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Chinese Physics B 2024年第4期33卷 683-689页

作者：张丽萍孙宗耀李佳妮苏俊燕 school of sciences Lanzhou University of TechnologyLanzhou 730050China

The instability of plasma waves in the channel of field-effect transistors will cause the electromagnetic waves with THz *** on a self-consistent quantum hydrodynamic model,the instability of THz plasmas waves in the channel of graphene field-effect transistors has been investigated with external magnetic field and quantum *** analyzed the influence of weak magnetic fields,quantum effects,device size,and temperature on the instability of plasma waves under asymmetric boundary conditions *** results show that the magnetic fields,quantum effects,and the thickness of the dielectric layer between the gate and the channel can increase the radiation ***,we observed that increase in temperature leads to a decrease in both oscillation frequency and instability *** numerical results and accompanying images obtained from our simulations provide support for the above conclusions.

关键词： graphene field-effect transistors external magnetic field radiation frequency instability increment

Identifying malicious traffic under concept drift based on intraclass consistency enhanced variational autoencoder

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Science China(Information sciences) 2024年第8期67卷 238-252页

作者： Xiang LUO Chang LIU Gaopeng GOU Gang XIONG Zhen LI Binxing FANG Institute of Information Engineering Chinese Academy of Sciences school of Cyber Security University of Chinese Academy of Sciences school of Computer Science and technology Harbin Institute of Technology (Shenzhen)

Accurate identification of malicious traffic is crucial for implementing effective defense countermeasures and has led to extensive research efforts. However, the continuously evolving techniques employed by adversaries have introduced the issues of concept drift, which significantly affects the performance of existing methods. To tackle this challenge, some researchers have focused on improving the separability of malicious traffic representation and designing drift detectors to reduce the number of false ***, these methods often overlook the importance of enhancing the generalization and intraclass consistency in the representation. Additionally, the detectors are not sufficiently sensitive to the variations among different malicious traffic classes, which results in poor performance and limited robustness. In this paper, we propose intraclass consistency enhanced variational autoencoder with Class-Perception detector(ICE-CP) to identify malicious traffic under concept drift. It comprises two key modules during training:intraclass consistency enhanced(ICE) representation learning and Class-Perception(CP) detector construction. In the first module, we employ a variational autoencoder(VAE) in conjunction with Kullback-Leibler(KL)-divergence and cross-entropy loss to model the distribution of each input malicious traffic flow. This approach simultaneously enhances the generalization, interclass consistency, and intraclass differences in the learned representation. Consequently, we obtain a compact representation and a trained classifier for nondrifting malicious traffic. In the second module, we design the CP detector, which generates a centroid and threshold for each malicious traffic class separately based on the learned representation, depicting the boundaries between drifting and non-drifting malicious traffic. During testing, we utilize the trained classifier to predict malicious traffic classes for the testing samples. Then, we use the CP det

关键词： concept drift malicious traffic identification variational autoencoder intrusion detection cyberspace security

Number of Solitons Emerged in the Initial Profile of Shallow Water Using Convolutional Neural Networks

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Journal of Systems Science & Complexity 2024年第2期37卷 463-479页

作者： WANG Zhen CUI Shikun school of Mathematical sciences Dalian University of TechnologyDalian 116024China school of Mathematical sciences Beihang UniversityBeijing 100191China

The soliton resolution conjecture proposes that the initial value problem can evolve into a dispersion part and a soliton ***,the problem of determining the number of solitons that form in a given initial profile remains unsolved,except for a few specific *** this paper,the authors use the deep learning method to predict the number of solitons in a given initial value of the Korteweg-de Vries(KdV)*** leveraging the analytical relationship between Asech^(2)(x)initial values and the number of solitons,the authors train a Convolutional Neural Network(CNN)that can accurately identify the soliton count from spatio-temporal *** trained neural network is capable of predicting the number of solitons with other given initial values without any additional *** extensive calculations,the authors demonstrate the effectiveness and high performance of the proposed method.

关键词： Convolutional neural network deep learning method KdV equation soliton

Deciphering the role of rGO in tuning dielectric properties of Er_(2)O_(3)/ZnO/rGO nanocomposites synthesized via facile approach

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Journal of Rare Earths 2024年第3期42卷 578-585,I0006页

作者： Saiqa Hafeez Saima Perveen Muhammad Zarrar Khan Awab Hashmi Fahad Azad school of Natural sciences(SNS) National University of Sciences and Technology(NUST)IslamabadPakistan Electronic and Magnetic Materials Laboratory School of Chemical and Materials Engineering(SCME)National University of Sciences and Technology(NUST)IslamabadPakistan

The present research investigated the effect of reduced graphene oxide(rGO) integration with assynthesized Er_(2)O_(3)/ZnO nanocomposites on dielectric properties. The samples were characterized by Xray diffraction(XRD), Fourier transform infrared spectroscopy(FTIR), scanning electron microscopy(SEM), diffuse reflectance spectroscopy(DRS), and energy dispersive spectroscopy(EDS). Complex impedance spectroscopy was utilized to characterize dielectric relaxation phenomena in Er_(2)O_(3)/ZnO and Er_(2)O_(3)/ZnO/rGO nanocomposites. All the samples exhibit single relaxation phenomena which are ascribed to grain boundaries. The addition of rGO to Er_(2)O_(3)/ZnO nanocomposite results in the enhancement of dielectric response due to increased local field intensity caused by the formation of conductinginsulating interfaces. Furthermore, the AC conductivity of the Er_(2)O_(3)/ZnO/rGO is increased at higher frequencies due to the presence of free charge carriers in rGO. At higher frequencies, Er_(2)O_(3)/ZnO/rGO nanocomposite with increased concentration of ZnO exhibits a more stable dielectric constant with extremely low dielectric loss. Hence, the inclusion of rGO in Er_(2)O_(3)/ZnO nanocomposites makes it a novel prospective candidate in the field of energy storage devices.

关键词： Erbium oxide(Er_(2)O_(3)) Rare earths Stable dielectric constant Impedance Polarization Grain boundaries

The Exchange Attack and the Mixture Differential Attack Revisited: From the Perspective of Automatic Evaluation

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Chinese Journal of Electronics 2024年第1期33卷 19-29页

作者： Kexin QIAO Zhiyu ZHANG Zhongfeng NIU Liehuang ZHU school of Cyberspace Science and technology Beijing Institute of Technology State Key Laboratory of Cryptology Institute of Information Engineering Chinese Academy of Sciences school of Cryptography University of Chinese Academy of Sciences

Recent results show that the differential properties within quadruples boom as a new inspiration in cryptanalysis of Advanced Encryption Standard(AES)-like constructions. These methods include the exchange attack proposed in Asiacrypt’19, the mixture differential attack proposed in To SC’18, etc., where the essential properties are obtained by manually scrutinizing the structures of the AES-like constructions. This paper presents a novel framework and an automatic tool based on mixed integer linear programming to search for mixture differential distinguishers for general constructions. This framework considers what equality patterns among quadruples can make a distinguisher and traces how the patterns propagate through cipher components with accurate probability *** this tool, a 5-round AES distinguishing attack with lower complexity and more 6-round distinguishing attacks in the chosen plaintext scenarios are deduced. We prove that no exchange-type or mixture differential distinguisher exists for 7 and above rounds AES if the details of the Sbox and Mix Columns matrix are not taken into account.

关键词： Ciphers Estimation Encryption Mixed integer linear programming Complexity theory Standards

Design of a novel hybrid quantum deep neural network in INEQR images classification

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Chinese Physics B 2024年第6期33卷 230-238页

作者：王爽王柯涵程涛赵润盛马鸿洋郭帅 school of Information and Control Engineering Qingdao University of TechnologyQingdao 266033China school of sciences Qingdao University of TechnologyQingdao 266033China

We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantum circuit, thereby propose a novel hybrid quantum deep neural network(HQDNN) used for image classification. After bilinear interpolation reduces the original image to a suitable size, an improved novel enhanced quantum representation(INEQR) is used to encode it into quantum states as the input of the HQDNN. Multi-layer parameterized quantum circuits are used as the main structure to implement feature extraction and classification. The output results of parameterized quantum circuits are converted into classical data through quantum measurements and then optimized on a classical computer. To verify the performance of the HQDNN, we conduct binary classification and three classification experiments on the MNIST(Modified National Institute of Standards and technology) data set. In the first binary classification, the accuracy of 0 and 4 exceeds98%. Then we compare the performance of three classification with other algorithms, the results on two datasets show that the classification accuracy is higher than that of quantum deep neural network and general quantum convolutional neural network.

关键词： quantum computing image classification quantum–classical hybrid neural network quantum image representation interpolation

Recurrent neural network decoding of rotated surface codes based on distributed strategy

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Chinese Physics B 2024年第4期33卷 322-330页

作者：李帆李熬庆甘启迪马鸿洋 school of Information and Control Engineering Qingdao University of TechnologyQingdao 266033China school of sciences Qingdao University of TechnologyQingdao 266033China

Quantum error correction is a crucial technology for realizing quantum *** computers achieve faulttolerant quantum computing by detecting and correcting errors using decoding *** error correction using neural network-based machine learning methods is a promising approach that is adapted to physical systems without the need to build noise *** this paper,we use a distributed decoding strategy,which effectively alleviates the problem of exponential growth of the training set required for neural networks as the code distance of quantum error-correcting codes *** decoding algorithm is based on renormalization group decoding and recurrent neural network *** recurrent neural network is trained through the ResNet architecture to improve its decoding *** we test the decoding performance of our distributed strategy decoder,recurrent neural network decoder,and the classic minimum weight perfect matching(MWPM)decoder for rotated surface codes with different code distances under the circuit noise model,the thresholds of these three decoders are about 0.0052,0.0051,and 0.0049,*** results demonstrate that the distributed strategy decoder outperforms the other two decoders,achieving approximately a 5%improvement in decoding efficiency compared to the MWPM decoder and approximately a 2%improvement compared to the recurrent neural network decoder.

关键词： quantum error correction rotated surface code recurrent neural network distributed strategy

Analysis of learnability of a novel hybrid quantum-classical convolutional neural network in image classification

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Chinese Physics B 2024年第4期33卷 275-283页

作者：程涛赵润盛王爽王睿马鸿洋 school of sciences Qingdao University of TechnologyQingdao 266033China school of Information and Control Engineering Qingdao University of TechnologyQingdao 266033China

We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum *** this model,we use parameterized quantum circuits(PQCs)to redesign the convolutional layer in classical convolutional neural networks,forming a new quantum convolutional layer to achieve unitary transformation of quantum states,enabling the model to more accurately extract hidden information from *** the same time,we combine the classical fully connected layer with PQCs to form a new hybrid quantum-classical fully connected layer to further improve the accuracy of ***,we use the MNIST dataset to test the potential of the *** results indicate that the HQCCNN has good performance in solving classification *** binary classification tasks,the classification accuracy of numbers 5 and 7 is as high as 99.71%.In multivariate classification,the accuracy rate also reaches 98.51%.Finally,we compare the performance of the HQCCNN with other models and find that the HQCCNN has better classification performance and convergence speed.

关键词： parameterized quantum circuits quantum machine learning hybrid quantum-classical convolutional neural network

Decoding topological XYZ^(2) codes with reinforcement learning based on attention mechanisms

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Chinese Physics B 2024年第6期33卷 262-270页

作者：陈庆辉姬宇欣王柯涵马鸿洋纪乃华 school of Information and Control Engineering Qingdao University of TechnologyQingdao 266033China school of sciences Qingdao University of TechnologyQingdao 266033China

Quantum error correction, a technique that relies on the principle of redundancy to encode logical information into additional qubits to better protect the system from noise, is necessary to design a viable quantum computer. For this new topological stabilizer code-XYZ^(2) code defined on the cellular lattice, it is implemented on a hexagonal lattice of qubits and it encodes the logical qubits with the help of stabilizer measurements of weight six and weight two. However topological stabilizer codes in cellular lattice quantum systems suffer from the detrimental effects of noise due to interaction with the environment. Several decoding approaches have been proposed to address this problem. Here, we propose the use of a state-attention based reinforcement learning decoder to decode XYZ^(2) codes, which enables the decoder to more accurately focus on the information related to the current decoding position, and the error correction accuracy of our reinforcement learning decoder model under the optimisation conditions can reach 83.27% under the depolarizing noise model, and we have measured thresholds of 0.18856 and 0.19043 for XYZ^(2) codes at code spacing of 3–7 and 7–11, respectively. our study provides directions and ideas for applications of decoding schemes combining reinforcement learning attention mechanisms to other topological quantum error-correcting codes.

关键词： quantum error correction topological quantum stabilizer code reinforcement learning attention mechanism

Quantum generative adversarial networks based on a readout error mitigation method with fault tolerant mechanism

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Chinese Physics B 2024年第4期33卷 285-295页

作者：赵润盛马鸿洋程涛王爽范兴奎 school of sciences Qingdao University of TechnologyQingdao 266033China school of Information and Control Engineering Qingdao University of TechnologyQingdao 266033China

Readout errors caused by measurement noise are a significant source of errors in quantum circuits,which severely affect the output results and are an urgent problem to be solved in noisy-intermediate scale quantum(NISQ)*** this paper,we use the bit-flip averaging(BFA)method to mitigate frequent readout errors in quantum generative adversarial networks(QGAN)for image generation,which simplifies the response matrix structure by averaging the qubits for each random bit-flip in advance,successfully solving problems with high cost of measurement for traditional error mitigation *** experiments were simulated in Qiskit using the handwritten digit image recognition dataset under the BFA-based method,the Kullback-Leibler(KL)divergence of the generated images converges to 0.04,0.05,and 0.1 for readout error probabilities of p=0.01,p=0.05,and p=0.1,***,by evaluating the fidelity of the quantum states representing the images,we observe average fidelity values of 0.97,0.96,and 0.95 for the three readout error probabilities,*** results demonstrate the robustness of the model in mitigating readout errors and provide a highly fault tolerant mechanism for image generation models.

关键词： readout errors quantum generative adversarial networks bit-flip averaging method fault tolerant mechanisms