Global Optimum Search in Quantum Deep Learning

Lanston Hau Man Chu; Tejas Bhojraj; Rui Huang

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arXiv · 2008.03655

Global Optimum Search in Quantum Deep Learning

Lanston Hau Man Chu·Tejas Bhojraj·Rui Huang

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Abstract

This paper aims to solve machine learning optimization problem by using quantum circuit. Two approaches, namely the average approach and the Partial Swap Test Cut-off method (PSTC) was proposed to search for the global minimum/maximum of two different objective functions. The current cost is $O(\sqrt{|\Theta|} N)$, but there is potential to improve PSTC further to $O(\sqrt{|\Theta|} \cdot sublinear \ N)$ by enhancing the checking process.

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quant-ph cs.LG stat.ML

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BibTeX RIS

Lanston Hau Man Chu, Tejas Bhojraj, Rui Huang. 2020-08-09. Global Optimum Search in Quantum Deep Learning. https://arxiv.org/abs/2008.03655

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