7 citations · 7 across the 4 of their papers we have counts for
4 papers
Light-cone feature selection for quantum machine learning
Yudai Suzuki, Rei Sakuma, Hideaki Kawaguchi
Feature selection plays an essential role in improving the predictive performance and interpretability of trained models in classical machine learning. On the other hand, the usabi…
Quantum reservoir computing with repeated measurements on superconducting devices
Toshiki Yasuda, Yudai Suzuki, Tomoyuki Kubota +6
Reservoir computing is a machine learning framework that uses artificial or physical dissipative dynamics to predict time-series data using nonlinearity and memory properties of dy…
Effect of alternating layered ansatzes on trainability of projected quantum kernel
Yudai Suzuki, Muyuan Li
Quantum kernel methods have been actively examined from both theoretical and practical perspectives due to the potential of quantum advantage in machine learning tasks. Despite a p…
Quantum Noise-Induced Reservoir Computing
Tomoyuki Kubota, Yudai Suzuki, Shumpei Kobayashi +3
Quantum computing has been moving from a theoretical phase to practical one, presenting daunting challenges in implementing physical qubits, which are subjected to noises from the…