Masahito Ueda

professor

University of Tokyo

Japan

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Dr. Masahito Ueda is a professor at The University of Tokyo, Japan. His research areas encompass quantum mechanics, particularly focusing on many-body systems, quantum feedback control, and thermodynamic uncertainty relations. He has contributed significantly to the understanding of quantum criticality, nonlinear response theory, and applications of reinforcement learning in symbolic equation solving.

Articles (17)

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2023

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2022

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2022

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2022

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2021

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2021

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2021

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2020

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2020

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2020

Collaborators (1)

Hosho Katsura

University of Tokyo

JAPAN

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Masahito Ueda

Research Interests

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About

Articles (17)

Quantum master equation for many-body systems based on the Lieb-Robinson bound

Topology of Discrete Quantum Feedback Control

Universal Upper Bound on Ergotropy and No-Go Theorem by the Eigenstate Thermalization Hypothesis

Symbolic equation solving via reinforcement learning

Pink-noise dynamics in an evolutionary game on a regular graph

Experimental Observation of the Yang-Lee Quantum Criticality in Open Quantum Systems

Exact eigenstates of multicomponent Hubbard models: SU( N ) magnetic η pairing, weak ergodicity breaking, and partial integrability

Universal thermodynamic uncertainty relation in nonequilibrium dynamics

Nonlinear Landauer formula: Nonlinear response theory of disordered and topological materials

Three learning stages and accuracy–efficiency tradeoff of restricted Boltzmann machines

Vortex lattices in binary Bose–Einstein condensates: collective modes, quantum fluctuations, and intercomponent entanglement

Intercomponent entanglement entropy and spectrum in binary Bose-Einstein condensates

Exact Liouvillian Spectrum of a One-Dimensional Dissipative Hubbard Model

Test of the Eigenstate Thermalization Hypothesis Based on Local Random Matrix Theory

Quantum equilibration, thermalization and prethermalization in ultracold atoms

Continuous Phase Transition without Gap Closing in Non-Hermitian Quantum Many-Body Systems

Deep Reinforcement Learning Control of Quantum Cartpoles

Collaborators (1)

Hosho Katsura

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