报告题目: Ansatz-free Hamiltonian learning with Heisenberg-limited scaling

报 告 人：Hong-Ye Hu

报告时间：2025年11月20日10:00

线上报告：#腾讯会议：224-573-295

内容摘要: Learning the unknown interactions that govern a quantum system is crucial for quantum information processing and quantum sensing. The problem, known as Hamiltonian learning, is well understood under the assumption that interactions are local, but this assumption may not hold for arbitrary Hamiltonians. Whether Heisenberg-limited Hamiltonian learning is possible without prior assumptions about the structures, a challenge we term ansatz-free Hamiltonian learning, remains an open question. In this talk, I will present a quantum algorithm to learn arbitrary sparse Hamiltonians without any structure constraints using only black-box queries of the system's real-time evolution and minimal digital controls to attain Heisenberg-limited scaling in estimation error. Moreover, I will discuss a fundamental trade-off between total evolution time and quantum control on learning arbitrary interactions.

参考文献：

[1] “Ansatz-free Hamiltonian learning with Heisenberg-limited scaling”, PRX Quantum 6, 040315 (2025)

[2] “Quantum Systems Modeled Without Prior Assumptions”, Physics 18, 165 (2025)

报告人简介：Hong-Ye Hu is a Harvard Quantum Initiative (HQI) Fellow whose research develops scalable methods for quantum control, verification, and learning in complex many-body systems, alongside modern deep-learning approaches that enable early fault-tolerant applications. On ultracold-atom platforms, he designs novel quantum gates, including new fermionic gates for probing d-wave superconductivity (featured by Physics World). He earned his Ph.D. from University of California San Diego in 2022 and collaborates extensively with the industry, including IBM Quantum, QuEra Computing, NASA’s Quantum AI Lab, and Rigetti Computing.