420 citations · 526 across the 8 of their papers we have counts for
14 papers
Physical Deep Learning with Biologically Plausible Training Method
Mitsumasa Nakajima, Katsuma Inoue, Kenji Tanaka +3
The ever-growing demand for further advances in artificial intelligence motivated research on unconventional computation based on analog physical devices. While such computation de…
Step-like dependence of memory function on pulse width in spintronics reservoir computing
Terufumi Yamaguchi, Nozomi Akashi, Kohei Nakajima +3
Physical reservoir computing is a type of recurrent neural network that applies the dynamical response from physical systems to information processing. However, the relation betwee…
Quantum reservoir computing: a reservoir approach toward quantum machine learning on near-term quantum devices
Keisuke Fujii, Kohei Nakajima
Quantum systems have an exponentially large degree of freedom in the number of particles and hence provide a rich dynamics that could not be simulated on conventional computers. Qu…
Periodic structure of memory function in spintronics reservoir with feedback current
Terufumi Yamaguchi, Nozomi Akashi, Sumito Tsunegi +3
The role of the feedback effect on physical reservoir computing is studied theoretically by solving the vortex-core dynamics in a nanostructured ferromagnet. Although the spin-tran…
Higher-Order Quantum Reservoir Computing
Quoc Hoan Tran, Kohei Nakajima
Quantum reservoir computing (QRC) is an emerging paradigm for harnessing the natural dynamics of quantum systems as computational resources that can be used for temporal machine le…
Physical reservoir computing -- An introductory perspective
Kohei Nakajima
Understanding the fundamental relationships between physics and its information-processing capability has been an active research topic for many years. Physical reservoir computing…