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可编程量子传感器电路的最佳设计的量子和经典增强剂。该研究项目的最终技术将允许在弥合许多科学领域的广泛应用程序中获得更好的物理世界。该项目的结果将对精确量子增强的计量测量系统（例如嘈杂的中间尺度量子设备）产生积极影响。该项目将为量子信息科学与工程（QISE）研究培训和专业发展提供丰富的机会。项目团队将以QISE研究方法招募，激励和培训潜水员和代表性不足的少数民族和女学生。该项目将对克利夫兰市的K – 16 QISE人才发展渠道和劳动力发展产生积极影响，克利夫兰市在那里代表性不足的少数民族（例如非裔美国人和西班牙裔人）构成了大多数人口。技术描述：量子灵敏度是一种成熟的技术，在过去几十年中取得了显着的进步。未来的挑战是利用量子纠缠和叠加的潜在收益来实现下一代传感器，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。但是，生成纠缠量子设计的量子传感器电路的最佳设计是一项非平凡的任务，它激发了对机器学习的考虑以协助这种设计。当前的努力很大程度上仅限于几个参数系统的变分优化，这些参数系统与几乎没有元素的简单电路相对应。为了推进最新技术的状态，在该项目中，为可编程量子传感器开发了基于加强学习的最佳电路设计。具体的目标是创建和评估基于量子和经典的增强剂，以设计深度电路。该方法利用量子Fisher信息作为量化奖励的手段来利用动作和奖励的学习周期和奖励以最佳性能生成门的顺序。该方法涉及多个组件，例如理想系统的演示，噪声和缺陷的评估，对量子剂的扩展以及对可编程量子传感器电路设计的经典和量子代理的性能评估。用于评估研究方法成功的指标包括灵敏度，动态范围，对耗散和脱骨的稳健性以及速度。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来通过评估来获得的支持。