Katsuyoshi Sakamoto

TL;DR: In this article, a method to prepare tungsten tips for use in multi-tip scanning tunneling microscopes is presented, which is based on a combination of a drop-off method and dynamic electrochemical etching, in which the tip is continuously and slowly drawn up from the electrolyte during etching.

TL;DR: Deep learning artificial intelligence techniques are employed to predict the energy consumption and power generation together with the weather forecasting numerical simulation to form an optimal decentralized renewable energy system.

TL;DR: Two deep reinforcement learning algorithms designed for both discrete and continuous action space were applied and showed that the agent successfully captured the energy demand and supply feature in the training data and learnt to choose behavior leading to maximize its reward.

TL;DR: In this article, the authors performed simulations and theoretical analysis of the quantum circuit learning problem with hardware-efficient ansatz and showed that the expressibility and generalization error scaling of the ansatz saturate when the circuit depth increases.

TL;DR: A gradient descent based backpropagation algorithm that can efficiently calculate the gradient in parameter optimization and update the parameter for quantum circuit learning is proposed, which outperforms the current parameter search algorithms in terms of computing speed while presents the same or even higher test accuracy.