报告时间：2025年9月22日 下午14:00 (14:00, Sept.22, 2025)

报告地点：物质科研楼A309会议室 Room A309 Material Science Building)

报告人：  汪杨博士  德国斯图加特大学第三物理研究所

报告题目/Title: Learning to steer quantum many-body dynamics with artificial intelligence

摘要/Abstract:

High-quality control over complex quantum systems is a key to achieving practical quantum technologies. However, progress is hindered by the exponential growth of quantum state spaces and the challenges posed by realistic experimental conditions. Here, we present an AI framework that learns to design pulse sequences for optimized quantum control over many-body spin systems, providing a powerful alternative to theory-driven methods. The framework combines customized tree search, neural network filtering, and numerical simulation guidance to navigate highly nonlinear optimization landscapes, using only desktop-level computational resources and minimal experimental input. The objective function is set to preserve coherence, a key prerequisite for quantum information processing. Our framework identifies over 900 high-performing sequences that exhibit non-intuitive structures and hence challenge long-standing design principles, while established optimization methods struggle to find such solutions. Experiments in a diamond spin ensemble show that the best AI-designed sequences achieve coherence times exceeding 200~\si{\micro\second}, representing a 100\% improvement over state-of-the-art baselines and approaching the temperature-imposed limit. Beyond spin coherence preservation, our framework is readily extendable through modified objective functions and incorporation of appropriate training data. This work highlights AI's potential to steer complex quantum many-body dynamics, marking a paradigm shift toward data-driven sequence design with broad applicability across spin-based quantum technologies and beyond.

报告人简介/Curriculum Vitae:

汪杨博士先后获得德国亚琛工业大学硕士学位和荷兰代尔夫特理工大学博士学位，博士阶段师从Barbara Terhal教授与Tim Taminiau研究员。现为德国斯图加特大学第三物理研究所博士后，合作导师为Joerg Wrachtrup教授。他目前的研究聚焦于推动固态自旋体系的量子技术走向实际应用。