Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/212696
Title: QUANTUM ALGORITHMS FOR PROVABLE MACHINE LEARNING
Authors: YANG SIYI
Keywords: quantum speedup, amplitude amplification, p-concept learnable, inner product estimation, sampling
Issue Date: 20-Aug-2021
Citation: YANG SIYI (2021-08-20). QUANTUM ALGORITHMS FOR PROVABLE MACHINE LEARNING. ScholarBank@NUS Repository.
Abstract: The thesis explores the quantum speedups for various machine learning algorithms, including the neural network, the Hedge algorithm, the Ising model and Markov Random Fields, with provable learning guarantees. A main subroutine in these quantizations is the inner product estimation of vectors. The exact computation of inner product is first replaced with estimation. Then the estimation is sped-up using quantum amplitude amplification. Then a quadratic speedup in terms of the data dimension is obtained.
URI: https://scholarbank.nus.edu.sg/handle/10635/212696
Appears in Collections:Ph.D Theses (Open)

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