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Quantum Machine Learning
Quantum Laplacian Eigenmap
arXiv
Authors: Yiming Huang, Xiaoyu Li
Year
2016
Paper ID
42619
Status
Preprint
Abstract Read
~2 min
Abstract Words
80
Citations
N/A
Abstract
Laplacian eigenmap algorithm is a typical nonlinear model for dimensionality reduction in classical machine learning. We propose an efficient quantum Laplacian eigenmap algorithm to exponentially speed up the original counterparts. In our work, we demonstrate that the Hermitian chain product proposed in quantum linear discriminant analysis (arXiv:1510.00113,2015) can be applied to implement quantum Laplacian eigenmap algorithm. While classical Laplacian eigenmap algorithm requires polynomial time to solve the eigenvector problem, our algorithm is able to exponentially speed up nonlinear dimensionality reduction.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2016 reference point for readers tracking recent quantum research.
- Laplacian eigenmap algorithm is a typical nonlinear model for dimensionality reduction in classical machine learning.
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