Abstract
The circuit model is one of the leading quantum computing architectures. In this model, a quantum algorithm is given by a set of quantum gates that must be distributed on the quantum computer over time, subject to a number of constraints. This process gives rise to the Quantum Circuit Compilation Problem (QCCP), which is in fact a hard scheduling problem. In this paper, we consider a compilation problem derived from the general Quantum Approximation Optimization Algorithm (QAOA) applied to the MaxCut problem and consider Noisy Intermediate Scale Quantum (NISQ) hardware architectures, which was already tackled in some previous studies. Specifically, we consider the problem denoted QCCP-X (QCCP with crosstalk constraints) and explore the use of genetic algorithms to solve it. We performed an experimental study across a conventional set of instances showing that the proposed genetic algorithm, termed \(GA_{X}\), outperforms a previous approach.
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Notes
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Note that a single transformation \(z=(\sigma + 1)/2\) converts the variables from the \(\sigma \in \{-1,+1\}\) space to the \(z \in \{0,1\}\) space.
References
Arufe, L., González, M.A., Oddi, A., Rasconi, R., Varela, R.: Quantum circuit compilation by genetic algorithm for quantum approximate optimization algorithm applied to maxcut problem. Swarm Evol. Comput. 69, 101030 (2022)
Booth, K.E., Do, M., Beck, J.C., Rieffel, E., Venturelli, D., Frank, J.: Comparing and integrating constraint programming and temporal planning for quantum circuit compilation. In: Twenty-Eighth International Conference on Automated Planning and Scheduling (ICAPS 2018), pp. 366–374 (2018)
Chand, S., Singh, H.K., Ray, T., Ryan, M.: Rollout based heuristics for the quantum circuit compilation problem. In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 974–981, June 2019
Farhi, E., Goldstone, J., Gutmann, S.: A quantum approximate optimization algorithm (2014)
Oddi, A., Rasconi, R.: Greedy randomized search for scalable compilation of quantum circuits. In: van Hoeve, W.-J. (ed.) CPAIOR 2018. LNCS, vol. 10848, pp. 446–461. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93031-2_32
Preskill, J.: Quantum computing in the NISQ era and beyond. Quantum 2, 79 (2018)
Rasconi, R., Oddi, A.: An innovative genetic algorithm for the quantum circuit compilation problem. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, vol. 33, pp. 7707–7714 (2019)
Sete, E.A., Zeng, W.J., Rigetti, C.T.: A functional architecture for scalable quantum computing. In: 2016 IEEE International Conference on Rebooting Computing (ICRC), pp. 1–6. IEEE (2016)
Venturelli, D., et al.: Quantum circuit compilation: an emerging application for automated reasoning. In: Bernardini, S., Talamadupula, K., Yorke-Smith, N. (eds.) Proceedings of the 12th International Scheduling and Planning Applications Workshop (SPARK 2019), pp. 95–103 (2019)
Venturelli, D., Do, M., Rieffel, E.G., Frank, J.: Temporal planning for compilation of quantum approximate optimization circuits. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI 2017), pp. 4440–4446 (2017)
Acknowledgement
This research was supported by the Spanish Government under project PID2019-106263RB-I00 and by ESA Contract No. 4000112300/14/D/MRP Mars Express Data Planning Tool MEXAR2 Maintenance.
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Arufe, L., Rasconi, R., Oddi, A., Varela, R., González, M.Á. (2022). Compiling Single Round QCCP-X Quantum Circuits by Genetic Algorithm. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. IWINAC 2022. Lecture Notes in Computer Science, vol 13259. Springer, Cham. https://doi.org/10.1007/978-3-031-06527-9_9
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