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Tensor tree learns hidden relational structures in data to construct generative models

日程 : 2024年12月13日(金) 4:00 pm - 5:00 pm 場所 : 物性研究所本館6階 第5セミナー室 (A615) 講師 : 原田 健自 所属 : 京都大学大学院 情報学研究科 世話人 : 川島 直輝講演言語 : 英語

Generative modeling is a significant machine learning technique that constructs the probability distribution of a dataset, owing to its wide range of applications across various problems. Recently, there has been extensive research into generative modeling on quantum computers, referred to as Born machines [1-8]. This approach utilizes the output of projective measurement of quantum states for stochastic samplings.

We propose a general method for constructing a generative model based on the tree tensor network within the Born machine framework [9]. The core idea is to optimize the tree structure dynamically to minimize the bond mutual information. We demonstrate potential applications with four examples:
(1) Random bit sequences with long-range correlation
(2) Images of handwritten digits from the QMNIST dataset
(3) Bayesian networks
(4) Stock price fluctuations in the S&P 500
Our method significantly enhances performance and reveals hidden relational structures in the target data, paving the way for future improvements and advancements.

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(公開日: 2024年11月26日)