24 citations · 27 across the 4 of their papers we have counts for
5 papers
Provably efficient experimental classification of quantum data with quantum kernels
Keitaro Anai, Akimoto Nakayama, Shintaro Ooka +9
Analyzing quantum systems is essential for quantum information processing, but can require exponentially many measurements without exploiting the structure of the system or task. Q…
Learning functions of Hamiltonians with Hamiltonian Fourier features
Yuto Morohoshi, Akimoto Nakayama, Hidetaka Manabe +1
We propose a quantum machine learning task that is provably easy for quantum computers and arguably hard for classical ones. The task involves predicting quantities of the form $\m…
Explicit quantum surrogates for quantum kernel models
Akimoto Nakayama, Hayata Morisaki, Kosuke Mitarai +2
Quantum machine learning (QML) leverages quantum states for data encoding, with key approaches being explicit models that use parameterized quantum circuits and implicit models tha…
A comprehensive survey on quantum computer usage: How many qubits are employed for what purposes?
Tsubasa Ichikawa, Hideaki Hakoshima, Koji Inui +18
Quantum computers (QCs), which work based on the law of quantum mechanics, are expected to be faster than classical computers in several computational tasks such as prime factoring…
VQE-generated quantum circuit dataset for machine learning
Akimoto Nakayama, Kosuke Mitarai, Leonardo Placidi +2
Quantum machine learning has the potential to computationally outperform classical machine learning, but it is not yet clear whether it will actually be valuable for practical prob…