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

该奖项支持洛杉矶加州大学的大卫·莱布兰特教授和他的研究小组开发和表征新技术，这些技术将使用激光来测量和控制单个被困和孤立分子的量子力学状态。这是一个跨学科项目，涵盖量子信息与控制、分子物理与物理化学以及机器学习与计算机科学。潜在的好处和应用也相应地多种多样，从量子传感和计算，到提高我们对化学物理的理解，再到搜索标准模型之外的物理，该项目的目标是构建一个新的实验装置，并开发用于分子离子量子逻辑光谱学的机器学习算法（也就是说，带电分子）。在量子逻辑光谱学中，单个感兴趣的分子离子（在这项工作中为BeH+、MgH+或CaH+）与单个原子离子量子比特（在这项工作中为Sr+）共捕获，用于基于双量子比特量子门的共振激光冷却和状态测量。该研究团队将开发基于部分可观察马尔可夫决策过程框架的机器学习算法，该算法自适应地实时选择量子逻辑测量脉冲，以便以高保真度和效率投射制备分子的纯量子态。这些算法将可扩展到具有许多热占据状态的多原子分子，从而在未来的工作中实现基础物理的精确测试。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.