ExpandQISE: Track 1: Reimagining Adaptive Quantum Algorithms

非技术描述：这项研究专注于使用量子计算机解决科学中重要问题的新方法，广泛地解决各种实际应用的广泛问题。特别是，该项目将开发一系列量子算法，能够解决包括健康、农业和气候在内的具有社会影响的重要问题。教育和外展活动在强大的量子信息科学和工程(QISE)劳动力的增长和多样化方面发挥着关键作用。从这个项目开发的教科书将使所有学生都可以使用QISE，并可能影响线性代数的介绍方式。技术描述：量子模拟算法面临着几个挑战，涉及ANSATZ选择、噪声、崎岖的优化环境以及对大规模问题的适用性。该项目旨在通过开发一系列量子算法来克服这些挑战，这些算法可以自适应地创建执行基态量子模拟所需的量子电路。这是通过一系列广泛的自适应算法来实现的，这些算法利用确定性和随机元素以及变分和非变分方法，以抗噪声和可靠的方式有效地准备基态。不涉及经典优化例程的随机化自适应策略有望提供收敛保证，而附加经典优化则可提高收敛速度。使用IBM最大的量子设备在现实条件下对性能进行基准测试。所获得的洞察力被用来设计最有效的自适应量子算法，这些算法具有抗噪声能力，并产生高收敛保证。虽然这些研究活动预计将通过提供解决策略，使用现有的和近期的、嘈杂的量子硬件来解决广泛的问题，从而对整个科学产生深远的影响，但该研究也为容错时代的可靠和高效的量子态准备建立了亟需的算法。该项目包括教育和外联活动，使所有背景的学生都能接触到量子科学，并可能在强大的QISE劳动力的增长和多样化方面发挥关键作用。通过让学生在早期参与研究，这项研究被紧密地整合到劳动力发展计划中。劳动力发展计划的基石是一种图形主义，它让学生接触到量子计算，而不需要复杂的数学概念。这一形式主义是在一本教科书中发展起来的，这本教科书同时作为设计一门新生量子信息科学与工程课程的指南，并在QISE暑期项目中培训高中教师和K12学生。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:This research focuses on new methods for solving important problems in science using quantum computers, broadly addressing a wide range of problems for various practical applications. In particular, the project will develop a family of quantum algorithms that are able to solve important problems of societal impact, including in health, agriculture, and climate. The education and outreach activities play a critical role in the growth and diversification of a strong quantum information science and engineering (QISE) workforce. The developed textbook from this project will make QISE accessible to all students and may also influence how linear algebra is introduced. Technical Description:Quantum simulation algorithms face several challenges associated with ansatz selection, noise, rugged optimization landscapes, and applicability to large-scale problems. The project aims to overcome these challenges by developing a family of quantum algorithms that adaptively create the quantum circuit needed to carry out ground state quantum simulation. This is achieved through a broad family of adaptive algorithms that leverage deterministic as well as random elements and variational and non-variational approaches, to efficiently prepare ground states in a noise-robust and reliable manner. Randomized adaptive strategies that do not involve classical optimization routines are expected to provide convergence guarantees, while additionally adding classical optimization enhances the speed of convergence. Performance is benchmarked and tested under realistic conditions using IBM’s largest quantum devices. The insights gained are leveraged to design the most efficient adaptive quantum algorithms that are noise-robust and yield high convergence guarantees. While the research activities are expected to have a profound effect across the sciences by providing solution strategies that use existing and near-term, noisy quantum hardware to solve a wide range of problems, the research also establishes the much-needed algorithms for reliable and efficient quantum state preparation in the fault-tolerant era. The project includes educational and outreach activities that make quantum science accessible to students from all backgrounds and that could play a critical role in the growth and diversification of a strong QISE workforce. The research is tightly integrated into the workforce development program by engaging students in research early on. The cornerstone of the workforce development program is a pictorial formalism that exposes students to quantum computing without the need for elaborate mathematical concepts. This formalism is developed in a textbook, which simultaneously serves as a guide to designing a freshman quantum information science and engineering course and training high-school teachers and K12 students in QISE summer programs.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.