3.3k citations · 6.9k across the 56 of their papers we have counts for
57 papers
Quantum machine learning interatomic potential: Application of variational quantum algorithm
Kohei Numata, Wataru Mizukami, Kosuke Mitarai +2
This study applied quantum circuit learning, a commonly used hybrid quantum-classical machine learning algorithm, to a machine learning interatomic potential (MLIP) for predicting…
Quasi-Monte Carlo Method for Linear Combination Unitaries via Classical Post-Processing
Yuya Kawamata, Kosuke Mitarai, Keisuke Fujii
We propose the quasi-Monte Carlo method for linear combination of unitaries via classical post-processing (LCU-CPP) on quantum applications. The LCU-CPP framework has been proposed…
Automating quantum feature map design via large language models
Kenya Sakka, Kosuke Mitarai, Keisuke Fujii
Quantum feature maps are a key component of quantum machine learning, encoding classical data into quantum states to exploit the expressive power of high-dimensional Hilbert spaces…
Quantum expectation value estimation by doubling the number of qubits
Hiroshi Yano, Masaya Kohda, Shoichiro Tsutsui +4
Expectation value estimation is ubiquitous in quantum algorithms. The expectation value of a Hamiltonian, which is essential in various practical applications, is often estimated b…
Efficient state preparation for multivariate Monte Carlo simulation
Hitomi Mori, Kosuke Mitarai, Keisuke Fujii
Quantum state preparation is a task to prepare a state with a specific function encoded in the amplitude, which is an essential subroutine in many quantum algorithms. In this paper…
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…