AI-Enabled Molecular State Control using Quantum-Logic Spectroscopy

Precision measurements of polyatomic molecules offer an unparalleled paradigm to probe physics beyond the Standard Model. The rich internal structure within these molecules makes them exquisite sensors for detecting fundamental symmetry violations, local position invariance, and dark matter. While trapping and controlling diatomic and a few simple polyatomic molecules have been experimentally demonstrated, leveraging the complex rovibrational structure of more general polyatomics demands the development of robust and efficient quantum control schemes. This talk presents a general, reinforcement-learning-designed, quantum logic approach to prepare molecular ions in a single, pure quantum state. The reinforcement learning agent optimizes the pulse sequence, each followed by a projective measurement, and probabilistically manipulates the collapse of the quantum system to a single state. The performance of the control algorithm has been numerically demonstrated in the case of a CaH$^+$ ion, with up to 96 thermally populated eigenstates and under the disturbance of environmental thermal radiation [1]. We generalize this to AI-enabled methods for the quantum control of hydronium to allow for new experimental tests of fundamental theories.

1. [1] A. Pipi, X. Tao, P. Narang, and D.R. Leibrandt (2024). Molecular Quantum Control Algorithm Design by Reinforcement Learning. arXiv preprint arXiv:2410.11839.

This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. This work was supported by NSF CAREER Award under grant number ECCS 2246394, NSF QuSeC-TAQS 2326840, NSF ExpandQISE 2231387, NSF PHY 2309315, and Moore Foundation EPI 12252.