Abstract
Quantum computing is inspired in quantum mechanical phenomena and uses superposition and entanglement to process data at very high speeds outperforming conventional computers on some tasks. At present, the access for testing algorithms in commercial quantum computers is too expensive for most institutions; hence, it is very important to have alternatives for testing quantum algorithms. In this paper, we present the results obtained when optimizing a two variables multimodal function when it was optimized through the Quack_GPU v1, which is a modification of the original software Quack! We show that it is possible to obtain speedups up to 8.4× using a Graphic Processing Unit (GPU) computer card with thousands of cores, saving hours of processing time. Performance comparative results of the Quack! vs. the Quack_GPU are presented.
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Acknowledgements
We thank Instituto Politécnico Nacional (IPN), to the Comisión de Fomento y Apoyo Académico del IPN (COFAA), and to the Mexican National Council of Science and Technology (CONACYT) for supporting our research activities.
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Montiel, O., Sepúlveda, R., Rubio, Y. (2018). Speeding Up Quantum Genetic Algorithms in Matlab Through the Quack_GPU V1. In: Melin, P., Castillo, O., Kacprzyk, J., Reformat, M., Melek, W. (eds) Fuzzy Logic in Intelligent System Design. NAFIPS 2017. Advances in Intelligent Systems and Computing, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-319-67137-6_17
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DOI: https://doi.org/10.1007/978-3-319-67137-6_17
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