Quantum Machine Learning Seminar (Dr. Hayata Yamasaki, IQOQI Vienna, TU Wien)

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園田翔

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Jul 7, 2021, 1:42:26 AMJul 7
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みなさま

理研AIPの園田翔です。

今月末 7/27(火)にオンライン開催される量子機械学習セミナーのご案内です。

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概要:
量子機械学習で先駆的な研究をされている山崎隼太氏(IQOQI Vienna/TU
Wien)に、量子情報の基本的な事項から始めて最近の研究内容について解説して頂きます。量子情報と機械学習の新たな交流が生まれることを期待して、ディスカッションの時間を少し長めに設定しています。使用言語は日本語です。

Title:
Learning with Optimized Random Features: Quantum Computation for
Accelerating Machine Learning

Abstract:
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.
https://arxiv.org/abs/2004.10756
https://arxiv.org/abs/2106.09028
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・参加費無料・要登録(当日迄)
・セミナーの詳細は以下のウェブサイトでご確認ください。
https://c5dc59ed978213830355fc8978.doorkeeper.jp/events/124339
・世話人:園田

よろしくお願いいたします。
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