Abstract
At present the scale of true universal quantum computer is still small. The quantum computer has not yet been introduced into practical applications from laboratory. Therefore, quantum simulation has become the main assistant method of verifying quantum algorithms. Grover’s quantum search algorithm can speed up many classical algorithms that use search heuristics. In this work, a high performance Grover algorithm simulation is proposed combining the characteristics of Grover’s algorithm and the parallelism of cloud computing, which dramatically improves the performance of the load balancing among multi-core, the utilization of memory space and the efficiency of simulation. Moreover, We propose five different computing configurations in the cloud environment suitable for quantum simulation and compare them through experimentation. We also validate the effectiveness of optimization by analysis and experimentation. The experimentation shows the simulation can reach 31 bits depending on the scale of current configuration.
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Acknowledgments
This work is supported by Natural Science Foundation of Jiangsu Province, China (Grant No. BK20140823), Fundamental Research Funds for the Central Universities (Grant No. NS2014096).
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Tang, X., Xu, J., Zhou, Y. (2017). Optimization of Grover’s Algorithm Simulation Based on Cloud Computing. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_10
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DOI: https://doi.org/10.1007/978-3-319-68935-7_10
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