Machine Learning Applications in Complex and Quantum Systems
POSTER
Abstract
AI for Science applies versatile machine-learning methods to accelerate discovery—revealing phenomena and governing laws in complex systems and enabling precise quantum control. This poster presents a big-picture view of our efforts: ML-driven optimization of synchronization in laser networks, data-guided protocols for entanglement engineering and ergodicity control in many-body quantum systems, and quantum reservoir computing.
Publication: Ye, Li-Li, Nathan Vigne, Fan-Yi Lin, Hui Cao, and Ying-Cheng Lai. "Disorder-mediated synchronization resonance in coupled semiconductor lasers." arXiv preprint arXiv:2509.07302 (2025).
Ye, Li-Li, Christian Arenz, Joseph M. Lukens, and Ying-Cheng Lai. "Entanglement engineering of optomechanical systems by reinforcement learning." APL Machine Learning 3, no. 1 (2025).
Ye, Li-Li, and Ying-Cheng Lai." Controlling nonergodicity in quantum many-body systems by reinforcement
learning." Physical Review Research 7, no. 1 (2025): 013256.
Presenters
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Lili Ye
- Arizona State University