Abstract
With the development of machine learning stepping into a bottleneck period, quantum machine learning has become a new popular research direction. Quantum computing is built on the principle of quantum mechanics, which can abstract the linear evolution process of quantum systems into a linear mathematical calculation process. This paper explores high-efficient storage and parallel computing performance of quantum computing by analyzing some quantum nearest-neighbor algorithms precisely. Based on these ideas, an improved quantum weighted nearest-neighbor algorithm (QWNN) is proposed, which sufficiently conforms to the idea of parallel computing. QWNN algorithm not only inherits special efficient coding method and amplitude estimation technique of previous quantum nearest-neighbor algorithm, but also includes the weighting algorithm of quantum version. The experimental data show that the performance of QWNN is comparable to that of similar algorithms.
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Zhang, Y., Feng, B., Jia, W., Xu, CZ. (2021). An Improved Quantum Nearest-Neighbor Algorithm. In: Liu, Q., Liu, X., Li, L., Zhou, H., Zhao, HH. (eds) Proceedings of the 9th International Conference on Computer Engineering and Networks . Advances in Intelligent Systems and Computing, vol 1143. Springer, Singapore. https://doi.org/10.1007/978-981-15-3753-0_39
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DOI: https://doi.org/10.1007/978-981-15-3753-0_39
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