From the 1 of 8 linked papers with an AI index.
8 papers
Quantum machine learning interatomic potential: Application of variational quantum algorithm
Kohei Numata, Wataru Mizukami, Kosuke Mitarai +2
The paper integrates a variational quantum circuit into a classical neural network for interatomic potentials, retraining the ANI model via quantum transfer learning and showing mo…
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…
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…
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…
Quantum Circuit Unoptimization
Yusei Mori, Hideaki Hakoshima, Kyohei Sudo +3
Optimization of circuits is an essential task for both quantum and classical computers to improve their efficiency. In contrast, classical logic optimization is known to be difficu…