Keisuke Fujii

Team Leader, Quantum Computing Theory Research Team


Our team focuses on quantum software engineering science, which is necessary for the realization of quantum computers. In the near future, we will develop basic technologies to exploit the performance of quantum computers on a scale that is now being realized or will be realized in the next few years, as well as applications of them to fundamental physics, quantum chemistry, and quantum machine learning. In the long term, we will work on quantum computer architectures, quantum error correction, and optimization of quantum circuits to realize large scale fault-tolerant quantum computers. We are also interested in interdisciplinary fields between quantum information science and fundamental physics, quantum chemistry, machine learning, and high-performance computing.

Research Theme

・Fault-tolerant quantum computing, Quantum error correction
・Quantum computation models and classes (classical simulatability and quantum computational supremacy)
・Application of Quantum computers (quantum manybody physics, quantum chemistry, quantum machine learning)
・Interdisciplinary study between quantum information science and physics.

Representative Research Results

1.*K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii.:
“Quantum Circuit Learning”
Phys. Rev. A, 98, 032309 (2018)

2.*K. Fujii and K. Nakajima.:
“Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning”
Phys. Rev. Applied, 8, 24030 (2017)

3.*K. Fujii, M. Negoro, N. Imoto, and M. Kitagawa.:
“Measurement-Free Topological Protection Using Dissipative Feedback”
Phys. Rev. X, 4, 041039 (2014)

4.*K. Fujii, Y. Nakata, M. Ohzeki, M. Murao.:
“Measurement-Based Quantum Computation on Symmetry Breaking Thermal States”
Phys. Rev. Lett., 110, 120502 (2013)

5.*K. Fujii and Y. Tokunaga.:
“Fault-Tolerant Topological One-Way Quantum Computation with Probabilistic Two-Qubit Gates”
Phys. Rev. Lett., 105, 250503 (2010)


A quantum circuit for quantum error correction (Left), fault-tolerant quantum computing using the surface code (Right).


Quantum Circuit Learning: A supervised machine learning using parameterized quantum circuits.

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