A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is...A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is composed of input, phase rotation, aggregation, reversal rotation and output. In this model, the input is described by qubits, and the output is given by the probability of the state in which （1） is observed. The phase rotation and the reversal rotation are performed by the universal quantum gates. Secondly, the quantum BP neural networks model is constructed, in which the output layer and the hide layer are quantum neurons. With the application of the gradient descent algorithm, a learning algorithm of the model is proposed, and the continuity of the model is proved. It is shown that this model and algorithm are superior to the conventional BP networks in three aspects： convergence speed, convergence rate and robustness, by two application examples of pattern recognition and function approximation.展开更多
Due to defects of time-difference of arrival localization,which influences by speed differences of various model waveforms and waveform distortion in transmitting process,a neural network technique is introduced to ca...Due to defects of time-difference of arrival localization,which influences by speed differences of various model waveforms and waveform distortion in transmitting process,a neural network technique is introduced to calculate localization of the acoustic emission source.However,in back propagation（BP） neural network,the BP algorithm is a stochastic gradient algorithm virtually,the network may get into local minimum and the result of network training is dissatisfactory.It is a kind of genetic algorithms with the form of quantum chromosomes,the random observation which simulates the quantum collapse can bring diverse individuals,and the evolutionary operators characterized by a quantum mechanism are introduced to speed up convergence and avoid prematurity.Simulation results show that the modeling of neural network based on quantum genetic algorithm has fast convergent and higher localization accuracy,so it has a good application prospect and is worth researching further more.展开更多
We analyze the development of quantum cryptography communication,including analyze the problems lie in the existent literatures and give the resolve methods according to these problems.Then discuss the quantum cryptog...We analyze the development of quantum cryptography communication,including analyze the problems lie in the existent literatures and give the resolve methods according to these problems.Then discuss the quantum cryptography communication for wireless networks and also point out the shortcoming of current research and the future of quantum wireless networks.展开更多
自然语言处理是实现人机交互的关键步骤,而汉语自然语言处理(Chinese natural language processing,CNLP)是其中的重要组成部分。随着大模型技术的发展,CNLP进入了一个新的阶段,这些汉语大模型具备更强的泛化能力和更快的任务适应性。然...自然语言处理是实现人机交互的关键步骤,而汉语自然语言处理(Chinese natural language processing,CNLP)是其中的重要组成部分。随着大模型技术的发展,CNLP进入了一个新的阶段,这些汉语大模型具备更强的泛化能力和更快的任务适应性。然而,相较于英语大模型,汉语大模型在逻辑推理和文本理解能力方面仍存在不足。介绍了图神经网络在特定CNLP任务中的优势,进行了量子机器学习在CNLP发展潜力的调查。总结了大模型的基本原理和技术架构,详细整理了大模型评测任务的典型数据集和模型评价指标,评估比较了当前主流的大模型在CNLP任务中的效果。分析了当前CNLP存在的挑战,并对CNLP任务的未来研究方向进行了展望,希望能帮助解决当前CNLP存在的挑战,同时为新方法的提出提供了一定的参考。展开更多
随着可再生能源的大规模并网,电力系统频率调节面临前所未有的挑战。本研究提出了一种基于量子增强深度强化学习和时空图神经网络(quantum-enhanced deep reinforcement learning and spatio-temporal graph neural networks,QE-DRL-ST-...随着可再生能源的大规模并网,电力系统频率调节面临前所未有的挑战。本研究提出了一种基于量子增强深度强化学习和时空图神经网络(quantum-enhanced deep reinforcement learning and spatio-temporal graph neural networks,QE-DRL-ST-GNN)的混合储能系统自适应频率调节方法,旨在提高多时间尺度下的电网频率调节性能。该方法创新性地将量子计算与深度强化学习和图神经网络相结合,克服了传统方法在处理高维状态空间和复杂时空依赖性方面的局限性。QE-DRL-ST-GNN采用量子状态编码来表示系统状态,利用量子图的卷积提取时空特征,并通过量子变分算法优化强化学习策略。此外,本研究还设计了一种自适应量子电路生成机制,可以根据系统的动态特性自动调整量子电路结构。案例分析结果表明,与传统的量子增强深度强化学习(quantum-enhanced deep reinforcement learning,QE-DRL)方法相比,QE-DRL-ST-GNN方法在极端情况下频率偏差控制在0.05 Hz,而传统DRL方法为0.15 Hz,提高了66.67%;在调节时间方面,QE-DRL-ST-GNN方法在复杂场景中仅需1.67 s,比传统DRL方法缩短47%;与传统DRL方法的83%相比,QE-DRL-ST-GNN方法在极端情况下提高了13%。展开更多
基金the National Natural Science Foundation of China (50138010)
文摘A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is composed of input, phase rotation, aggregation, reversal rotation and output. In this model, the input is described by qubits, and the output is given by the probability of the state in which （1） is observed. The phase rotation and the reversal rotation are performed by the universal quantum gates. Secondly, the quantum BP neural networks model is constructed, in which the output layer and the hide layer are quantum neurons. With the application of the gradient descent algorithm, a learning algorithm of the model is proposed, and the continuity of the model is proved. It is shown that this model and algorithm are superior to the conventional BP networks in three aspects： convergence speed, convergence rate and robustness, by two application examples of pattern recognition and function approximation.
基金supported by the National Natural Science Foundation of China (51075068)the Southeast University Science Foundation Funded Program (KJ2009348)
文摘Due to defects of time-difference of arrival localization,which influences by speed differences of various model waveforms and waveform distortion in transmitting process,a neural network technique is introduced to calculate localization of the acoustic emission source.However,in back propagation（BP） neural network,the BP algorithm is a stochastic gradient algorithm virtually,the network may get into local minimum and the result of network training is dissatisfactory.It is a kind of genetic algorithms with the form of quantum chromosomes,the random observation which simulates the quantum collapse can bring diverse individuals,and the evolutionary operators characterized by a quantum mechanism are introduced to speed up convergence and avoid prematurity.Simulation results show that the modeling of neural network based on quantum genetic algorithm has fast convergent and higher localization accuracy,so it has a good application prospect and is worth researching further more.
基金supported by the National High Technology Research and Development Program of China(863 Program)(No. 2006AA01Z208)Nataral Sci-ence Foundation of Jiangsu Province(No. BK2007236)Six Projects Sponsoring Talent Summits of Jiangsu Province(No. SJ207001)
文摘We analyze the development of quantum cryptography communication,including analyze the problems lie in the existent literatures and give the resolve methods according to these problems.Then discuss the quantum cryptography communication for wireless networks and also point out the shortcoming of current research and the future of quantum wireless networks.
文摘自然语言处理是实现人机交互的关键步骤,而汉语自然语言处理(Chinese natural language processing,CNLP)是其中的重要组成部分。随着大模型技术的发展,CNLP进入了一个新的阶段,这些汉语大模型具备更强的泛化能力和更快的任务适应性。然而,相较于英语大模型,汉语大模型在逻辑推理和文本理解能力方面仍存在不足。介绍了图神经网络在特定CNLP任务中的优势,进行了量子机器学习在CNLP发展潜力的调查。总结了大模型的基本原理和技术架构,详细整理了大模型评测任务的典型数据集和模型评价指标,评估比较了当前主流的大模型在CNLP任务中的效果。分析了当前CNLP存在的挑战,并对CNLP任务的未来研究方向进行了展望,希望能帮助解决当前CNLP存在的挑战,同时为新方法的提出提供了一定的参考。
文摘密码作为国家重要的战略资源,在网络与信息安全领域发挥着核心作用。近年来,量子计算技术的快速发展,有效推动了学术界、产业界等对后量子密码(post-quantum cryptography,PQC)的研究与应用。基于此,研究了量子计算对经典密码算法的安全威胁,调研了PQC的发展现状,分析了电信领域典型场景的量子安全影响,最后开展了PQC签名算法模块化格基数字签名标准(module-lattice-based digital signature standard,ML-DSA)的迁移验证与测试分析。实验结果表明,ML-DSA算法能够满足系统的高并发、高可用要求。
文摘随着可再生能源的大规模并网,电力系统频率调节面临前所未有的挑战。本研究提出了一种基于量子增强深度强化学习和时空图神经网络(quantum-enhanced deep reinforcement learning and spatio-temporal graph neural networks,QE-DRL-ST-GNN)的混合储能系统自适应频率调节方法,旨在提高多时间尺度下的电网频率调节性能。该方法创新性地将量子计算与深度强化学习和图神经网络相结合,克服了传统方法在处理高维状态空间和复杂时空依赖性方面的局限性。QE-DRL-ST-GNN采用量子状态编码来表示系统状态,利用量子图的卷积提取时空特征,并通过量子变分算法优化强化学习策略。此外,本研究还设计了一种自适应量子电路生成机制,可以根据系统的动态特性自动调整量子电路结构。案例分析结果表明,与传统的量子增强深度强化学习(quantum-enhanced deep reinforcement learning,QE-DRL)方法相比,QE-DRL-ST-GNN方法在极端情况下频率偏差控制在0.05 Hz,而传统DRL方法为0.15 Hz,提高了66.67%;在调节时间方面,QE-DRL-ST-GNN方法在复杂场景中仅需1.67 s,比传统DRL方法缩短47%;与传统DRL方法的83%相比,QE-DRL-ST-GNN方法在极端情况下提高了13%。
