36 citations · 58 across the 12 of their papers we have counts for
13 papers
Optimized Randomized Hamiltonian Simulation via Average-Error Analysis
Hayata Morisaki, Keisuke Fujii
Hamiltonian simulation is a central application of quantum computing. Randomized Hamiltonian simulation approximates the target dynamics by sampling quantum circuits and often allo…
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…
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…
Scalable circuit depth reduction in feedback-based quantum optimization with a quadratic approximation
Don Arai, Ken N. Okada, Yuichiro Nakano +2
Combinatorial optimization problems are one of the areas where near-term noisy quantum computers may have practical advantage against classical computers. Recently a novel feedback…
Feedback-driven quantum reservoir computing for time-series analysis
Kaito Kobayashi, Keisuke Fujii, Naoki Yamamoto
Quantum reservoir computing (QRC) is a highly promising computational paradigm that leverages quantum systems as a computational resource for nonlinear information processing. Whil…
Experimental Demonstration of Fermionic QAOA with One-Dimensional Cyclic Driver Hamiltonian
Takuya Yoshioka, Keita Sasada, Yuichiro Nakano +1
Quantum approximate optimization algorithm (QAOA) has attracted much attention as an algorithm that has the potential to efficiently solve combinatorial optimization problems. Amon…
Quantum Kernel t-Distributed Stochastic Neighbor Embedding
Yoshiaki Kawase, Kosuke Mitarai, Keisuke Fujii
Data visualization is important in understanding the characteristics of data that are difficult to see directly. It is used to visualize loss landscapes and optimization trajectori…
End-to-end complexity for simulating the Schwinger model on quantum computers
Kazuki Sakamoto, Hayata Morisaki, Junichi Haruna +3
The Schwinger model is one of the simplest gauge theories. It is known that a topological term of the model leads to the infamous sign problem in the classical Monte Carlo method.…
Continuous optimization by quantum adaptive distribution search
Kohei Morimoto, Yusuke Takase, Kosuke Mitarai +1
In this paper, we introduce the quantum adaptive distribution search (QuADS), a quantum continuous optimization algorithm that integrates Grover adaptive search (GAS) with the cova…
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…