Quantum State Merging for Arbitrarily Small-Dimensional Systems. - Hayata Yamasaki; Mio Murao (IEEE Trans. Inf. Theory, 2019)

Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions. - Hayata Yamasaki; Sathyawageeswar Subramanian; Sho Sonoda; Masato Koashi (NeurIPS, 2020)

Fast Quantum Algorithm for Learning with Optimized Random Features. - Hayata Yamasaki; Sathyawageeswar Subramanian; Sho Sonoda; Masato Koashi (CoRR, 2020)

General Quantum Resource Theories: Distillation, Formation and Consistent Resource Measures. - Kohdai Kuroiwa; Hayata Yamasaki (Quantum, 2020)

Exponential Error Convergence in Data Classification with Optimized Random Features: Acceleration by Quantum Machine Learning. - Hayata Yamasaki; Sho Sonoda (CoRR, 2021)

Stochastic Gradient Line Bayesian Optimization: Reducing Measurement Shots in Optimizing Parameterized Quantum Circuits. - Shiro Tamiya; Hayata Yamasaki (CoRR, 2021)

Hierarchy of quantum operations in manipulating coherence and entanglement. - Hayata Yamasaki; Madhav Krishnan Vijayan; Min-Hsiu Hsieh (Quantum, 2021)

Activation of genuine multipartite entanglement: Beyond the single-copy paradigm of entanglement characterisation. - Hayata Yamasaki; Simon Morelli; Markus Miethlinger; Jessica Bavaresco; Nicolai Friis; Marcus Huber (Quantum, 2022)

Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation. - Hayata Yamasaki; Sathyawageeswar Subramanian; Satoshi Hayakawa; Sho Sonoda (ICML, 2023)

Advantage of Quantum Machine Learning from General Computational Advantages. - Hayata Yamasaki; Natsuto Isogai; Mio Murao (CoRR, 2023)