Quantum circuit-based proxy blind signatures:A novel approach and experimental evaluation on the IBM quantum cloud platform

This paper presents a novel approach to proxy blind signatures in the realm of quantum circuits,aiming to enhance security while safeguarding sensitive information.The main objective of this research is to introduce a quantum proxy blind signature(QPBS)protocol that utilizes quantum logical gates and quantum measurement techniques.The QPBS protocol is constructed by the initial phase,proximal blinding message phase,remote authorization and signature phase,remote validation,and de-blinding phase.This innovative design ensures a secure mechanism for signing documents without revealing the content to the proxy signer,providing practical security authentication in a quantum environment under the assumption that the CNOT gates are securely implemented.Unlike existing approaches,our proposed QPBS protocol eliminates the need for quantum entanglement preparation,thus simplifying the implementation process.To assess the effectiveness and robustness of the QPBS protocol,we conduct comprehensive simulation studies in both ideal and noisy quantum environments on the IBM quantum cloud platform.The results demonstrate the superior performance of the QPBS algorithm,highlighting its resilience against repudiation and forgeability,which are key security concerns in the realm of proxy blind signatures.Furthermore,we have established authentic security thresholds(82.102%)in the presence of real noise,thereby emphasizing the practicality of our proposed solution.

Realization of -bit semiclassical quantum Fourier transform on IBM's quantum cloud computer

To overcome the difficulty of realizing large-scale quantum Fourier transform(QFT) within existing technology, this paper implements a resource-saving method(named t-bit semiclassical QFT over Z_(2~n)), which could realize large-scale QFT using an arbitrary-scale quantum register. By developing a feasible method to realize the control quantum gate Rk, we experimentally realize the 2-bit semiclassical QFT over Z_(2~3) on IBM's quantum cloud computer, which shows the feasibility of the method. Then, we compare the actual performance of 2-bit semiclassical QFT with standard QFT in the experiments.The squared statistical overlap experimental data shows that the fidelity of 2-bit semiclassical QFT is higher than that of standard QFT, which is mainly due to fewer two-qubit gates in the semiclassical QFT. Furthermore, based on the proposed method, N = 15 is successfully factorized by implementing Shor's algorithm.

Quafu-Qcover:Explore combinatorial optimization problems on cloud-based quantum computers

We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.

Quafu-RL:The cloud quantum computers based quantum reinforcement learning

With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.

On Electron Clouds and Light

The wave equation of the electron, recently improved, allows physics to obtain all the quantum numbers and other results explaining the hydrogen spectrum. The Pauli exclusion principle then gives the description of electron clouds used in chemistry. The relativistic wave equation is associated with a Lagrangian density, thus also with an energy-momentum tensorial density. The wave of an electron cloud adds these energy-momentum densities, while photons in light are precisely those differences between such energy-momentum densities.

NMRCloudQ： a quantum cloud experience on a nuclear magnetic resonance quantum computer

Cloud-based quantum computing is anticipated to be the most useful and reachable form for public users to experience with the power of quantum. As initial attempts, IBM Q has launched influential cloud services on a superconducting quantum processor in 2016, but no other platforms has followed up yet. Here,we report our new cloud quantum computing service – NMRCloud Q(http://nmrcloudq.com/zh-hans/),where nuclear magnetic resonance, one of the pioneer platforms with mature techniques in experimental quantum computing, plays as the role of implementing computing tasks. Our service provides a comprehensive software environment preconfigured with a list of quantum information processing packages,and aims to be freely accessible to either amateurs that look forward to keeping pace with this quantum era or professionals that are interested in carrying out real quantum computing experiments in person. In our current version, four qubits are already usable with in average 99.10% single-qubit gate fidelity and 97.15% two-qubit fidelity via randomized benchmaking tests. Improved control precisions as well as a new seven-qubit processor are also in preparation and will be available later.

A Novel Quantum - inspired Multi - Objective Evolutionary Algorithm Based on Cloud Theory

In the previous papers,Quantum-inspired multi-objective evolutionary algorithm(QMEA) was proved to be better than conventional genetic algorithms for multi-objective optimization problem.To improve the quality of the non-dominated set as well as the diversity of population in multi-objective problems,in this paper,a Novel Cloud -based quantum -inspired multi-objective evolutionary Algorithm(CQMEA) is proposed.CQMEA is proposed by employing the concept and principles of Cloud theory.The algorithm utilizes the random orientation and stability of the cloud model,uses a self-adaptive mechanism with cloud model of Quantum gates updating strategy to implement global search efficient.By using the self-adaptive mechanism and the better solution which is determined by the membership function uncertainly,Compared with several well-known algorithms such as NSGA-Ⅱ,QMEA.Experimental results show that(CQMEA) is more effective than QMEA and NSGA -Ⅱ.

量子计算云平台的应用生态建设和发展建议

量子计算技术的发展使超越现有经典计算的强大并行计算成为可能,实用化的量子计算技术将以算力为载体产生广泛而深远的影响。目前,量子计算云平台是输出量子计算能力和探索量子计算应用的主流形式。通过分析国内外量子计算云平台发展和应用现状,提出了依托云平台开展量子计算应用生态建设的新思路,并针对面临的主要挑战提出未来发展建议。

2023年网络安全热点技术领域全景扫描

网络安全离不开防火墙、通信加密和身份识别等技术的支撑,而网络安全技术的每一次迭代升级,都可能因技术优势易手而引起全球网络格局的重新洗牌。正因如此,近年来各国都在积极开发和运用与网络安全有关的新兴技术,以利用先进的网络技术来为军事、经济和舆论等领域的竞争提供助力。对此,分别阐述了人工智能、零信任、云环境、太空网络和量子信息技术这5个网络安全热点技术领域在2023年的发展态势,并分析研判了新兴技术在网络安全应用领域的未来趋势。

基于区块链的量子密钥分发在云存储的应用研究

针对当前量子密钥分发技术在实际应用中存在建设成本极高和难以实现远距离的量子密钥分发等问题,提出了一种基于区块链的量子密钥分发方案。将该方案应用于云存储的数据隐私保护中,利用量子密钥对数据进行加密存储,实现量子密钥的扩展应用。实验结果表明,利用区块链技术能够实现安全且高效的量子密钥分发,相较于传统的量子密钥分发技术,该方案在实际应用中具有显著的优势。

量子网络系统研究进展与关键技术分析

量子信息领域的迅速发展为现代信息技术带来了新的机遇与挑战,其中的热门研究方向之一即量子网络,旨在利用量子力学的基本特性实现长距离的(安全)通信任务,或通过分布式计算提供优于经典计算网络的计算能力,相关研究对推动量子信息的实用化具有重要意义。本文根据量子网络应用场景和技术手段的差异性,从量子密码网络、量子云计算网络、量子隐形传态网络3个细分类别出发,全面梳理了国内外的研究进展及发展挑战,以便掌握量子网络系统的最新发展态势;结合量子网络的实施情况,阐述了量子网络系统发展中亟待攻克的链路建立、信息传输、网络协议、物理硬件等关键技术。综合来看,量子网络仍处于初级发展阶段,当前需积极应对挑战并把握机遇,以增强我国前沿领域的科技硬实力。研究建议,加强基础硬件设施研发投入、重视量子网络理论研究、加强交叉学科研究和相关人才培养,以促进我国量子网络系统的发展。

基于量子密钥的车-云加密通信架构研究

目前主流的车-云安全通信方法是采用公钥保护对称密钥加密数据,但公钥方法计算耗时,且存在被以量子计算为代表的超计算能力快速破解的风险。因此,当前智能网联汽车车-云通信存在着较大的安全隐患。本文将量子密钥应用于对称密钥的保护,提出了一种全新的融合量子密钥的车-云加密通信架构,实现了通过预置密钥进行对称会话密钥的无线分发和BB84协议下对称会话密钥的实时协商。对上述架构进行安全性分析和性能分析,结果表明该架构在威胁场景下对数据具有较高的保护性,通信的平均时延和丢包率控制在16.861 ms和0.0248%范围内,实现了安全高效的车-云通信。

基于量子计算云平台的量子搜索算法实验教学探究

基于近几年量子计算领域的快速发展和各种量子计算云平台的出现,探究了可线上、线下开展基于量子计算云平台的量子搜索算法实验。借鉴“前置课堂”教学模式,准备该实验所涉及的数学和物理学基础知识;围绕搜索问题,设计了三条思维主线组成知识框架,并结合简单实例帮助学生理解抽象的表述和复杂的公式,从而解决知识点较多、算法不易理解的难题;设计了具有启发性、引导性的基础实验和思考题;探究了研究量子搜索算法本身以及算法的实际应用两种类型的拓展实验,为学生今后学习高级量子编程打下基础。

基于四粒子GHZ态的半量子私有比较协议

半量子通信协议允许只具有有限量子能力的参与者进行量子通信,与全量子通信协议相比,减少了量子资源损耗,节约了量子硬件成本。基于半量子模型,以四粒子Greenberger-Horne-Zeilinger(GHZ)态作为量子资源态,提出了一种新型半量子私有比较协议。该协议可以在半诚实第三方的帮助下比较两个经典用户Alice和Bob的信息是否相等,且不泄露他们的秘密信息。安全性分析表明该协议可以抵御内部攻击和外部攻击,与现有的半量子私有比较协议相比,经典用户所需的量子能力更少,只需执行Z基测量和反射接收的粒子,且具有较高的量子比特效率。此外,通过IBM量子云平台进行了仿真实验,验证了所提协议的正确性。

量子计算云平台的架构与发展

量子计算机软硬件尚不成熟,环境要求严苛,运维成本高,无法满足个人用户本地部署的要求,而云平台已成为量子计算科普教育、算法开发以及应用创新的试验床。在介绍量子计算云平台国内外发展现状及其服务模式分类的基础上,总结提出通用功能架构模型,进一步分析量子计算云平台发展前景,并针对面临的主要挑战提出未来发展建议。

基于量子云模型演化的最小属性约简增强算法

为提高决策表中最小属性约简的效率、稳定性和鲁棒性,基于云模型在非规范知识定性、定量表示及其相互转换过程中的优良特征对量子进化算法进行算子设计,提出了一种基于量子云模型演化的最小属性约简增强算法(QCMEARE).该算法采用量子基因云对进化种群进行编码,基于约简属性熵权逆向云进行量子旋转门自适应调整,使其在定性知识指导下能够自适应控制属性约简空间搜索范围,并采用量子云变异和云纠缠操作算子较好地避免了在属性演化约简中易陷入局部最优和早熟收敛等问题,使算法快速搜索到全局最优属性约简集.仿真实验表明,提出的最小属性约简增强算法具有收敛速度快、约简精度高和稳定性强等优点.

基于量子密钥和云服务的身份加密方案

针对现有利用云服务进行基于身份加密(Identity-based encryption,IBE)的公钥加密方案中存在的安全问题,提出了一种基于量子密钥和云服务的IBE方案。该方案首先通过引入量子加密技术解决密钥生成器与云服务之间用户隐私信息和撤销列表等信息安全传输问题;然后利用提出的量子时间令牌机制,安全地对用户私钥进行分发和管理;最后利用量子密钥共享和云服务安全高效地实现了用户撤销管理。与现有方案相比,该方案能够充分利用量子加密技术和云计算服务的优势,更为安全高效地实现基于身份加密公钥体制中的密钥管理和用户撤销,并能够抵抗多种安全威胁,在云计算环境中具有更好的安全性和实用性。在真实的量子密钥分发和云计算环境中的实验结果证明了该方案的可行性和有效性。

基于改进量子遗传算法的云计算资源调度

针对云计算环境下资源的高效调度问题,当前研究较少关注云服务提供商的服务成本,为此,以云服务提供商降低最小服务成本为目的,提出了改进量子遗传算法的云资源调度算法。由于采用二进制量子位表示的染色体无法描述资源调度矩阵,该算法将量子位的二进制编码转换为实数编码,并使用旋转策略和变异算子保证算法的收敛性。通过仿真实验平台将此算法与遗传算法和粒子群算法进行比较分析,在种群迭代次数为100的情况下,分别取种群数为1和10,实验结果表明该算法能取得更小的最小服务成本。

一种基于云模型的改进型量子遗传算法

针对量子遗传算法在函数优化中易陷入局部最优和早熟收敛等缺点,采用云模型对其进行改进,采用量子种群基因云对种群进化进行定性控制,采用基于云模型的量子旋转门自适应调整策略进行更新操作,使算法在定性知识的指导下能够自适应控制搜索空间范围,能在较大搜索空间条件下避开局部最优解。典型函数对比实验表明,该算法可以避免陷入局部最优解,能提高全局寻优能力,同时能以更快的速度收敛于全局最优解,优化质量和效率都要优于遗传算法和量子遗传算法。

基于云模型的自适应量子粒子群算法

利用云模型理论能兼顾随机性和模糊性的特质,提出一种基于云模型的自适应量子粒子群优化算法.首先分析量子粒子群算法的控制机制,在此基础上,使用云算子实现对每个粒子的吸收扩张因子自适应控制,达到在进化过程中对粒子飞行位置动态调整的目的,使算法具有较快的收敛速度和较强的全局搜索能力.同时,补充针对性的优化方案,有效避免算法陷入局部最优.对典型测试函数的仿真对比实验表明,该算法具有寻优能力强、搜索精度高、稳定度好等优点,相比其它同类算法具有一定优势.