CAREER: Developing Quantum Algorithms for High-Entropy Alloy Discovery

非技术总结这个职业奖项支持理论和计算研究，这些研究在嘈杂的中等规模量子时代采用量子计算机来发现被称为高熵合金(HEAs)的新型合金，高熵合金指的是由相同或几乎相同浓度的多种元素组成的合金。典型的HEA采用单相固溶体结构，其中组成元素的原子位于固定晶格的随机位置。由某些元素组合制成的HEA具有独特的性能，如平衡的延展性和强度，这是传统合金所不具备的，在传统合金中，一种元素的含量决定了整体的浓度。发现HEAs是一个复杂的组合问题，其中元素的最佳选择及其对应的摩尔比显著影响所得到的材料的性能。该项目旨在通过开发量子算法并在近期的量子计算机上实施来解决这个问题，以寻找人类基因组。该奖项还支持PI的教育和推广活动，旨在为即将到来的人工智能和量子计算革命做准备。国际量子研究所将(I)通过亚利桑那州立大学(ASU)现有的量子合作为从业人员提供一个综合培训平台，并通过教育和研究机会、课程和论文项目以及暑期实习为多元化的学生群体提供一个综合培训平台，(Ii)根据亚利桑那州立大学的现有推广计划，如亚利桑那州立大学的“科学和工程经验”计划，培训研究生和指导代表不足的高中生，(Iii)在美国物理学会等主要研究学会的年会上组织研讨会，(Iv)组织一期特别期刊，以收集报告HEA研究前沿的稿件，以及(V)参加由美国-非洲电子结构倡议组织的各种活动，以加强非洲和美国物理学家之间的合作。TECHNICAL SUMMARY该职业奖项支持理论和计算研究，旨在阐明与高熵合金(HEA)材料发现相关的潜在物理和机制。PI将(I)开发量子编码算法以将经典HEA数据转换为量子数据，这将促进后续的量子搜索、学习和探索，并帮助理解HEA的组成元素和浓度对熵稳定相的影响，(Ii)开发使用编码的量子态作为输入的量子搜索算法，以实现与经典搜索相比在搜索HEA数据库中的更低的时间复杂性，(Iii)执行量子机器学习计算以确定HEA的相位选择，并获得可与经典机器学习模型相媲美的预测精度水平，以及(Iv)开发量子行走算法来探索高维组成空间，以实现快速探索结构-性质关系，以发现新的HEAs。该奖项还支持PI的教育和外联活动，旨在为即将到来的人工智能和量子计算革命做准备。国际量子研究所将(I)通过亚利桑那州立大学(ASU)现有的量子合作为从业人员提供一个综合培训平台，并通过教育和研究机会、课程和论文项目以及暑期实习为多元化的学生群体提供一个综合培训平台，(Ii)根据亚利桑那州立大学的现有推广计划，如亚利桑那州立大学的“科学和工程经验”计划，培训研究生和指导代表不足的高中生，(Iii)在美国物理学会等主要研究学会的年会上组织研讨会，(Iv)组织一期特别期刊，以收集报告HEA研究前沿的稿件，以及(V)参加美国-非洲电子结构倡议组织的各种活动，以加强非洲和美国物理学家之间的合作。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。

NONTECHNICAL SUMMARYThis CAREER award supports theoretical and computational research that adopts quantum computers in the noisy intermediate-scale quantum era to discover novel alloys known as high-entropy alloys (HEAs), which refer to alloys consisting of multiple elements with the same or nearly the same concentration. A typical HEA adopts a single-phase solid solution structure, where atoms of the constituent elements are located at random sites of a fixed crystal lattice. HEAs made of certain combinations of elements possess unique properties, such as balanced ductility and strength, that are absent in the conventional alloys, where the content of one element dominates the overall concentration. Discovering HEAs is a complex combinatorial problem, where an optimal selection of elements and their corresponding molar ratios significantly affect the resulting materials' properties. This project tackles this problem to search for the "Materials Genome" of HEAs via developing quantum algorithms and implementing them on near-term quantum computers.This award also supports the PI’s educational and outreach activities that aim to prepare for upcoming revolutions in artificial intelligence and quantum computing. The PI will (i) train quantum workforce by providing an integrated training platform through existing Quantum Collaborative at Arizona State University (ASU) for practitioners and a diversified student body through education and research opportunities, course and thesis projects, and summer internships, (ii) train graduate students and mentor underrepresented high school students based upon the existing outreach programs such as the "Science and Engineering Experience" program at ASU, (iii) organize symposia in the annual meetings of main research societies such as the American Physical Society, (iv) organize a special journal issue to collect contributions reporting the frontier of HEA research, and (v) participate in various activities organized by the U.S.-Africa Initiative in Electronic Structure to enhance collaborations between African and U.S. physicists.TECHNICAL SUMMARYThis CAREER award supports theoretical and computational research with an aim to elucidate the underlying physics and mechanisms associated with the materials discovery of high entropy alloys (HEAs). The PI will (i) develop quantum encoding algorithms to convert classical HEA data to quantum data, which will facilitate subsequent quantum search, learning, and exploring and help understand the effects of constituent elements and concentrations of HEAs on the entropy-stabilized phases, (ii) develop quantum search algorithms that use the encoded quantum states as inputs to achieve reduced time complexity in searching a HEA database comparing with classical search, (iii) perform quantum machine learning computations to determine the phase selection of HEAs and achieve a prediction accuracy level that is comparable to classical machine learning models, and (iv) develop quantum walk algorithms to explore the high-dimensional compositional space to achieve rapid explorations of structure-property relationships to discover new HEAs. This award also supports the PI’s educational and outreach activities that aim to prepare for upcoming revolutions in artificial intelligence and quantum computing. The PI will (i) train quantum workforce by providing an integrated training platform through existing Quantum Collaborative at Arizona State University (ASU) for practitioners and a diversified student body through education and research opportunities, course and thesis projects, and summer internships, (ii) train graduate students and mentor underrepresented high school students based upon the existing outreach programs such as the "Science and Engineering Experience" program at ASU, (iii) organize symposia in the annual meetings of main research societies such as the American Physical Society, (iv) organize a special journal issue to collect contributions reporting the frontier of HEA research, and (v) participate in various activities organized by the U.S.-Africa Initiative in Electronic Structure to enhance collaborations between African and U.S. physicists.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.