Abstract
Three kinds of quantum optimizations are introduced in this paper as follows: quantum minimization (QM), neuromprphic quantum-based optimization (NQO), and logarithmic search with quantum existence testing (LSQET). In order to compare their fitting ability among three quantum optimizations, the performance evaluation on these methods is implemented for the application of time series forecast. Finally, based on the predictive accuracy of time series forecast the concluding remark will be made to illustrate and discuss these three quantum optimizations.
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Chang, B.R., Tsai, H.F. (2007). Performance Evaluation of Three Kinds of Quantum Optimization. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_24
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DOI: https://doi.org/10.1007/978-3-540-74581-5_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74580-8
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