Expand QISE: Track 1: RLQSC: Reinforcement Learning for the Optimal Design of Programmable Quantum Sensor Circuit

展开 QISE：轨道 1：RLQSC：用于可编程量子传感器电路优化设计的强化学习

• 批准号: 2231377
• 负责人: Sathish Kumar
• 金额: $ 80万
• 依托单位: Cleveland State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2231377&HistoricalAwards=false
• 关键词: Expand; QISE; Track; RLQSC; Reinforcement

Non-technical Description:The project aims to create and evaluate quantum and classical reinforcement learning-based agents for the optimal design of programmable quantum sensor circuit. The resulting technology from this research project will allow for better meters of the physical world with a breadth of applications that bridge many fields of science. The outcomes from this project will have a positive impact on the precision quantum-enhanced metrology measurements systems, such as the Noisy Intermediate Scale Quantum devices. The project will provide rich opportunities for Quantum Information Science and Engineering (QISE) research training and professional development. The project team will recruit, motivate, and train diverse and underrepresented minority and female students in QISE research methods. The project will have a positive impact on the K–16 QISE talent development pipeline and workforce development for the city of Cleveland, where underrepresented minorities such as African Americans and Hispanics constitute the majority of the population. Technical Description:Quantum sensing is a mature technology that has achieved remarkable progress over the past decades. The challenge going forward is to leverage potential gains from quantum entanglement and superposition to enable the next generation of sensors and thereby narrow the gap between the current performance and the fundamental limits set by quantum physics. However, the optimal design of a quantum sensor circuit that generates entangled qubits is a non-trivial task, which motivates the consideration of machine learning to assist with this design. Current efforts have in large part been limited to variational optimization of few parameter systems corresponding to simple circuits with few elements. To advance the state of the art, in this project, a reinforcement-learning-based optimal circuit design is developed for programmable quantum sensors. The specific objective is to create and evaluate quantum and classical reinforcement learning-based agents to design the deep circuit. The method utilizes a learning cycle of actions and rewards to generate the sequence of gates with optimal performance, using the measure of quantum Fisher information as a means to quantify the reward. The methodology involves multiple components such as demonstrations of the ideal system, evaluation of noise and imperfections, extensions to a quantum agent, and the performance evaluation of classical and quantum agents towards the design of the programmable quantum sensor circuit. Metrics used to evaluate the success of the research approach include sensitivity, dynamic range, robustness to dissipation and decoherence, and speed.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.

非技术描述：该项目旨在创建和评估基于量子和经典强化学习的代理，以实现可编程量子传感器电路的优化设计。该研究项目所产生的技术将能够更好地测量物理世界。该项目的成果将对精密量子增强计量测量系统产生积极影响，例如噪声中尺度量子设备。该项目将为量子信息科学和工程提供丰富的机会。 (QISE) 研究培训和专业发展。该项目团队将招募、激励和培训多元化和代表性不足的少数族裔和女性学生。该项目将对 K-16 QISE 人才发展渠道和劳动力发展产生积极影响。对于克利夫兰市来说，非洲裔美国人和西班牙裔等少数族裔占人口的大多数。 技术描述：量子传感是一项成熟的技术，在过去几十年中取得了显着的进步，未来的挑战是如何利用潜在的收益。来自量子纠缠和叠加以实现下一代传感器，从而缩小当前性能与量子物理学设定的基本限制之间的差距。然而，生成纠缠量子位的量子传感器电路的优化设计是一项艰巨的任务，这激励了人们。考虑机器学习来协助这种设计，目前的努力在很大程度上仅限于与具有很少元素的简单电路相对应的少数参数系统的变分优化，在这个项目中，强化学习-基于优化电路设计的开发可编程量子传感器的具体目标是创建和评估基于量子和经典强化学习的代理来设计深度电路，该方法利用动作和奖励的学习周期来生成具有最佳性能的门序列。该方法涉及多个组成部分，例如理想系统的演示、噪声和缺陷的评估、量子代理的扩展以及针对设计的经典和量子代理的性能评估。使用的可编程量子传感器电路。评估研究方法成功与否的指标包括灵敏度、动态范围、对耗散和退相干的鲁棒性以及速度。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。