Hamiltonian-Driven Shadow Tomography of Quantum States. - Hong-Ye Hu; Yi-Zhuang You (CoRR, 2021)

Neural ordinary differential equation and holographic quantum chromodynamics. - Koji Hashimoto; Hong-Ye Hu; Yi-Zhuang You (Mach. Learn. Sci. Technol., 2021)

Differentiable programming of isometric tensor networks. - Chenhua Geng; Hong-Ye Hu; Yijian Zou (Mach. Learn. Sci. Technol., 2022)

RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior. - Hong-Ye Hu; Dian Wu; Yi-Zhuang You; Bruno A. Olshausen; Yubei Chen (Mach. Learn. Sci. Technol., 2022)

Discovery of Optimal Quantum Error Correcting Codes via Reinforcement Learning. - Vincent Paul Su; ChunJun Cao; Hong-Ye Hu; Yariv Yanay; Charles Tahan; Brian Swingle (CoRR, 2023)

Scalable and Flexible Classical Shadow Tomography with Tensor Networks. - Ahmed A. Akhtar; Hong-Ye Hu; Yi-Zhuang You (Quantum, 2023)

Digital-analog quantum learning on Rydberg atom arrays. - Jonathan Z. Lu; Lucy Jiao; Kristina Wolinski; Milan Kornjaca; Hong-Ye Hu; Sergio Cantu; Fangli Liu; Susanne F. Yelin; Sheng-Tao Wang (CoRR, 2024)

Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows. - Hong-Ye Hu; Andi Gu; Swarnadeep Majumder; Hang Ren; Yipei Zhang; Derek S. Wang; Yi-Zhuang You; Zlatko Minev; Susanne F. Yelin; Alireza Seif (CoRR, 2024)

Derandomized shallow shadows: Efficient Pauli learning with bounded-depth circuits. - Katherine Van Kirk; Christian Kokail; Jonathan Kunjummen; Hong-Ye Hu; Yanting Teng; Madelyn Cain; Jacob Taylor; Susanne F. Yelin; Hannes Pichler; Mikhail D. Lukin (CoRR, 2024)

Zero and Finite Temperature Quantum Simulations Powered by Quantum Magic. - Andi Gu; Hong-Ye Hu; Di Luo; Taylor L. Patti; Nicholas C. Rubin; Susanne F. Yelin (Quantum, 2024)

Ansatz-free Hamiltonian learning with Heisenberg-limited scaling. - Hong-Ye Hu; Muzhou Ma; Weiyuan Gong; Qi Ye; Yu Tong 002; Steven T. Flammia; Susanne F. Yelin (CoRR, 2025)