基于数理学科竞赛的创新人才培养模式研究与实践

以培养拔尖创新人才顶端需求为目标,基于数理学科竞赛培养体系,系统优化基于OBE理念的“一二三四”课程教学内容和教学模式改革,增加学科竞赛的内涵。建设新型“两体系三改革”交互的学科竞赛平台,促进学生综合素质及能力的培养。探索并实践“后竞赛”时期学生科研活动的管理机制,“两层次四环节”地将竞赛对学生的影响延伸到参加科研项目和国创项目、发表专利和论文等研究活动中。

基于交替量子随机行走的改进AES和离散余弦变换的图像加密算法

随着科学技术手段飞速发展,图像加密算法被破解的风险也逐渐增加.AES(advanced encryption standard)算法虽然性能优越,但也存在着密钥固定等问题,未来量子计算机的问世将会对此造成重大威胁.首先,该文采用离散余弦变换(discrete cosine transform,DCT)对明文图像进行DCT域的变换;其次,利用交替量子随机行走构造概率分布矩阵,对概率分布矩阵进行预处理,与DCT后的明文进行逐位异或的操作;最后,计算概率分布矩阵的奇异值,并给出对应的十六进制表达式作为AES的密钥,随机提取概率分布矩阵的一个元素,按照AES算法的编码规则构建轮常量Rcon,进而改进AES算法的加密迭代函数.测试结果表明,该算法具有很好的加密效果和较高的鲁棒性,密文图像相邻像素在3个方向上的相关系数可以达到0.0015的显著效果.

数值分析“四项融合”教学改革研究与实践

该文通过分析数值分析教学中存在的“痛点”问题,教学团队遵循持续改进的教育教学理念,充分利用MOOC、智慧教室等教学资源,构建“创新教学内容、混合教学模式、实施思政育人、改进评价体系”的“四项融合”创新举措。通过创新教学实践,培养学生课前深度预习,营造自主、合作、探究式的学习环境,提高科学计算素质,掌握编程能力,能够运用所学理论、方法和技能进行工程问题的表达、建模及分析计算能力,全面提升学生的实践创新能力。

数学类竞赛融入公共数学课堂教学研究与实践

在高等院校中开展大学生数学类竞赛是一种素质教育,通过介绍目前我国高校数学类竞赛的基本情况,探讨数学类竞赛在培养大学生创新素质及推动课程改革和建设等方面起到的积极作用,并根据实际教学研究和体会给出了数学类竞赛融入公共数学课堂教学的研究途径和内容,最后引出数学类竞赛在高等数学的案例应用。结果表明:将数学类竞赛融入公共数学教学,有助于提高学生的学习兴趣和积极性,提高创新和科研能力。

大学生爱、协作与分享精神的作用及塑造

大学生是高等教育研究和改革的主体,为让其从中华优秀传统文化中汲取营养,建立爱、协作与分享精神对高等教育创新人才培养促进作用的理论模型。从引导大学生发挥主观能动性出发,培育和引导其塑造中华优秀传统文化延伸而来的爱、协作与分享精神,探索在社会发展新形势下的高等教育内涵建设改革新途径,从而紧跟社会需求,因材施教,提高教育教学质量,培养适应社会需求的创新型人才,破解高等教育大众化大趋势下的就业难与就业质量低等迫切需要解决的问题。

基于量子长短期记忆网络的量子图像混沌加密方案

近年来,图像信息的传输安全性已经成为互联网领域的重要研究方向.本文提出了一种基于量子长短期记忆(quantum long-short term memory,QLSTM)网络的量子图像混沌加密方案.结果发现,因为QLSTM网络具有复杂的结构和较多的参数,应用QLSTM网络对Lorenz混沌序列进行改进,其最大Lyapunov指数比原序列提高2.5465%,比经典长短期记忆(long-short term memory,LSTM)网络改进的序列提高0.2844%,同时在0—1测试中结果更接近1且更稳定,因此QLSTM网络改进的序列具备更优异的混沌性能,更难以被预测,提高了单一混沌系统加密的安全性.运用NCQI(novel quantum representation of color digital images)量子图像表示模型,将原始图像存储为量子态形式,利用QLSTM网络改进的序列分别控制三级径向扩散、量子广义Arnold变换和量子W变换,改变量子图像的灰度值与像素位置,生成最终的加密图像.本文提出的加密方案在统计学特性测试中,实现了RGB三通道平均信息熵均大于7.999,像素数改变率的平均值达99.6047%,统一平均变化强度的平均值为33.4613%,平均相关性为0.0038等,比其他一些传统方法具有更高的安全性,能够抵抗常见的攻击方式.

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

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.

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

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)computing.In 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 methods.Our 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,respectively.Additionally,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,respectively.These results demonstrate the robustness of the model in mitigating readout errors and provide a highly fault tolerant mechanism for image generation models.

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

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.

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

Quantum error correction is a crucial technology for realizing quantum computers.These computers achieve faulttolerant quantum computing by detecting and correcting errors using decoding algorithms.Quantum 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 models.In 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 increases.Our decoding algorithm is based on renormalization group decoding and recurrent neural network decoder.The recurrent neural network is trained through the ResNet architecture to improve its decoding accuracy.Then 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,respectively.Our 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.

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

We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum circuits.In 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 images.At 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 classification.Finally,we use the MNIST dataset to test the potential of the HQCCNN.The results indicate that the HQCCNN has good performance in solving classification problems.In 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.

基于交替量子随机行走的高维量子图像加密模型

图像加密是保证数字图像在互联网上进行安全传输的关键环节。文中提出了一种基于交替量子随机行走(AQW)与异或操作的彩色量子图像加密算法。首先,对原始彩色图像进行彩色量子图像表示法(NCQI)表示,构建量子线路,利用受控AQW生成的二进制概率矩阵构建量子异或矩阵,完成对原始量子图像的加密操作,解密过程是加密过程的逆过程。AQW的初始条件是保证量子图像加密算法安全性的关键。通过对提出的加密模型进行经典计算机上的仿真实验,验证了该量子图像加密模型的安全性。

Latin方阵和二维量子漫步相结合的图像加密

针对数字图像在网络传输过程中容易受到攻击导致信息泄漏的问题,在Arnold置乱变换的基础上引入量子漫步和Latin方阵,设计了一种新型的彩色图像加密方案。首先把彩色图像的三通道分离,用Arnold变换来对图像像素点置乱,量子漫步和Latin方阵被用来处理置乱后图像的像素值,然后对处理之后的图像使用加取模扩散算法进一步改变图像的像素值,最后合并三通道加密图像得到彩色加密图像。量子漫步的作用是为该文的加密算法提供随机序列。该文对加密算法进行了实验仿真,在直方图、相关性、信息熵、噪声攻击、裁剪攻击等多个方面对实验结果进行了性能分析。仿真结果显示:加密图像的直方图均匀分布,像素点之间的相关性趋近于0,信息熵为7.9993,接近于8,说明该算法具有较不错的抵抗统计分析的能力;经过噪声攻击和裁剪攻击之后的加密图像经过解密之后,仍然可以看到原图像信息,表明该算法的鲁棒性是良好的;该加密算法的密钥空间足够大为1060且密钥敏感性良好,NPCR值接近于99.61%,UACI值接近于33.45%,证明该算法拥有抵抗差分攻击的能力。

工科类院校导师团队制协同育人模式的理论与实践研究

在新时代新工科大背景下,社会对人才培养质量特别是对研究生在提升知识创新和实践创新能力、增强使命感及责任感等方面提出了更高要求。然而,单一导师制的常规培养模式存在导师专业领域局限性大、研究生培养质量稳定性差等问题。文章从研究生人才培养存在的问题出发,结合工科类高校导师团队制协同育人模式的培养理念,构建研究生培养模式的理论框架,并探索其在提高研究生创新能力、促进人才全面发展等方面的实践路径。

基于量子纠错码的小型量子网络路由通信协议

在小型量子网络中采用量子隐形传态通信从物理机制上保证通信信息的绝对安全,但是由于量子信道存在噪声,干扰信息的正确性从而产生误码.为保证通信信息的可靠性,本文提出基于量子纠错码的小型量子网络路由通信协议.根据小型量子网络的路由特点构建路由表;依据路由表实现源量子节点到一跳、两跳目的量子节点的量子隐形传态;利用量子纠错码纠正因噪声产生的误码信息;对该协议的安全性进行理论证明.

噪声情况下的量子网络直接通信

提出和研究了噪声情况下的量子网络直接通信.通信过程中所有量子节点共享多粒子GreenbergerHorne-Zeilinger(GHZ)量子纠缠态;发送节点将手中共享的GHZ态的粒子作为控制比特、传输秘密信息的粒子作为目标比特,应用控制非门(CNOT)操作;每个接收节点将手中共享GHZ态的粒子作为控制比特、接收到的秘密信息粒子作为目标比特,再次应用CNOT门操作从而获得含误码的秘密信息.每个接收节点从秘密信息中提取部分作为检测比特串,并将剩余的秘密信息应用奇偶校验矩阵纠正其中存在的比特翻转错误,所有接收节点获得纠正后的秘密信息.对协议安全、吞吐效率、通信效率等进行了分析和讨论.

无线网络中基于量子隐形传态的鲁棒安全通信协议

该文在深入研究无线网络802.11i鲁棒安全通信的基础上,提出基于量子隐形传态的无线网络鲁棒安全通信协议,利用量子纠缠对的非定域关联性保证数据链路层的安全。首先,对量子隐形传态理论进行描述,并着重分析临时密钥完整性协议和计数器模式及密码块链消息认证协议的成对密钥、组密钥的层次结构;其次,给出了嵌入量子隐形传态的成对密钥、组密钥的层次结构方案;最后,在理论上给出安全证明。该协议不需要变动用户、接入点、认证服务器等基础网络设备,只需增加产生和处理纠缠对的设备,即可进行量子化的密钥认证工作,网络整体框架变动较小。

三态纠缠的可控的量子秘密共享协议

提出由3个节点组成的星型量子网络中,基于三态纠缠的可控的量子秘密共享协议。在协议中,3个节点S1、S2、S3共享2N个GHZ-like states,发送编码序列,利用可控制的状态参量α,将测量结果的联合计算共享密钥。理论分析证明,该协议对于外在的窃听者Eve和内在的窃听者都具有很高的安全性。如果存在窃听者,则必然发现,从而保证了共享量子密钥的安全性。

基于GHZ的多用户量子网络密钥传输协议

提出了在一个多用户量子网络中,基于3个粒子最大纠缠态GHZ的密钥传输协议。这个量子密钥传输协议在通信节点和控制中心之间通过多个GHZ对构建安全的密码分配系统。与经典的量子密码术相比,理论分析证明,如果存在窃听者Eve,则他为获得有用的信息会不断向网络引入错误。该网络的节点和控制中心必然发现Eve,从而保证了网络密钥的安全性。

基于循环码和信息压缩融合的量子保密通信算法

针对经典保密通信中信息安全传输的问题,提出了一种基于循环码和信息压缩的量子保密通信算法。首先,发送端对传输的信息进行预处理,将其分割为长度不等的2组数据,分别用于循环编码和压缩编码。然后,发送端添加一串量子态传输至接收端,采用误码数作为信道安全检测的依据,若信道安全,则对预处理后的数据量子态处理,利用量子稳定子码编码分段并传输,依据稳定字码的特性克服环境引起的误码。最后,接收端接收到量子信息后进行解码,并解循环和解压缩从而获得数据。安全性分析表明,所提量子保密通信算法能较好地抵抗篡改和截断信息的攻击。仿真结果表明,对于数据压缩部分按5分段能获得较好的效果。