计算机科学 ›› 2026, Vol. 53 ›› Issue (1): 353-362.doi: 10.11896/jsjkx.241100181
张兴兰, 容潇军
ZHANG Xinglan, RONG Xiaojun
摘要: 离散对数问题是数论中的一个重要问题,因其求解困难,经典计算机没有高效的算法可以解决这一难题,故离散对数问题被广泛用于公钥密码体系中,而一旦离散对数问题被破解,将直接威胁密码系统的安全。但随着量子计算理论的引入,人们开始考虑采用量子计算机解决离散对数问题。目前求解离散对数问题的量子算法基本都基于Shor算法,但Shor算法由于自身的局限性,大多存在量子线路深度过大、使用量子比特数过多、后处理步骤复杂等问题,Shor算法难以在有噪声的中等规模量子(Noisy Intermediate-scale Quantum,NISQ)计算机上实现。为了解决这些问题,提出了基于变分量子的离散对数求解算法。首先,利用量子计算的并行性来计算参数化量子态的模幂,并设计标记解线路,将符合离散对数问题的解映射到辅助位上。然后,通过经典优化器不断对含参量子线路中的参数进行调整,使设计好的损失函数不断降低。最后,将经典优化器调整后的参数提出,并放入测量线路中进行测量,即可以较高的概率得到离散对数问题的解。与Shor算法相比,基于变分量子的离散对数求解算法减少了所需量子比特,同时将量子线路的深度减小了近一半。此外,还给出了详细的量子线路设计并用Python中的Qiskit包验证了所提算法的正确性。
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| [1]FEYNMAN R P.Simulating physics with computers[J].International Journal of Theoretical Physics,1982,21(6):467-488. [2]DEUTSCH D,JOZSA R.Rapid solution of problems by quantum computation[C]//Proceedings of the Royal Society of London.1992:553-558. [3]SHOR P W.Algorithms for quantum computation:discrete logarithms and factoring[C]//Proceedings 35th Annual Sympo-sium on Foundations of Computer Science.2002:124-134. [4]GROVER L K.A Fast Quantum Mechanical Algorithm for Database Search[C]//Proceedings of the 28th Annual ACM Symposium on Theory of Computing.1996:212-219. [5]HARROW A W,HASSIDIM A,LLOYD S.Quantum Algo-rithm for Linear Systems of Equations[J].Physical Review Letters,2009,103(15):150502. [6]OHZEKI M.Message-passing algorithm of quantum annealing with nonstoquastic Hamiltonian[J].arXiv:1901.06901,2019. [7]LLOYD S,MOHSENI M,REBENTROST P.Quantum principal component analysis[J].Nature Physics,2014,10(9):631-633. [8]GUOFENG Z,HAMDULLA A.Adaptive Morphological Contrast Enhancement Based on Quantum Genetic Algorithm for Point Target Detection[J].Mobile Networks and Applications,2021,26(2):638-648. [9]DIFFIE W,HELLMAN M.New directions in cryptography[J].IEEE Transactions on Information Theory,1976,22(6):644-654. [10]ELGAMAL T.A Public Key Cryptosystem and a SignatureScheme Based on Discrete Logarithms[J].IEEE Transactions on Information Theory,1985,31(4):469-472. [11]POHLIG S,HELLMAN M.An improved algorithm for computing logarithms over GF(p) and its cryptographic significance[J].IEEE Transactions on Information Theory,1978,24(1):106-110. [12]NIELSEN M A,CHUANG I L.Quantum Computation andQuantum Information[M]//Cambridge:Cambridge University Press,2010. [13]FU X Q,BAO W S,WANG S.Discrete Logarithm Quantum Computing Algorithm on Z_N[J].Chinese Journal of Compu-ters,2014,37(5):1058-1062. [14]EKERÅ M,HÅSTAD J.Quantum Algorithms for Computing Short Discrete Logarithms and Factoring RSA Integers[J].ar-Xiv:1702.00249,2017. [15]AONO Y,LIU S,TANAKA T,et al.The Present and Future of Discrete Logarithm Problems on Noisy Quantum Computers[J].arXiv:2111.06102,2021. [16]WROŃSKI M.Practical Solving of Discrete Logarithm Problem over Prime Fields Using Quantum Annealing[C]//Computational Science-ICCS 2022.2022:93-106. [17]PRESKILL J.Quantum Computing in the NISQ era and beyond[J].arXiv:1801.00862,2018. [18]ORTS F,FILATOVAS E,ORTEGA G,et al.Improving thenumber of t gates and their spread in integer multipliers on quantum computing[J].Physical Review A,2023,107(4):042621. [19]CEREZO M,ARRASMITH A,BABBUSH R,et al.Variational Quantum Algorithms[J].Nature Reviews Physics,2021,3(9):625-644. [20]ZHANG X L,ZHANG F.Variational Quantum ComputationInteger Factorization Algorithm[J].International Journal of Theoretical Physics,2023,62(11):245-264. [21]CROSS A.The IBM Q Experience and Qiskit Open-SourceQuantum Computing Software[C]//APS March Meeting Abstracts.2018. [22]SCHULD M.Supervised quantum machine learning models are kernel methods[J].arXiv:2101.11020,2021. [23]ARAUJO I F,PARK D K,LUDERMIR T B,et al.Configurable sublinear circuits for quantum state preparation[J].Quantum Information Processing,2023,22(2):123-149. [24]HAVLÍČEK V,CÓRCOLES A,TEMME K,et al.Supervisedlearning with quantum-enhanced feature spaces[J].Nature,2019,567(7747):209-212. [25]LAROSE R,COYLE B.Robust data encodings for quantumclassifiers[J].Physical Review A,2020,102(3):032420. [26]SIM S,JOHNSON P D,ASPURU-GUZIK A.Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms[J].arXiv:1905.10876,2019. [27]KANDALA A,MEZZACAPO A,TEMME K,et al.Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets[J].Nature,2017,549(7671):242-264. [28]SCHULD M,BERGHOLM V,GOGOLIN C,et al.Evaluating analytic gradients on quantum hardware[J].Physical Review A,2019,99(3):032331. [29]STOKES J,IZAAC J,KILLORAN N,et al.Quantum Natural Gradient[J].arXiv:1909.02108,2019. [30]GACON J,ZOUFAL C,CARLEO G,et al.Simultaneous Per-turbation Stochastic Approximation of the Quantum Fisher Information[J].arXiv:2103.09232,2021. [31]POWELL M J D.A Direct Search Optimization Method That Models the Objective and Constraint Functions by Linear Interpolation[M]//Dordrecht:Springer Netherlands,1994. [32]BEAUREGARD S.Circuit for Shor’s algorithm using 2n+3 qubits[J].Quantum Information & Computation,2003,3(2):175-185. [33]DRAPER T G.Addition on a Quantum Computer[J].arXiv:quant-ph/0008033,2000. [34]LARASATI H T,KIM H.Simulation of Modular Exponentiation Circuit for Shor's Algorithm in Qiskit[C]//2020 14th International Conference on Telecommunication Systems,Services,and Applications(TSSA).2020:1-7. [35]BARENCO A,BENNETT C H,CLEVE R,et al.Elementary gates for quantum computation[J].Physical Review A,1995,52(5):3457-3467. [36]SILVA A J D,PARK D K.Linear-depth quantum circuits formultiqubit controlled gates[J].Physical Review A,2022,106(4):042602. [37]XIULI S,LIANGSEN W.An improved circuit for Shor’s factoring algorithm using 2n+2 qubits[J].Quantum Information Processing,2023,22(11):402-418. [38]ORTIZ M C,KIEFEROVÁ M,WIEBE N.Entanglement-in-duced barren plateaus[J].PRX Quantum,2021,2(4):040316. [39]SKOLIK A,MCCLEAN J R,MOHSENI M,et al.Layerwise learning for quantum neural networks[J].Quantum Machine Intelligence,2021,3:1-11. [40]CAMPOS E,NASRALLAH A,BIAMONTE J.Abrupt transitions in variational quantum circuit training[J].Physical Review A,2021,103(3):032607. |
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