Abstract
Setting the values of various parameters for an evolutionary algorithm is essential for its good performance. This paper discusses two optimization strategies that may be used on a conventional Genetic Algorithm to evolve quantum circuits: adaptive (parameters initial values are set before actually running the algorithm) or self-adaptive (parameters change at runtime). The differences between these approaches are investigated, with the focus being put on algorithm performance in terms of evolution time. When taking into consideration the runtime as main target, the performed experiments show that the adaptive behavior (tuning) is more effective for quantum circuit synthesis as opposed to self-adaptive (control). This research provides an answer to whether an evolutionary algorithm applied to quantum circuit synthesis may be more effective when automatic parameter adjustments are made during evolution.
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References
Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith, J.E.: Parameter Control in Evolutionary Algorithms. In: Parameter Setting in Evolutionary Algorithms, Springer, Heidelberg (2007)
Wolpert, D.H., Macready, W.G.: No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation 67(1), 67–82 (1997)
Rechenberg, I.: Evolutionsstrategie - Optimierung technischer Systeme nach Prinzipien der biologischen. Frommann-Holzboog, Stuttgart (1973)
Ruican, C., Udrescu, M., Prodan, L., Vladutiu, M.: Automatic Synthesis for Quantum Circuits using Genetic Algorithms. In: International Conference on Adaptive and Natural Computing Algorithms, pp. 174–183 (2007)
Ruican, C., Udrescu, M., Prodan, L., Vladutiu, M.: A Genetic Algorithm Framework Applied to Quantum Circuit Synthesis. In: Nature Inspired Cooperative Strategies for Optimization, pp. 419–429 (2007)
Gheorghies, O., Luchian, H., Gheorghies, A.: Walking the Royal Road with Integrated-Adaptive Genetic Algorithms. University Alexandru Ioan Cuza of Iasi (2005), http://thor.info.uaic.ro/~tr/tr05-04.pdf
Maslov, D.: Reversible Logic Synthesis Benchmarks Page (2008), http://www.cs.uvic.ca/%7Edmaslov/
Spector, L.: Automatic Quantum Computer Programming. A Genetic Programming Approach, 2nd edn. Springer, Heidelberg (2006)
Nielsen, M., Chuang, I.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)
Yao, X.: An Empirical Study of Genetic Operators in Genetic Algorithms. Microprocessing and Microprogramming 38(1-5), 707–714 (1993)
Hilding, F.G., Ward, K.: Automated Operator Selection on Genetic Algorithms. Knowledge-Based Intelligent Information and Engineering Systems, 903–909 (2005)
Affenzeller, M., Wagner, S.: Offspring Selection: A New Self-Adaptive Selection Scheme for Genetic Algorithms. Adaptive and Natural Computing Algorithms, 218–221 (2005)
Ruican, C.: Projects Web Site Page (2010), http://www.cs.utt.ro/~crys/index_files/public/ices.tar.gz
Luke, S.: Essentials of Metaheuristics. Zeroth Edition (2009), http://cs.gmu.edu/~sean/book/metaheuristics/
Smit, S.K., Eiben, A.E.: Comparing Parameter Tuning Methods for Evolutionary Algorithms. In: IEEE Congress on Evolutionary Computation, pp. 399–406 (2009)
Maslov, D., Dueck, G.W.: Level Compaction in Quantum Circuits. In: IEEE Congress on Evolutionary Computation, pp. 2405–2409 (2006)
Shende, V., Prasad, A.K., Markov, I.L., Hayes, J.P.: Synthesis of Reversible Logic Circuits. IEEE Transaction on CAD 22 22(6), 710–722 (2003)
Lukac, M., Perkowski, M.: Evolving quantum circuits using genetic algorithm. In: NASA/DoD Conference on Evolvable Hardware, pp. 177–185 (2002)
Ruican, C., Udrescu, M., Prodan, L., Vladutiu, M.: Quantum Circuit Synthesis with Adaptive Parametres Control. In: European Conference on Genetic Programming, pp. 339–350 (2009)
Ruican, C., Udrescu, M., Prodan, L., Vladutiu, M.: Genetic Algorithm Based - Quantum Circuit Synthesis with Adaptive Parameters. In: IEEE Congress on Evolutionary Computation, pp. 896–903 (2009)
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Ruican, C., Udrescu, M., Prodan, L., Vladutiu, M. (2010). Adaptive vs. Self-adaptive Parameters for Evolving Quantum Circuits. In: Tempesti, G., Tyrrell, A.M., Miller, J.F. (eds) Evolvable Systems: From Biology to Hardware. ICES 2010. Lecture Notes in Computer Science, vol 6274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15323-5_30
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DOI: https://doi.org/10.1007/978-3-642-15323-5_30
Publisher Name: Springer, Berlin, Heidelberg
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