• R. Usuki, D. Arai, K. N. Okada, and K. Fujii,
    “Enhancing Pauli Correlation Encoding for quantum optimization via systematic expressivity analysis” arXiv:2609.09718
  • S. Shoji, K. Ito, Y. Shimizu, and K. Fujii,
    “Variational Quantum Operator Simulation” Phys. Rev. A 114(2), 022414(2026). arXiv:2603.06013 
  • K. Sakamoto and K. Fujii,
    “On the quantum computational complexity of classical linear dynamics with geometrically local interactions: Dequantization and universality”
    Quantum 10, 2182(2026). arXiv:2505.10445
  • K. Numata, W. Mizukami, K. Mitarai, K. Fujii, and Y. Imamura,
    “Quantum machine learning interatomic potential: Application of variational quantum algorithm” arXiv:2607.27841
  • T. Yoshioka, K. Sasada, R. Usuki, Y. Nakano, and K. Fujii,
    “Scalable Variational Quantum Optimization via Pauli Correlation Encoding: Application to Large-Scale Power Demand Portfolio Optimization“ arXiv:2607.24722
  • Y. Akahoshi, R. Toshio, J. Fujisaki, H. Oshima, S. Sato, and K. Fujii,
    “Runtime reduction in lattice surgery utilizing timelike soft information”
    Phys. Rev. Research 8(3), 033085(2026). arXiv:2510.21149
  • Y. Mori, H. Hakoshima, and K. Fujii,
    “Nontrivial Multiproduct Commutation Relation Toward Reducing 𝑇-Count in Sequential Pauli-Based Computation” PRX Quantum 7(2), 020345(2026). arXiv:2509.20052
  • T. Itogawa, Y. Hirano, Y. Akahoshi, and K. Fujii,
    “Zero-level CCZ Distillation” arXiv:2605.21867
  • K. Yamamoto and K. Fujii,
    “Efficient Fault-Tolerant Ancilla Preparation for Quantum BCH codes via Cyclic Symmetry” arXiv:2605.19471
  • M. Mishima, R. Toshio, K. Kishi, J. Fujisaki, H. Oshima, S. Sato, and K. Fujii,
    “Adaptive Window Decoding based on Spatiotemporal Complementary Gap” arXiv:2605.14637
  • Y. Kawamata, K. Mitarai, and K. Fujii,
    “Quasi-Monte Carlo Method for Linear Combination Unitaries via Classical Post-Processing” Phy. Rev. Res. 8(2), 023161(2026). arXiv:2509.14451
  • Y. Ito, H. Mori, K. Sakamoto, and K. Fujii,
    “Polynomial time constructive decision algorithm for multivariable quantum signal processing” Quantum 10, 2102(2026). arXiv:2410.02332
  • Y. Kawamata, Y. Nakano, and K. Fujii,
    “Divide-and-Conquer Neural Network Surrogates for Quantum Sampling: Accelerating Markov Chain Monte Carlo in Large-Scale Constrained Optimization Problems” arXiv:2604.20701
  • L. Placidi, E. Rinaldi, K. Fujii, and CY. Liu,
    “The Impact of Qubit Connectivity on Quantum Advantage in Noisy IQP Circuits” arXiv:2604.12635