2021年 | 大阪大学基礎工学研究科藤井研究室

K. Fujii, K. Mizuta, H. Ueda, K. Mitarai, W. Mizukami, and Y. O. Nakagawa,
“Deep Variational Quantum Eigensolver: a divide-and-conquer method for solving a larger problem with smaller size quantum computers” PRXQuantum 3 (1), 010346 (2021). arXiv:2007.10917
Y. Suzuki, S. Endo, K. Fujii, and Y. Tokunaga,
“Quantum error mitigation as a universal error-minimization technique: applications from NISQ to FTQC eras” PRXQuantum 3 (1), 010345 (2021). arXiv:2010.03887
L. Erhart, K. Mitarai, W. Mizukami, and K. Fujii,
“A comprehensive study on how to construct local bases in deep variational quantum eigensolver for molecular systems” arXiv:2202.08473
Y. Kawase, K. Mitarai, and K. Fujii,
“Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network” arXiv:2202.04238
K.N. Okada, K. Osaki, K. Mitarai, and K. Fujii,
“Identification of topological phases using classically-optimized variational quantum eigensolver” arXiv:2202.02909
R. Suzuki, K. Mitarai, and K. Fujii,
“Computational power of one- and two-dimensional dual-unitary quantum circuits” Quantum 6, 631 (2021). arXiv:2103.09211
S. Hakkaku, K. Mitarai,and K. Fujii,
“Sampling-based quasiprobability simulation for fault-tolerant quantum error correction on the surface codes under coherent noise”
Phys. Rev. Research 3, 043130 (2021). arXiv:2105.04478
N. Shirai, K. Kubo, K. Mitarai, and K. Fujii,
“Quantum tangent kernel” arXiv:2111.02951
Y. Suzuki, Y. Kawase, Y. Masumura, Y. Hiraga, M. Nakadai, J. Chen, K. M. Nakanishi, K. Mitarai, R. Imai, S. Tamiya, T. Yamamoto, T. Yan, T. Kawakubo, Y. O. Nakagawa, Y. Ibe, Y. Zhang, H. Yamashita, H. Yoshimura, A. Hayashi, and K. Fujii,
“Qulacs: a fast and versatile quantum circuit simulator for research purpose” Quantum 5, 559 (2021). arXiv:2011.13524
Y. Luo, M. Chen, M. Erhard, H. Zhong, D. Wu, H. Tang, Q. Zhao, X. Wang, K. Fujii, L. Li, N. Liu, K. Nemoto, W. J. Munro, C. Lu, A. Zeilinger, and J. Pan,
“Quantum teleportation of physical qubits into logical code-spaces”
Proceedings of the National Academy of Sciences of the United States of America(PNAS) 118, e2026250118 (2021). arXiv:2009.06242
M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, and P. J. Coles,
“Variational Quantum Algorithms” Nature Reviews Physics 3, 625-644 (2021). arXiv:2012.09265
K. Fujii and K. Nakajima,
“Quantum reservoir computing: a reservoir approach toward quantum machine learning on near-term quantum devices”
Reservoir Computing, pp 331-360 (2021). arXiv:2011.04890
M, Negoro, K, Mitarai, K, Nakajima, and K, Fujii,
“Toward NMR Quantum Reservoir Computing” Reservoir Computing, pp 451-458 (2021)
N. Guo, K. Mitarai, and K. Fujii,
“Nonlinear transformation of complex amplitudes via quantum singular value transformation” arXiv:2107.10764
S. Ghosh, K. Nakajima, T. Krisnanda, K. Fujii, and T. C. H. Liew,
“Quantum Neuromorphic Computing with Reservoir Computing Networks” Advanced Quantum Technologies 4, 2100053 (2021)
S. Hakkaku and K. Fujii,
“Comparative study for sampling-based simulation costs of noisy quantum circuits” Phys. Rev. A 15, 064027 (2021). arXiv:2011.06233
T. Kusumoto, K. Mitarai, K. Fujii, M. Kitagawa, and M. Negoro,
“Experimental quantum kernel machine learning with nuclear spins in a solid” npj Quantum Information 7, 94 (2021). arXiv:1911.12021
K. Ito, W. Mizukami, and K. Fujii,
“Universal noise-precision relations in variational quantum algorithms” arXiv:2106.03390
K. Kubo, Y. O. Nakagawa, S. Endo, and S. Nagayama,
“Variational quantum simulations of stochastic differential equations” Phys. Rev. A 103, 052425 (2021). arXiv:2012.04429
Y. Takaki, K. Mitarai, M. Negoro, K. Fujii, and M. Kitagawa,
“Learning temporal data with variational quantum recurrent neural network” Phys. Rev. A 103, 52414 (2021). arXiv:2012.11242