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(All talks will be given in Japanese. Simultaneous interpretation will not be available.)
Speaker: Dr. Hayata Yamasaki
1. Institute for Quantum Optics and Quantum Information (IQOQI), Austrian Academy of Sciences
2. Atominstitut, Technische Universität Wien
Learning with Optimized Random Features: Quantum Computation for Accelerating Machine Learning
This talk will review the basics of quantum computation, and a series of recent works on quantum machine learning (QML) with optimized random features. The goal of the talk is to explain how to use exponential speedup achieved by quantum computation to accelerate learning without imposing restrictive assumptions.
Random features are a central technique for scalable learning algorithms based on kernel methods. A recent work has shown that an algorithm using quantum computation can exponentially speed up sampling of optimized random features, even without imposing restrictive assumptions on sparsity and low-rankness of matrices that had limited applicability of conventional QML algorithms. This QML algorithm makes it possible to significantly reduce and provably minimize the required number of features for achieving learning tasks.
This talk will present applications of this QML algorithm to significant acceleration of leading regression and classification algorithms based on kernel methods, based on the following papers.
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