Simulating QAOA operation using Cirq and qsim quantum frameworks
- 作者: Palii Y.G.1, Bogolubskaya A.A.1, Yanovich D.A.1
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隶属关系:
- Joint Institute for Nuclear Research
- 期: 卷 33, 编号 4 (2025)
- 页面: 440-460
- 栏目: Physics and Astronomy
- URL: https://journal-vniispk.ru/2658-4670/article/view/356904
- DOI: https://doi.org/10.22363/2658-4670-2025-33-4-440-460
- EDN: https://elibrary.ru/HYWEXV
- ID: 356904
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The problem of finding the lowest-energy state in the Ising model with a longitudinal magnetic field is studied for two- and three-dimensional lattices of various sizes using the Quantum Approximate Optimization Algorithm (QAOA). The basis states of the quantum computer register correspond to spin configurations on a spatial lattice, and the Hamiltonian of the model is implemented using a sequence of quantum gates. The average energy value is efficiently measured using the Hadamard test. We simulate the QAOA operation on increasingly complex lattice configurations using the software libraries Cirq and qsim. The results of optimization, obtained using gradient-based and gradient-free methods, demonstrate the superiority of the latter in both modeling performance and quantum computer usage. Key arguments in favor of the advantages of quantum computation for this problem are presented.
作者简介
Yuri Palii
Joint Institute for Nuclear Research
Email: palii@jinr.ru
ORCID iD: 0000-0001-9000-9794
Candidate of Physical and Mathematical Sciences, Senior Researcher of Laboratory of Information Technologies
俄罗斯联邦, 6 Joliot-Curie St, Dubna, 141980, Russian FederationAlla Bogolubskaya
Joint Institute for Nuclear Research
Email: abogol@jinr.ru
ORCID iD: 0000-0003-4356-8336
Scopus 作者 ID: 6508333497
Candidate of Physical and Mathematical Sciences, Senior Researcher of Laboratory of Information Technologies
俄罗斯联邦, 6 Joliot-Curie St, Dubna, 141980, Russian FederationDenis Yanovich
Joint Institute for Nuclear Research
编辑信件的主要联系方式.
Email: yan@jinr.ru
Senior Researcher of Laboratory of Information Technologies
俄罗斯联邦, 6 Joliot-Curie St, Dubna, 141980, Russian Federation参考
- Pedersen, J. W., Lamm, H., Lawrence, S. & Yeter-Aydeniz, K. Quantum Simulation of Finite Temperature Schwinger Model via Quantum Imaginary Time Evolution. Phys. Rev. D 108, 114506. doi: 10.1103/PhysRevD.108.114506. arXiv: 2304.01144 [hep-lat] (2023).
- Jordan, S. P., Lee, K. S. M. & Preskill, J. Quantum Algorithms for Quantum Field Theories. Science 336, 1130-1133. doi: 10.1126/science.1217069. arXiv: 1111.3633 [hep-th] (2012).
- Abhijith, J. et al. Quantum Algorithm Implementations for Beginners. Quantum 6, 881. doi: 10.22331/q-2022-12-22-881. arXiv: 1804.03719 [cs.ET] (2022).
- Bharti, K. et al. Noisy intermediate-scale quantum (NISQ) algorithms. Rev. Mod. Phys. 94, 015004. doi: 10.1103/RevModPhys.94.015004. arXiv: 2101.08448 [quant-ph] (2022).
- Hidary, J. D. Quantum Computing: An Applied Approach 2nd. doi: 10.1007/978-3-030-23927-6 (Springer Nature Switzerland AG, Cham, Switzerland, 2021).
- Farhi, E., Goldstone, J. & Gutmann, S. A Quantum Approximate Optimization Algorithm 2014. arXiv: 1411.4028 [quant-ph].
- Lotshaw, P. C. et al. Simulations of Frustrated Ising Hamiltonians using Quantum Approximate Optimization. Phil. Trans. R. Soc. Lond. A 381, 20210414. doi: 10.1098/rsta.2021.0414. arXiv: 2206.05343 [quant-ph] (2022).
- Ozaeta, A., van Dam, W. & McMahon, P. L. Expectation Values from the Single-Layer Quantum Approximate Optimization Algorithm on Ising Problems. Quantum Sci. Technol. 7, 045036. doi: 10.1088/2058-9565/ac88d4. arXiv: 2012.0342 [quant-ph] (2022).
- AI, G. Q. Cirq: Quantum approximate optimization algorithm for the Ising model https://quantumai.google/cirq/experiments/qaoa/qaoa_ising. Accessed: 2024-04-01. 2023.
- Bengtsson, I. & Życzkowski, K. Geometry of Quantum States.Introduction to Quantum Entanglement Second. doi: 10.1017/9781139030710 (Cambridge University Press, Cambridge, UK, 2017).
- Palii, Y., Bogolubskaya, A. & Yanovich, D. Quantum Approximation Optimization Algorithm for the Ising Model in an External Magnetic Field. Phys. Part. Nucl. 55, 600-602. doi: 10.1134/S1063779624040185 (2024).
- Belyakov, D. V., Bogolyubskaya, A. A., Zuev, M. I., Palii, Y. G., Podgajny, D. V., Streltsova, O. I. & Yanovich, D. A. Polygon for quantum computing on the heterogeneous HybriLIT platform Russian. in Proc. of the International Conference “Information Technologies and Mathematical Methods” (ITTMM-2024) (In Russian) (2024), 303.
- Corporation, N. NVIDIA cuStateVec: A High-Performance Library for Quantum Circuit Simulation https://docs.nvidia.com/cuda/cuquantum/custatevec/index.html. Accessed: 2024-04-01. 2023.
- Bayraktar, H. et al. NVIDIA cuQuantum SDK, Accelerate quantum computing research 2023. arXiv: 2308.01999 [quant-ph].
- Powell, M. J. D. A direct search optimization method that models the objective and constraint functions by linear interpolation. Advances in Optimization and Numerical Analysis (eds Gomez, S. & Hennart, J.-P.) 51-67. doi: 10.1007/978-94-015-8330-5_4 (1994).
- Powell, M. J. D. An efficient method for finding the minimum of a function of several variables without calculating derivatives.Comput. J. 7, 155-162. doi: 10.1093/comjnl/7.2.155 (1964).
- Nelder, J. A. & Mead, R. A simplex method for function minimization.Comput. J. 7, 308-313. doi: 10.1093/comjnl/7.4.308 (1965).
- Xiang, Y., Sun, D. Y., Fan, W. & Gong, X. G. Generalized simulated annealing algorithm and its application to the Thomson model. Phys. Lett. A 233, 216-220. doi: 10.1016/S0375-9601(97)00484-1 (1997).
- Endres, S. C., Sandrock, C. & Focke, W. W. A simplicial homology algorithm for Lipschitz optimization. J. Glob. Optim. 72, 181-217. doi: 10.1007/s10898-018-0645-y (2018).
- Palii,Y., Belyakov, D., Bogolubskaya, A., Zuev, M. &Yanovich, D. Simulation of the QAOA algorithm at the JINR quantum testbed Russian. in Proc. of the International Conference “Mathematical Problems in Quantum Information Technologies” (MPQIT 2024) In press (Dubna, JINR, 2024).
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