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arXiv · 1802.07828

Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization

Yuxuan Du·Tongliang Liu·Yinan Li·Runyao Duan·Dacheng Tao
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Abstract

It is NP-complete to find non-negative factors $W$ and $H$ with fixed rank $r$ from a non-negative matrix $X$ by minimizing $\|X-WH^\top\|_F^2$. Although the separability assumption (all data points are in the conical hull of the extreme rows) enables polynomial-time algorithms, the computational cost is not affordable for big data. This paper investigates how the power of quantum computation can be capitalized to solve the non-negative matrix factorization with the separability assumption (SNMF) by devising a quantum algorithm based on the divide-and-conquer anchoring (DCA) scheme. The design of quantum DCA (QDCA) is challenging. In the divide step, the random projections in DCA is completed by a quantum algorithm for linear operations, which achieves the exponential speedup. We then devise a heuristic post-selection procedure which extracts the information of anchors stored in the quantum states efficiently. Under a plausible assumption, QDCA performs efficiently, achieves the quantum speedup, and is beneficial for high dimensional problems.

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Yuxuan Du, Tongliang Liu, Yinan Li, Runyao Duan, Dacheng Tao. 2018-02-20. Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization. https://arxiv.org/abs/1802.07828

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