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Variational quantum attacks threaten advanced encryption standard based symmetric cryptography

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Science China(Information Sciences) 2022年第10期65卷 31-42页

作者： Zeguo WANG shijie wei Gui-Lu LONG Lajos HANZO State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing Academy of Quantum Information Sciences Beijing National Research Center for Information Science and Technology School of Information Tsinghua University Frontier Science Center for Quantum Information Department of Electronics and Computer Science (ECS) University of Southampton

We propose a variational quantum attack algorithm(VQAA) for classical advanced encryption standard(AES)-like symmetric cryptography, as exemplified by the simplified-data encryption standard(S-DES). In the VQAA, the known ciphertext is encoded as the ground state of a Hamiltonian that is constructed through a regular graph, and the ground state can be found using a variational approach. We designed the ansatz and cost function for the S-DES’s variational quantum attack. It is surprising that sometimes the VQAA is even faster than Grover’s algorithm as demonstrated by our simulation results. The relationships of the entanglement entropy, concurrence, and the cost function are investigated, which indicate that entanglement plays a crucial role in the speedup.

关键词： S-DES VQA ansatz cost function optimization

A Full Quantum Eigensolver for Quantum Chemistry Simulations

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Research 2020年第1期2020卷 111-121页

作者： shijie wei Hang Li GuiLu Long Beijing Academy of Quantum Information Sciences Beijing 100193China State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology and School of Information Tsinghua University Beijing 100084China Frontier Science Center for Quantum Information Beijing 100084China

Quantum simulation of quantum chemistry is one of the most compelling applications of quantum computing.It is of particular importance in areas ranging from materials science,biochemistry,and condensed matter physics.Here,we propose a full quantum eigensolver(FQE)algorithm to calculate the molecular ground energies and electronic structures using quantum gradient descent.Compared to existing classical-quantum hybrid methods such as variational quantum eigensolver(VQE),our method removes the classical optimizer and performs all the calculations on a quantum computer with faster convergence.The gradient descent iteration depth has a favorable complexity that is logarithmically dependent on the system size and inverse of the precision.Moreover,the FQE can be further simplified by exploiting a perturbation theory for the calculations of intermediate matrix elements and obtaining results with a precision that satisfies the requirement of chemistry application.The full quantum eigensolver can be implemented on a near-term quantum computer.With the rapid development of quantum computing hardware,the FQE provides an efficient and powerful tool to solve quantum chemistry problems.

关键词： convergence quantum iteration

Quantum second-order optimization algorithm for general polynomials

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Science China(Physics,Mechanics & Astronomy) 2021年第10期64卷 54-63页

作者： Pan Gao Keren Li shijie wei Gui-Lu Long State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua UniversityBeijing 100084China Shenzhen JL Computational Science and Applied Research Institute Shenzhen 518109China Beijing Academy of Quantum Information Sciences Beijing 100193China Beijing National Research Center for Information Science and Technology and School of Information Tsinghua UniversityBeijing 100084China Frontier Science Center for Quantum Information Beijing 100084China

Quantum optimization algorithms can outperform their classical counterpart and are key in modern technology. The second-order optimization algorithm(the Newton algorithm) is a critical optimization method, speeding up the convergence by employing the second-order derivative of loss functions in addition to their first derivative. Here, we propose a new quantum second-order optimization algorithm for general polynomials with a computational complexity of O(poly(log d)). We use this algorithm to solve the nonlinear equation and learning parameter problems in factorization machines. Numerical simulations show that our new algorithm is faster than its classical counterpart and the first-order quantum gradient descent algorithm. While existing quantum Newton optimization algorithms apply only to homogeneous polynomials, our new algorithm can be used in the case of general polynomials, which are more widely present in real applications.

关键词： quantum computation quantum algorithm optimization

Reconstructing unknown quantum states using variational layerwise method

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Frontiers of physics 2022年第5期17卷 137-148页

作者： Junxiang Xiao Jingwei Wen shijie wei Guilu Long State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua UniversityBeijing 100084China Beijing Academy of Quantum Information Sciences Beijing 100193China Frontier Science Center for Quantum Information Beijing 100084China Beijing National Research Center for Information Science and Technology Beijing 100084China

In order to gain comprehensive knowledge of an arbitrary unknown quantum state,one feasible way is to reconstruct it,which can be realized by finding a series of quantum operations that can refactor the unitary evolution producing the unknown state.We design an adaptive framework that can reconstruct unknown quantum states at high fidelities,which utilizes SWAP test,parameterized quantum circuits(PQCs)and layerwise learning strategy.We conduct benchmarking on the framework using numerical simulations and reproduce states of up to six qubits at more than 96%overlaps with original states on average using PQCs trained by our framework,revealing its high applicability to quantum systems of different scales theoretically.Moreover,we perform experiments on a five-qubit IBM Quantum hardware to reconstruct random unknown single qubit states,illustrating the practical performance of our framework.For a certain reconstructing fidelity,our method can effectively construct a PQC of suitable length,avoiding barren plateaus of shadow circuits and overuse of quantum resources by deep circuits,which is of much significance when the scale of the target state is large and there is no a priori information on it.This advantage indicates that it can learn credible information of unknown states with limited quantum resources,giving a boost to quantum algorithms based on parameterized circuits on near-term quantum processors.

关键词： variational quantum algorithm layerwise learning quantum state reconstructing

Preparation and pharmacokinetic study of fenofibrate cubic liquid crystalline

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Asian Journal of Pharmaceutical Sciences 2017年第6期12卷 580-585页

作者： shijie wei Jingbo Ren Ning Li Wuzhen Huo Chongkai Gao Guangdong Provincial Engineering Center of topical precise drug delivery system Guangdong Pharmaceutical UniversityGuangzhouGuangdongChina Department of Pharmaceutics School of PharmacyGuangdong Pharmaceutical UniversityGuangzhouGuangdongChina Research&Development Institute of Chinese Materia Medica Guangdong Pharmaceutical UniversityGuangzhouGuangdongChina

An LCC delivery system for Fenofibrate (Fen) was developed to improve its poorly oral bioavailability. Fen-LCC preparation methods were screened, and the prepared Fen-LCC was then characterized by a polarizing microscope and transmission electron microscopy (TEM).The spray drying technique was selected to dry and solidify particles into powder. The in vitro release of Fen-LCC was measured and in vivo pharmacokinetic experiments were carried out on rats after oral administration. Particles prepared through the high-temperature input method exhibited structural characteristics of LCC, and re-dissolved particles maintained the same features. The LCC delivery system can significantly improve in vitro release outcomes. After oral administration, AUCs of the suspension and LCC systems were measured at 131.6853 μg·h/ml and 1435.72893 μg·h/ml, respectively. The spray drying process presented here better maintains cubic structures, and the LCC system significantly improves bioavailability levels.

关键词： Lipid cubic liquid crystalline Fenofibrate Spray drying Bioavailability

基于运动法的航空发动机高速燃油齿轮泵卸荷槽设计与验证

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推进技术 2024年第5期45卷 197-207页

作者：符江锋赵志杰刘显为魏士杰崔建魏鹏飞西北工业大学动力与能源学院陕西西安710129 中国兵器工业试验测试研究院陕西华阴714200 中国航发西安动力控制科技有限公司陕西西安710077

高压、高速、高温化使得齿轮泵固有困油问题愈发严重,传统卸荷槽结构已难以满足高性能燃油泵设计要求。为此本文提出一种基于运动法的新型卸荷槽结构设计方法,通过构建困油模型并从整泵全局角度分析齿轮参数对困油各项性能的影响规律,... 详细信息

高压、高速、高温化使得齿轮泵固有困油问题愈发严重,传统卸荷槽结构已难以满足高性能燃油泵设计要求。为此本文提出一种基于运动法的新型卸荷槽结构设计方法,通过构建困油模型并从整泵全局角度分析齿轮参数对困油各项性能的影响规律,以确定齿轮参数并为卸荷槽设计提供约束条件;其次基于齿轮运动规律和卸荷槽设计原则进行了某型燃油齿轮泵卸荷槽的设计,并进行了多个工况下新型卸荷槽和传统卸荷槽困油特性仿真对比;最后试制了样机并通过试验验证了卸荷槽设计的有效性。研究结果表明:所设计的卸荷槽与传统矩形卸荷槽相比,同工况下最大困油压力降低63.4 MPa,流量脉动率减小33.5%,空化区域更小,工作性能更优,能够显著缓解困油带来的不利影响。采用该结构卸荷槽的齿轮泵具有高的容积效率和长时抗汽蚀能力。

关键词：航空发动机燃油泵齿轮泵卸荷槽设计困油模型

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轻钢-混凝土组合梁极限承载力研究

粘接 2024年第6期51卷 158-161页

作者：宋胜男管宇姚欣梅卫世杰西安培华学院建筑与艺术设计学院陕西西安710125 长安大学建筑工程学院陕西西安710061

轻钢-混凝土组合梁作为冷弯薄壁型钢结构体系中主要的受力构件,应具备良好的受弯性能以保证竖向荷载的有效传递。参照冷弯薄壁型钢-混凝土组合梁的抗弯试验,建立试验试件有限元分析模型,通过与试验结果对比,验证了有限元建模方法的可靠... 详细信息

轻钢-混凝土组合梁作为冷弯薄壁型钢结构体系中主要的受力构件,应具备良好的受弯性能以保证竖向荷载的有效传递。参照冷弯薄壁型钢-混凝土组合梁的抗弯试验,建立试验试件有限元分析模型,通过与试验结果对比,验证了有限元建模方法的可靠性。基于验证后的有限元模型,对组合梁跨度、楼盖梁跨高比以及楼盖梁高厚比等关键因素进行了变参数分析。结果表明,跨度影响组合梁的破坏模式是受剪控制或是受弯控制的重要因素。在设计组合梁时,建议通过减小跨度、增加楼盖梁高度和厚度的方式来提高组合梁的极限承载能力。

关键词：冷弯薄壁型钢混凝土组合梁极限承载力数值分析

艾司奥美拉唑对对乙酰氨基酚药动学与肠道菌群的影响

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医药导报 2024年第6期43卷 862-866页

作者：贾茹王怡帆陈文华张文萍贺少龙党宏万魏世杰宁夏医科大学总医院药剂科银川750004 宁夏医科大学药学院银川750004 宁夏医科大学总医院药物临床试验机构办公室银川750004

目的探讨质子泵抑制剂(PPI)艾司奥美拉唑(EMZ)对对乙酰氨基酚(APAP)药动学与肠道菌群生态平衡的作用。方法将14只SD大鼠随机分为2组,分别为APAP组、APAP+EMZ组,每组7只;将APAP+EMZ组大鼠置于代谢笼中饲养。APAP+EMZ组灌胃给予EMZ 3.6 mg... 详细信息

目的探讨质子泵抑制剂(PPI)艾司奥美拉唑(EMZ)对对乙酰氨基酚(APAP)药动学与肠道菌群生态平衡的作用。方法将14只SD大鼠随机分为2组,分别为APAP组、APAP+EMZ组,每组7只;将APAP+EMZ组大鼠置于代谢笼中饲养。APAP+EMZ组灌胃给予EMZ 3.6 mg·kg^(-1)·d^(-1),APAP组以等体积0.9%氯化钠溶液代替,连续灌胃14 d;分别在给予EMZ前和给药14 d后收集大鼠粪便进行微生物16SrRNA测序。第15天APAP组和APAP+EMZ组灌胃EMZ后同法给予APAP 44.82 mg·kg^(-1)。超高效液相色谱-串联质谱(UPLC-MS/MS)法测定样品中APAP浓度。计算药动学参数并统计分析,比较APAP组和APAP+EMZ组APAP药动学参数。结果①两组APAP的达峰浓度(C max)比较差异有统计学意义(P0.05);②使用EMZ后乳酸杆菌属、拟杆菌属、梭状芽孢杆菌属和埃希杆菌属相对丰度较用药前减少,而双歧杆菌属增加。但上述菌群相对丰度在EMZ干预前后差异无统计学意义(P>0.05)。结论EMZ与APAP联用时,影响β-葡萄糖醛酸苷酶的菌群相对丰度有一些变化,EMZ对APAP的C_(max)有影响;使用EMZ 2周不通过影响肠道菌群而改变APAP的药动学。

关键词：对乙酰氨基酚艾司奥美拉唑超高效液相色谱-串联质谱药动学肠道菌群

基于检验指标构建急性心肌梗死患者预后不良的列线图预测模型

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检验医学与临床 2024年第14期21卷 2057-2061页

作者：杨茹李保林梁微微王文慧魏世杰北京大学第三医院秦皇岛医院检验科河北秦皇岛066000 河北省秦皇岛市第一医院检验科河北秦皇岛066099 河北省秦皇岛市第一医院综合内科河北秦皇岛066099 河北省秦皇岛市第一医院信息处河北秦皇岛066099

目的基于检验指标分析急性心肌梗死(AMI)患者预后不良的相关危险因素,并构建列线图预测模型。方法回顾性分析2020年9月至2022年8月于北京大学第三医院秦皇岛医院接受经皮冠状动脉介入术(PCI)治疗的300例急性AMI患者临床资料,根据术后... 详细信息

目的基于检验指标分析急性心肌梗死(AMI)患者预后不良的相关危险因素,并构建列线图预测模型。方法回顾性分析2020年9月至2022年8月于北京大学第三医院秦皇岛医院接受经皮冠状动脉介入术(PCI)治疗的300例急性AMI患者临床资料,根据术后随访1年是否发生不良心血管事件分为观察组(发生,61例)和对照组(未发生,239例)。对2组的临床资料进行统计分析;采用多因素Logistic回归分析AMI患者预后不良的独立危险因素;采用R软件构建预测AMI预后的列线图模型;采用校正曲线对预测模型进行内部验证;采用决策曲线评估预测模型的临床净收益。结果观察组冠状动脉病变程度评分(Gensini评分),胱抑素C(CysC)、脑钠肽(BNP)、高敏C反应蛋白(hs-CRP)、高迁移率族蛋白1(HMGB1)水平,以及白细胞计数>10.0×10~9/L的患者比例均高于对照组,差异均有统计学意义(P0.17时,此预测模型在预测AMI患者预后不良风险方面可以提供额外的临床净收益。结论该研究构建了AMI患者预后不良危险因素的列线图预测模型,有助于医护人员认识AMI预后不良的相关因素,尽早制订个性化对策以改善预后。

关键词：急性心肌梗死不良心血管事件预后危险因素列线图预测模型