CAREER: Active Feedback to Control Dynamic Quantum Phases

非技术总结这个教师早期职业发展（职业生涯）奖支持理论凝聚态思想的发展和整合到动态量子阶段的创新研究，推广和受过教育的，批判性思维的量子劳动力。控制物质的量子相位是量子信息科学和量子凝聚态物理中的一个重要理论和实际问题。即使在经典物理学中，当系统动态变化时，将其引导到特定阶段也是一个复杂而具有挑战性的问题。以天气为例，这是一个典型的例子。天气的混沌动态意味着它变得内在不可预测（大约14天后）。然而，如果通过全球工程的一些伟大的脚，人们可以对地球进行轻微的修改，那么人们可能希望控制天气。虽然这在如此大的尺度上是不切实际的，但这种混沌的理论控制导致了物理系统被控制或不被控制的尖锐相位（即，混沌的）。在他最近的研究中，PI发现了类似量子系统承载量子信息动态阶段的第一个线索，但还有很宽的领域需要探索。这种联系揭示的新概念是，基于收集到的关于物理系统的信息的反馈可以控制量子相位并将其引导到期望的状态。这使得实验能够立即见证这些阶段，并打开了我们可以选择感兴趣的状态来控制的前景，这可以帮助我们解决凝聚态和量子信息科学中的其他问题。在这个项目中，PI和他的团队将开发和应用新的算法，使用高性能计算资源和人工智能方法来发现新的动态量子相，并将混沌系统控制到特定的理想状态。该奖项支持的教育和推广活动包括：（i）创建量子科学播客系列并在主要播客平台上发布，开发量子信息和凝聚态交叉领域的开放源码课程，旨在向学生讲授目前工业中使用的量子技术，以及（iii）与鄂尔多斯研究所合作，为研究生提供工业职业发展机会。技术总结该职业奖支持研究和教育活动，旨在将理论凝聚态思想融入动态量子阶段，用于创新研究，推广和想法，以建立批判性思维的量子劳动力。PI将使用反馈来发现新的动态量子相位，并将混沌系统控制到特定状态。该提案的具体目标是（1）开发混合动态模型（具有测量，单一动态和反馈），该模型具有到系统特定状态的受控相变，以及（2）了解现有的动态相变，并建立一种具有规定反馈和机器学习的控制形式。PI和他的团队将开发新的算法，使用高性能计算资源，并在量子化经典模型，随机量子电路和量子模拟哈密顿（如一维哈伯德模型）中对这些跃迁进行理论理解。该奖项支持的教育和推广活动包括（i）创建量子科学播客系列并在主要播客平台上发布，（ii）开发量子信息和凝聚态交叉点的开源课程，（iii）开发量子信息和凝聚态交叉点的开源课程，（iv）开发量子信息和凝聚态交叉点的开源课程。该奖项旨在向学生传授目前在工业中使用的量子技术，以及（iii）与鄂尔多斯研究所合作，为研究生提供工业职业发展机会。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。

NONTECHNICAL SUMMARYThis Faculty Early Career Development (CAREER) award supports the development and integration of theoretical condensed matter ideas into dynamic quantum phases for innovative research, outreach, and an educated, critical-thinking quantum workforce. Controlling quantum phases of matter is an important theoretical and practical problem in quantum information science and quantum condensed matter physics. Even in classical physics, steering a system, as it dynamically changes, into a particular phase is a complicated and challenging problem. Take, for example, the weather as a classical example. The chaotic dynamics of weather means that it becomes inherently unpredictable (after roughly 14 days). However, if, through some great feet of global engineering, one could make slight modifications to the planet, then one might hope to control the weather. While this is impractical on such large scales, such theoretical control of chaos leads to sharp phases where the physical system is either controlled or uncontrolled (i.e., chaotic). In his recent studies, the PI has found the first hints that the analogous quantum systems host dynamic phases of quantum information, but there is a wide frontier to explore. The new concept that this connection reveals is that feedback based on information gathered about a physical system can control the quantum phase and steer it into a desired state. This allows experiments to immediately witness the phases, and it opens the prospect that we can choose interesting states to control that could help us solve other problems in condensed matter and quantum information sciences. In this project, the PI and his team will develop and apply new algorithms, employ high-performance computing resources and artificial intelligence methods to uncover new dynamic quantum phases and control chaotic systems onto specific, desired states of matter.The education and outreach activities supported by this award include (i) creating a podcast series on quantum science and releasing it on major podcasting platforms, (ii) the development of an open-source course at the intersection of quantum information and condensed matter, aimed at teaching students about the quantum technologies currently being used in industry, and (iii) working with the Erdos institute to bring industrial career development opportunities to graduate students.TECHNICAL SUMMARYThis CAREER award supports research and education activities that are aimed at integrating theoretical condensed matter ideas into dynamic quantum phases for innovative research, outreach, and ideas to build a critical-thinking quantum workforce. The PI will use feedback to uncover new dynamic quantum phases and control chaotic systems onto specific states. The specific objectives of this proposal are to (1) develop hybrid dynamic models (with measurements, unitary dynamics, and feedback) that have a controlled phase transition onto specific states of the system and (2) learn about existing dynamical phase transitions and institute a form of control with prescribed feedback and machine learning. The PI and his team will develop new algorithms, employ high-performance computing resources, and develop a theoretical understanding of these transitions in quantized classical models, random quantum circuits, and quantum simulation of Hamiltonians such as the one-dimensional Hubbard model. The goal is to understand dynamic, ergodic, quantum phases, uniting theoretical condensed matter with quantum information sciences in the context of quantum control theory.The education and outreach activities supported by this award include (i) creating a podcast series on quantum science and releasing it on major podcasting platforms, (ii) the development of an open-source course at the intersection of quantum information and condensed matter, aimed at teaching students about the quantum technologies currently being used in industry, and (iii) working with the Erdos institute to bring industrial career development opportunities to graduate students.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.