PM: Machine Learning Algorithms for Quantum-Logic Spectroscopy of Molecular Ions

This award supports Professor David Leibrandt and his research group at the University of California, Los Angeles to develop and characterize new techniques that will use lasers to measure and control the quantum mechanical state of individual trapped and isolated molecules. This is an interdisciplinary project that spans quantum information and control, molecular physics and physical chemistry, and machine learning and computer science. The potential benefits and applications are correspondingly diverse, ranging from quantum sensing and computing to improving our understanding of chemical physics to enabling searches for physics beyond the Standard Model, which encompasses our understanding of the fundamental particles and their interactions. The goals of this project are to construct a new experimental apparatus and develop machine learning algorithms for quantum-logic spectroscopy of molecular ions (i.e., electrically charged molecules). In quantum-logic spectroscopy, a single molecular ion of interest (BeH+, MgH+, or CaH+ in this work) is co-trapped with a single atomic ion qubit (Sr+ in this work) for sympathetic laser cooling and state measurement based on a two-qubit quantum gate. The research team will develop machine learning algorithms based on the partially observable Markov decision processes framework that select the quantum-logic measurement pulses adaptively and in real-time in order to projectively prepare pure quantum states of the molecule with high fidelity and efficiency. These algorithms will be scalable to polyatomic molecules with many thermally occupied states, enabling precision tests of fundamental physics in future work. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.