and static pressure reach 48.75 % and −6.1 pa, respectively, which are 213 % and 563 % higher than the original model.

The brick-and-mortar structure of shell nacre allows for the simultaneous enhancement of strength and plasticity through the synergistic effect of multiple toughening mechanisms at different scales. In this work, a novel nacre-like brick-and-mortar structure Ti6Al4V/Al3Ti metal-intermetallic laminate (BMS-MIL) composite was successfully fabricated via vacuum hot pressing sintering method. The BMS-MIL composite is mainly composed of Al3Ti and Ti phases, with a small amount of Al2Ti and AlTi phases. Al3Ti is in the shape of polygonal platelet, and a 5–8 μm diffusion layer composed of Al2Ti and AlTi is distributed on the surface of the platelets. Ti6Al4V connects the platelets and is present in a continuous network frame structure. Compared with the layered structure Ti6Al4V/Al3Ti metal-intermetallic laminate (LS-MIL) composite, the fracture strain of BMS-MIL composite increases by 125.1% while maintaining high strength under load perpendicular to layer, and the compressive strength of BMS-MIL composite increases by 26.3% under load parallel to layer, respectively. Material isotropy in compressive properties has been significantly improved. In bending tests, the strength of the BMS-MIL composite is 1.9 times that of the LS-MIL composite. The brick-and-mortar structure’s excellent strengthening and toughening effect benefits from the enhancement of its crack constraint.

In the field of machine learning, the multi-category classification problem plays a crucial role. Solving the problem has a profound impact on driving the innovation and development of machine learning techniques and addressing complex problems in the real world. In recent years, researchers have begun to focus on utilizing quantum computing to solve the multi-category classification problem. Some studies have shown that the process of processing information in the brain may be related to quantum phenomena, with different brain regions having neurons with different structures. Inspired by this, we design a quantum multi-category classifier model from this perspective for the first time. The model employs a heterogeneous population of quantum neural networks (QNNs) to simulate the cooperative work of multiple different brain regions. When processing information, these heterogeneous clusters of QNNs allow for simultaneous execution on different quantum computers, thus simulating the brain’s ability to utilize multiple brain regions working in concert to maintain the robustness of the model. By setting the number of heterogeneous QNN clusters and parameterizing the number of stacks of unit layers in the quantum circuit, the model demonstrates excellent scalability in dealing with different types of data and different numbers of classes in the classification problem. Based on the attention mechanism of the brain, we integrate the processing results of heterogeneous QNN clusters to achieve high accuracy in classification. Finally, we conducted classification simulation experiments on different datasets. The results show that our method exhibits strong robustness and scalability. Among them, on different subsets of the MNIST dataset, its classification accuracy improves by up to about 5% compared to other quantum multiclassification algorithms. This result becomes the state-of-the-art simulation result for quantum classification models and exceeds the performance of classical classifiers with a considerable number of trainable parameters on some subsets of the MNIST dataset.

There have been numerous quantum neural networks reported, but they struggle to match traditional neural networks in accuracy. Given the huge improvement of the neural network models’ accuracy by two-dimensional tensor network (TN) states in classical tensor network machine learning (TNML), it is promising to explore whether its application in quantum machine learning can extend the performance boundary of the models. Here, we transform two-dimensional TNs into quantum circuits for supervised learning. Specifically, we encode two-dimensional TNs into quantum circuits through rigorous mathematical proofs for constructing model ansätze, including string-bond states, entangled-plaquette states and isometric TN states. In addition, we propose adaptive data encoding methods and combine with TNs. We construct a tensor-network-inspired quantum circuit (TNQC) supervised learning framework for transferring TNML from classical to quantum, and build several novel two-dimensional TN-inspired quantum classifiers based on this framework. Finally, we propose a parallel quantum machine learning method for multi-class classification to construct 2D TNQC-based multi-class classifiers. Classical simulation results on the MNIST benchmark dataset show that our proposed models achieve the state-of-the-art accuracy performance, significantly outperforming other quantum classifiers on both binary and multi-class classification tasks, and beat simple convolutional classifiers on a fair track with identical inputs. The noise resilience of the models makes them successfully run and work in a real quantum computer.

The use of floating-point numbers inevitably leads to inaccurate results and, in certain cases, significant program failures. Detecting floating-point errors is critical to ensuring that floating-point programs outputs are proper. However, due to the sparsity of floating-point errors, only a limited number of inputs can cause significant floating-point errors, and determining how to detect these inputs and to selecting the appropriate search technique is critical to detecting significant errors. This paper proposes characteristic particle swarm optimization (CPSO) algorithm based on particle swarm optimization (PSO) algorithm. The floating-point expression error detection tool PSOED is implemented, which can detect significant errors in floating-point arithmetic expressions and provide corresponding input. The method presented in this paper is based on two insights: (1) treating floating-point error detection as a search problem and selecting reliable heuristic search strategies to solve the problem; (2) fully utilizing the error distribution laws of expressions and the distribution characteristics of floating-point numbers to guide the search space generation and improve the search efficiency. This paper selects 28 expressions from the FPBench standard set as test cases, uses PSOED to detect the maximum error of the expressions, and compares them to the current dynamic error detection tools S3FP and Herbie. PSOED detects the maximum error 100% better than S3FP, 68% better than Herbie, and 14% equivalent to Herbie. The results of the experiments indicate that PSOED can detect significant floating-point expression errors.

With the wide application of drone technology, there is an increasing demand for the detection of radar return signals from drones. Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition. This method suffers from the problem of large dimensionality of image features, which leads to large input data size and noise affecting learning. Therefore, this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512 × 4 to 16 dimensions. However, the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease, so we propose a new hybrid quantum neural network with signal feature overlay projection (HQNN-SFOP), which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal, introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features, and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features, thus improving the accuracy and migration generalization ability of drone detection. In order to validate the effectiveness of the proposed method, we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment. The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB. On the time-domain feature data set, HQNN-SFOP obtains the highest accuracy compared to other conventional methods. In addition, HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions. Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing, it provides a highly efficient method for the drone detection using radar return signals. [ABSTRACT FROM AUTHOR]

Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representations. However, due to limitations in code representation and neural network design, the validity and practicality of the model still need to be improved. Additionally, due to differences in programming languages, most methods lack cross-language detection generality. To address these issues, in this paper, we analyze the shortcomings of previous code representations and neural networks. We propose a novel hierarchical code representation that combines Concrete Syntax Trees (CST) with Program Dependence Graphs (PDG). Furthermore, we introduce a Tree-Graph-Gated- Attention (TGGA) network based on gated recurrent units and attention mechanisms to build a Hierarchical Code Representation learning-based Vulnerability Detection (HCRVD) system. This system enables cross-language vulnerability detection at the function-level. The experiments show that HCRVD surpasses many competitors in vulnerability detection capabilities. It benefits from the hierarchical code representation learning method, and outperforms baseline in cross-language vulnerability detection by 9.772% and 11.819% in the C/C++ and Java datasets, respectively. Moreover, HCRVD has certain ability to detect vulnerabilities in unknown programming languages and is useful in real open-source projects. HCRVD shows good validity, generality and practicality. [ABSTRACT FROM AUTHOR]

Compiling for distributed-memory architectures comprise two main phases. The first phase is to determine computation and data composition. In the 1990s, a great deal of work addressed this problem. The second phase is code generation. However, there is still no effective solution to this problem. Existing methods try to generate codes on the basis of computation and data composition. To enhance the performance of generated codes, various communication optimizations are introduced since communication is one of the main factors degrading the performance. These approaches would bring redundant communication data, as they did not optimize communications jointly with code generation. In this paper, we propose a novel code generation technique for distributed-memory architectures. First, we determine the communication sender and receiver by traversing a loop-based tree structure. To support message aggregation, we find the most appropriate point to insert a message. Secondly, we construct the communication set by proposing some code generation rules, and prove their correctness and accuracy. Redundant communication is thus eliminated. Also, we have evaluated some programs ranging from micro-kernels to applications in NAS parallel benchmarks, and have compared the performance with their message passing interface (MPI), High Performance Fortran (HPF) and Unified Parallel C (UPC) versions. Compared with these versions, our compiler can generate fewer communication points. The generated codes of outperform the HPF and UPC versions and the state-of-the-art, and the average performance can reach 70% of the hand-coded MPI programs. [ABSTRACT FROM AUTHOR]