85 citations · 96 across the 5 of their papers we have counts for
6 papers · 1 filter
Learning from imperfect quantum data via unsupervised domain adaptation with classical shadows
Kosuke Ito, Akira Tanji, Hiroshi Yano +2
Learning from quantum data using classical machine learning models has emerged as a promising paradigm toward realizing quantum advantages. Despite extensive analyses on their perf…
Quantum reservoir computing with repeated measurements on superconducting devices
Toshiki Yasuda, Yudai Suzuki, Tomoyuki Kubota +8
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…
Quantum Fisher kernel for mitigating the vanishing similarity issue
Yudai Suzuki, Hideaki Kawaguchi, Naoki Yamamoto
Quantum kernel method is a machine learning model exploiting quantum computers to calculate the quantum kernels (QKs) that measure the similarity between data. Despite the potentia…
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…
Natural quantum reservoir computing for temporal information processing
Yudai Suzuki, Qi Gao, Ken C. Pradel +2
Reservoir computing is a temporal information processing system that exploits artificial or physical dissipative dynamics to learn a dynamical system and generate the target time-s…
Efficient Discrete Feature Encoding for Variational Quantum Classifier
Hiroshi Yano, Yudai Suzuki, Kohei M. Itoh +2
Recent days have witnessed significant interests in applying quantum-enhanced techniques for solving a variety of machine learning tasks. Variational methods that use quantum resou…