most citedExtending echo state property for quantum reservoir computing

18 citations · 18 across the 6 of their papers we have counts for

collaborators

6 papers

nlin.CD2026

One-shot prediction of noise-induced bifurcations with reservoir computing

Nozomi Akashi, Takayuki Watanabe, Masato Hara +4

Dynamical systems can exhibit complex responses when noise is injected. In particular, dynamics can be qualitatively altered by dynamic noise, a phenomenon known as noise-induced b…

nlin.AO2026

Memory Uncertainty Relation and Harmonic Memory in Random Recurrent Networks

Taichi Haruna, Kohei Nakajima

We present an inequality that bounds the short-term memory capability of dynamical systems from below. It can be interpreted as an uncertainty relation between a measure of short-t…

cs.NE2026

Optimal Memory Encoding Through Fluctuation-Response Structure

Lianxiang Cui, Kohei Nakajima, Kazuyuki Aihara

Physical reservoir computing exploits the intrinsic dynamics of physical systems for information processing, while keeping the internal dynamics fixed and training only linear read…

quant-ph2024

Coherence influx is indispensable for quantum reservoir computing

Shumpei Kobayashi, Quoc Hoan Tran, Kohei Nakajima

Echo state property (ESP) is a fundamental property that allows an input-driven dynamical system to perform information processing tasks. Recently, extensions of ESP to potentially…

cs.NE2024

Designing Chaotic Attractors: A Semi-supervised Approach

Tempei Kabayama, Yasuo Kuniyoshi, Kazuyuki Aihara +1

Chaotic dynamics are ubiquitous in nature and useful in engineering, but their geometric design can be challenging. Here, we propose a method using reservoir computing to generate…

quant-ph2024★ 18 cited

Extending echo state property for quantum reservoir computing

Shumpei Kobayashi, Quoc Hoan Tran, Kohei Nakajima

The echo state property (ESP) represents a fundamental concept in the reservoir computing (RC) framework that ensures output-only training of reservoir networks by being agnostic t…