Abstract
It is known that the use of quantum computing can reduce the time needed for a search in an unsorted array: from the original non-quantum time T to a much smaller quantum computation time \(T_q\sim \sqrt{T}\). In this paper, we show that for a continuous optimization problem, with quantum computing, we can reach almost the same speed-up: namely, we can reduce the non-quantum time T to a much shorter quantum computation time \(\sqrt{T}\cdot \ln (T)\).
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Acknowledgements
This work was supported in part by the US National Science Foundation grant HRD-1242122 (Cyber-ShARE Center of Excellence).
The authors are thankful for all the participants of the NMSU/UTEP Workshop on Mathematics, Computer Science, and Computational Science (Las Cruces, New Mexico, April 6, 2019) for valuable suggestions.
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Ayub, C., Ceberio, M., Kreinovich, V. (2020). How Quantum Computing Can Help with (Continuous) Optimization. In: Ceberio, M., Kreinovich, V. (eds) Decision Making under Constraints. Studies in Systems, Decision and Control, vol 276. Springer, Cham. https://doi.org/10.1007/978-3-030-40814-5_2
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DOI: https://doi.org/10.1007/978-3-030-40814-5_2
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