25 citations · 145 across the 17 of their papers we have counts for
6 papers · 1 filter
Approximate complex amplitude encoding algorithm and its application to data classification problems
Naoki Mitsuda, Tatsuhiro Ichimura, Kouhei Nakaji +6
Quantum computing has a potential to accelerate the data processing efficiency, especially in machine learning, by exploiting special features such as the quantum interference. The…
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…
Generative model for learning quantum ensemble via optimal transport loss
Hiroyuki Tezuka, Shumpei Uno, Naoki Yamamoto
Generative modeling is an unsupervised machine learning framework, that exhibits strong performance in various machine learning tasks. Recently we find several quantum version of g…
Quantum-enhanced mean value estimation via adaptive measurement
Kaito Wada, Kazuma Fukuchi, Naoki Yamamoto
Quantum-enhanced (i.e., higher performance by quantum effects than any classical methods) mean value estimation of observables is a fundamental task in various quantum technologies…
Deterministic and random features for large-scale quantum kernel machine
Kouhei Nakaji, Hiroyuki Tezuka, Naoki Yamamoto
Quantum machine learning (QML) is the spearhead of quantum computer applications. In particular, quantum neural networks (QNN) are actively studied as the method that works both in…
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…