3.3k citations · 6.9k across the 56 of their papers we have counts for
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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…
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…
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…
Classical variational optimization of PREPARE circuit for quantum phase estimation of quantum chemistry Hamiltonians
Hayata Morisaki, Kosuke Mitarai, Keisuke Fujii +1
We propose a method for constructing circuits for quantum phase estimation of a molecular Hamiltonian in quantum chemistry by using variational optimization of q…
Algorithmic error mitigation for quantum eigenvalues estimation
Adam Siegel, Kosuke Mitarai, Keisuke Fujii
When estimating the eigenvalues of a given observable, even fault-tolerant quantum computers will be subject to errors, namely algorithmic errors. These stem from approximations in…
Accelerating Grover Adaptive Search: Qubit and Gate Count Reduction Strategies with Higher-Order Formulations
Yuki Sano, Kosuke Mitarai, Naoki Yamamoto +1
Grover adaptive search (GAS) is a quantum exhaustive search algorithm designed to solve binary optimization problems. In this paper, we propose higher-order binary formulations 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…
Experimental demonstration of a high-fidelity virtual two-qubit gate
Akhil Pratap Singh, Kosuke Mitarai, Yasunari Suzuki +4
We experimentally demonstrate a virtual two-qubit gate and characterize it using quantum process tomography~(QPT). The virtual two-qubit gate decomposes an actual two-qubit gate in…
MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ era
Leonardo Placidi, Ryuichiro Hataya, Toshio Mori +4
We introduce the first large-scale dataset, MNISQ, for both the Quantum and the Classical Machine Learning community during the Noisy Intermediate-Scale Quantum era. MNISQ consists…
QAOA-MC: Markov chain Monte Carlo enhanced by Quantum Alternating Operator Ansatz
Yuichiro Nakano, Hideaki Hakoshima, Kosuke Mitarai +1
Quantum computation is expected to accelerate certain computational task over classical counterpart. Its most primitive advantage is its ability to sample from classically intracta…
Quantum-Selected Configuration Interaction: classical diagonalization of Hamiltonians in subspaces selected by quantum computers
Keita Kanno, Masaya Kohda, Ryosuke Imai +4
We propose quantum-selected configuration interaction (QSCI), a class of hybrid quantum-classical algorithms for calculating the ground- and excited-state energies of many-electron…
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…
Almost optimal measurement scheduling of molecular Hamiltonian via finite projective plane
Wataru Inoue, Koki Aoyama, Yusuke Teranishi +3
We propose an efficient and almost optimal scheme for measuring molecular Hamiltonians in quantum chemistry on quantum computers, which requires distinct measurements in the…
Perturbation theory with quantum signal processing
Kosuke Mitarai, Kiichiro Toyoizumi, Wataru Mizukami
Perturbation theory is an important technique for reducing computational cost and providing physical insights in simulating quantum systems with classical computers. Here, we provi…
Quantum-inspired algorithm applied to extreme learning
Iori Takeda, Souichi Takahira, Kosuke Mitarai +1
Quantum-inspired singular value decomposition (SVD) is a technique to perform SVD in logarithmic time with respect to the dimension of a matrix, given access to the matrix embedded…
Pricing multi-asset derivatives by variational quantum algorithms
Kenji Kubo, Koichi Miyamoto, Kosuke Mitarai +1
Pricing a multi-asset derivative is an important problem in financial engineering, both theoretically and practically. Although it is suitable to numerically solve partial differen…