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various processes in modern machine learning, including learning, inference, and generation. In particular, we are working to establish novel theories and algorithms that enhance the efficiency
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, 2022) and extended this to the triple equivalence between neural dynamics, Bayesian inference, and algorithmic computation (Commun Phys, 2025). -We validated it within in vitro neural networks (Nature
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quantum information, develop techniques for quantum control and measurement, build quantum computing hardware and software, and explore novel applications. Our main interest is to propose quantum algorithms
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of concepts from quantum information to quantum materials; algorithmic aspects of quantum computing; topological aspects of magnetism; connections between condensed matter and high-energy physics