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【基礎沙龍】第36期-王春昊


主講人:王春昊,博士,德州大學奧斯汀分校計算機系博士后研究員

主題:Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning

時間:2020年1月14日星期二,10:00-11:00

地點:沙河校區通信樓725室

主講人簡介

王春昊红鹰彩票骗局,博士,現德州大學奧斯汀分校計算機系博士后研究員。2009年本科畢業于浙江大學計算機系红鹰彩票骗局,2011年碩士畢業于加拿大西門弗雷澤大學計算機系,2018年博士畢業于加拿大滑鐵盧大學計算機系。王春昊博士長期從事計算機算法以及量子計算理論研究红鹰彩票骗局,目前致力于量子模擬,量子機器學習,量子優化算法红鹰彩票骗局,量子計算復雜度等方面的研究红鹰彩票骗局,現已有多篇論文被理論量子計算頂級會議QIP接收。

報告摘要

We present an algorithmic framework generalizing quantum-inspired polylogarithmic-time algorithms on low-rank matrices. Our work follows the line of research started by Tang's breakthrough classical algorithm for recommendation systems [STOC'19]. The main result of this work is an algorithm for singular value transformation on low-rank inputs in the quantum-inspired regime, where singular value transformation is a framework proposed by Gilyén et al. [STOC'19] to study various quantum speedups. Since singular value transformation encompasses a vast range of matrix arithmetic, this result, combined with simple sampling lemmas from previous work, suffices to generalize all results dequantizing quantum machine learning algorithms to the authors' knowledge. Via simple black-box applications of our singular value transformation framework, we recover the dequantization results on recommendation systems, principal component analysis, supervised clustering, low-rank matrix inversion, low-rank semidefinite programming, and support vector machines. We also give additional dequantizations results on low-rank Hamiltonian simulation and discriminant analysis.

红鹰彩票骗局
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