Complementarity and Additivity for Covariant Channels. - Nilanjana Datta; Motohisa Fukuda; Alexander S. Holevo (Quantum Inf. Process., 2006)

Simplification of Additivity Conjecture in Quantum Information Theory. - Motohisa Fukuda (Quantum Inf. Process., 2007)

Quantum Channels With Polytopic Images and Image Additivity. - Motohisa Fukuda; Ion Nechita; Michael M. Wolf (IEEE Trans. Inf. Theory, 2015)

Additive Bounds of Minimum Output Entropies for Unital Channels and an Exact Qubit Formula. - Motohisa Fukuda; Gilad Gour (IEEE Trans. Inf. Theory, 2017)

On the Minimum Output Entropy of Random Orthogonal Quantum Channels. - Motohisa Fukuda; Ion Nechita (IEEE Trans. Inf. Theory, 2018)

Central object segmentation by deep learning for fruits and other roundish objects. - Motohisa Fukuda; Takashi Okuno; Shinya Yuki (CoRR, 2020)

Deep Learning in Forestry Using UAV-Acquired RGB Data: A Practical Review. - Yago Diez; Sarah Kentsch; Motohisa Fukuda; Maximo Larry Lopez Caceres; Koma Moritake; Mariano Cabezas (Remote. Sens., 2021)

Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images. - Motohisa Fukuda; Takashi Okuno; Shinya Yuki (Sensors, 2021)

Symbolically integrating tensor networks over various random tensors by the second version of Python RTNI. - Motohisa Fukuda (CoRR, 2023)

Concentration of Quantum Channels With Random Kraus Operators via Matrix Bernstein Inequality. - Motohisa Fukuda (IEEE Trans. Inf. Theory, 2025)