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

非技术描述：该项目旨在为可编程量子传感器电路的优化设计创建和评估基于量子和经典强化学习的代理。这项研究项目产生的技术将允许更好地测量物理世界的仪表，以及连接许多科学领域的广泛应用。该项目的成果将对精密量子测量系统产生积极影响，例如噪音较大的中间尺度量子器件。该项目将为量子信息科学与工程(QISE)的研究、培训和专业发展提供丰富的机会。该项目团队将在QISE研究方法方面招募、激励和培训多样化和代表性不足的少数民族和女性学生。该项目将对克利夫兰市K-16 QISE人才培养管道和劳动力发展产生积极影响，在克利夫兰市，非洲裔美国人和拉美裔等代表性不足的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，在过去的几十年里取得了显著的进步。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设定的基本极限之间的差距。然而，产生纠缠量子比特的量子传感器电路的优化设计是一项不平凡的任务，这促使机器学习的考虑来辅助这一设计。目前的努力在很大程度上局限于对应于具有较少元件的简单电路的较少参数系统的变分优化。为了提高这方面的研究水平，本项目提出了一种基于强化学习的可编程量子传感器电路优化设计方法。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层电路。该方法利用动作和奖励的学习循环来生成性能最优的门序列，并使用量子Fisher信息作为量化奖励的手段。该方法包括多个组成部分，如理想系统的演示、噪声和缺陷的评估、对量子代理的扩展，以及针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、对耗散和消相干的稳健性以及速度。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.