Abstract
This paper addresses classical simulations in the assessment of quantum computing performance. It emphasises the significance of these simulations in understanding quantum systems and exploring the potential of quantum algorithms. The challenges posed by the exponential growth of quantum states and the limitations of full-state simulations are addressed. Various approximation techniques and encoding methods are pointed out to enable simulations of larger quantum systems, and advanced simulation strategies tailored to specific goals are also discussed. This work focuses on the feasibility of classical simulation in decision processes regarding the development of software solutions, extending the assessment beyond high-performance computing systems to include standard hardware. This opportunity can foster the adoption of classical simulations of quantum algorithms to a wider range of users.
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References
Aaronson, S., Gottesman, D.: Improved simulation of stabilizer circuits. Phys. Rev. A 70(5), 052328 (2004)
Albash, T., Lidar, D.A.: Adiabatic quantum computation. Rev. Mod. Phys. 90(1), 015002 (2018)
Angelelli, M., Arima, S., Catalano, C., Ciavolino, E.: Cyber-risk perception and prioritization for decision-making and threat intelligence. arXiv preprint arXiv:2302.08348 (2023)
Arute, F., et al.: Quantum supremacy using a programmable superconducting processor. Nature 574(7779), 505–510 (2019)
Barletta, V.S., Caivano, D., De Vincentiis, M., Magrì, A., Piccinno, A.: Quantum optimization for IoT security detection. In: Julián, V., Carneiro, J., Alonso, R.S., Chamoso, P., Novais, P. (eds.) ISAmI 2022. LNNS, vol. 603, pp. 187–196. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-22356-3_18
Barletta, V.S., Caivano, D., Gigante, D., Ragone, A.: A rapid review of responsible AI frameworks: how to guide the development of ethical AI. In: Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, EASE 2023, pp. 358–367. Association for Computing Machinery, New York (2023). https://doi.org/10.1145/3593434.3593478
Bartlett, S.D., Sanders, B.C.: Efficient classical simulation of optical quantum information circuits. Phys. Rev. Lett. 89(20), 207903 (2002)
Bertels, K., et al.: Quantum computer architecture: towards full-stack quantum accelerators. 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1–6 (2019)
Boixo, S., et al.: Characterizing quantum supremacy in near-term devices. Nat. Phys. 14(6), 595–600 (2018)
Bourassa, J.E., et al.: Blueprint for a scalable photonic fault-tolerant quantum computer. Quantum 5, 392 (2021)
Bravyi, S., Smith, G., Smolin, J.A.: Trading classical and quantum computational resources. Phys. Rev. X 6(2), 021043 (2016)
Burgholzer, L., Ploier, A., Wille, R.: Simulation paths for quantum circuit simulation with decision diagrams what to learn from tensor networks, and what not. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 42(4), 1113–1122 (2022)
Callison, A., Chancellor, N.: Hybrid quantum-classical algorithms in the noisy intermediate-scale quantum era and beyond. Phys. Rev. A 106(1), 010101 (2022)
Cartaxo, B., Pinto, G., Soares, S.: Rapid reviews in software engineering. In: Felderer, M., Travassos, G. (eds.) Contemporary Empirical Methods in Software Engineering, pp. 357–384. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-32489-6_13
Castillo, J.E., Sierra, Y., Cubillos, N.L.: Classical simulation of Grovers quantum algorithm. Rev. Brasil. Ensino Fisica 42, e20190115 (2020)
Catalano, C., Chezzi, A., Angelelli, M., Tommasi, F.: Deceiving AI-based malware detection through polymorphic attacks. Comput. Ind. 143, 103751 (2022)
Catalano, C., Afrune, P., Angelelli, M., Maglio, G., Striani, F., Tommasi, F.: Security testing reuse enhancing active cyber defence in public administration. In: ITASEC, pp. 120–132 (2021)
Chen, Y.T., Farquhar, C., Parrish, R.M.: Low-rank density-matrix evolution for noisy quantum circuits. NPJ Quant. Inf. 7(1), 61 (2021)
De Raedt, K., et al.: Massively parallel quantum computer simulator. Comput. Phys. Commun. 176(2), 121–136 (2007)
Díaz-Pier, S., Venegas-Andraca, S.E.: Classical simulation of quantum adiabatic algorithms using mathematica on GPUs. Int. J. Unconv. Comput. 7, 315–330 (2011)
Gao, X., Duan, L.: Efficient classical simulation of noisy quantum computation. arXiv preprint arXiv:1810.03176 (2018)
Gray, J., Kourtis, S.: Hyper-optimized tensor network contraction. Quantum 5, 410 (2021)
Kadowaki, T., Nishimori, H.: Quantum annealing in the transverse ising model. Phys. Rev. E 58(5), 5355 (1998)
Kyaw, T.H., et al.: Quantum computer-aided design: digital quantum simulation of quantum processors. Phys. Rev. Appl. 16(4), 044042 (2021)
Li, G., Ding, Y., Xie, Y.: Eliminating redundant computation in noisy quantum computing simulation. In: 2020 57th ACM/IEEE Design Automation Conference (DAC), pp. 1–6. IEEE (2020)
Markov, I.L., Shi, Y.: Simulating quantum computation by contracting tensor networks. SIAM J. Comput. 38(3), 963–981 (2008)
Miranskyy, A.V., Khan, M., Faye, J.P.L., Mendes, U.C.: Quantum computing for software engineering: prospects. In: Proceedings of the 1st International Workshop on Quantum Programming for Software Engineering (2022)
Pan, F., Zhang, P.: Simulation of quantum circuits using the big-batch tensor network method. Phys. Rev. Lett. 128(3), 030501 (2022)
Schutski, R., Khakhulin, T., Oseledets, I., Kolmakov, D.: Simple heuristics for efficient parallel tensor contraction and quantum circuit simulation. Phys. Rev. A 102(6), 062614 (2020)
Steijl, R.: Quantum algorithms for fluid simulations. Adv. Quant. Commun. Inf. (2019)
Van Den Nes, M.: Classical simulation of quantum computation, the Gottesman-Knill theorem, and slightly beyond. Quantum Inf. Comput. 10(3), 258–271 (2010)
Viamontes, G.F., Markov, I.L., Hayes, J.P.: Graph-based simulation of quantum computation in the density matrix representation. In: Quantum Information and Computation II, vol. 5436, pp. 285–296. SPIE (2004)
Villalonga, B., et al.: A flexible high-performance simulator for verifying and benchmarking quantum circuits implemented on real hardware. NPJ Quant. Inf. 5(1), 86 (2019)
Zhang, M., Wang, C., Han, Y.: Noisy random quantum circuit sampling and its classical simulation. Adv. Quant. Technol. 2300030 (2023)
Zhong, H.S., et al.: Quantum computational advantage using photons. Science 370(6523), 1460–1463 (2020)
Acknowledgments
Andrea D’Urbano acknowledges the funding received by Deep Consulting s.r.l. within the Ph.D. program in Engineering of Complex Systems.
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D’Urbano, A., Angelelli, M., Catalano, C. (2024). The Significance of Classical Simulations in the Adoption of Quantum Technologies for Software Development. In: Kadgien, R., Jedlitschka, A., Janes, A., Lenarduzzi, V., Li, X. (eds) Product-Focused Software Process Improvement. PROFES 2023. Lecture Notes in Computer Science, vol 14484. Springer, Cham. https://doi.org/10.1007/978-3-031-49269-3_6
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