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

PM：分子离子量子逻辑光谱的机器学习算法

• 批准号: 2309315
• 负责人: David Leibrandt
• 金额: $ 69.24万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309315&HistoricalAwards=false
• 关键词: PM; Machine; Learning; Algorithms; Quantum

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.

该奖项支持加州大学洛杉矶分校的David Leibrandt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捕获和孤立分子的量子力学状态。这是一个跨学科的项目，横跨量子信息与控制、分子物理与物理化学、机器学习与计算机科学。相应地，潜在的好处和应用也是多种多样的，从量子传感和计算到提高我们对化学物理的理解，到能够在标准模型之外搜索物理，标准模型包含了我们对基本粒子及其相互作用的理解。这个项目的目标是构建一个新的实验设备，并为分子离子(即带电分子)的量子逻辑光谱开发机器学习算法。在量子逻辑光谱学中，感兴趣的单个分子离子(在本工作中为BEH+、MgH+或CaH+)与单个原子离子量子比特(在本工作中为锶+)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。该研究小组将开发基于部分可观测马尔可夫决策过程框架的机器学习算法，该框架自适应地、实时地选择量子逻辑测量脉冲，以投射出高保真、高效率的分子纯量子态。这些算法将可扩展到具有许多热占据态的多原子分子，从而能够在未来的工作中对基础物理进行精确测试。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。