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Quantum algorithm for neighborhood preserving embedding
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作者 Shi-Jie Pan lin-chun wan +4 位作者 Hai-Ling Liu Yu-Sen Wu Su-Juan Qin Qiao-Yan Wen Fei Gao 《Chinese Physics B》 SCIE EI CAS CSCD 2022年第6期192-203,共12页
Neighborhood preserving embedding(NPE)is an important linear dimensionality reduction technique that aims at preserving the local manifold structure.NPE contains three steps,i.e.,finding the nearest neighbors of each ... Neighborhood preserving embedding(NPE)is an important linear dimensionality reduction technique that aims at preserving the local manifold structure.NPE contains three steps,i.e.,finding the nearest neighbors of each data point,constructing the weight matrix,and obtaining the transformation matrix.Liang et al.proposed a variational quantum algorithm(VQA)for NPE[Phys.Rev.A 101032323(2020)].The algorithm consists of three quantum sub-algorithms,corresponding to the three steps of NPE,and was expected to have an exponential speedup on the dimensionality n.However,the algorithm has two disadvantages:(i)It is not known how to efficiently obtain the input of the third sub-algorithm from the output of the second one.(ii)Its complexity cannot be rigorously analyzed because the third sub-algorithm in it is a VQA.In this paper,we propose a complete quantum algorithm for NPE,in which we redesign the three sub-algorithms and give a rigorous complexity analysis.It is shown that our algorithm can achieve a polynomial speedup on the number of data points m and an exponential speedup on the dimensionality n under certain conditions over the classical NPE algorithm,and achieve a significant speedup compared to Liang et al.’s algorithm even without considering the complexity of the VQA. 展开更多
关键词 quantum algorithm quantum machine learning amplitude amplification
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