Collaborative Research: NRT-QL: A Program for Training a Quantum Workforce

合作研究：NRT-QL：量子劳动力培训计划

• 批准号: 2125906
• 负责人: Hilary Hurst
• 金额: $ 73.9万
• 依托单位: San Jose State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125906&HistoricalAwards=false
• 关键词: Collaborative; Research; NRT; QL; Program

Recent progress in quantum information science and technology (QIST) promises advances that will fundamentally reshape today’s leading technologies, including computing, communications, and sensing. A well-prepared workforce is essential to fulfilling this promise. In response to this critical workforce need, this National Science Foundation Research Traineeship (NRT) award to the Colorado School of Mines (Mines) and San Jose State University (SJSU) supports the development of rigorous, integrated, interdisciplinary training programs preparing both Master’s and Doctoral students for careers in QIST. The program has three components that address key challenges in training a quantum workforce. First, new training materials will be developed to introduce QIST basics to students from different disciplines. Trainees will come from physics, electrical engineering, metallurgical and materials engineering, applied mathematics and statistics, computer science, and quantum engineering. Second, industrial and national lab partners will be engaged to provide trainees access to cutting-edge technologies and research experiences. Finally, student recruitment and mentoring programs, unconscious bias training, ally workshops, and regular climate surveys will be developed to ensure diversity in the QIST workforce. The project will train one-hundred and twenty-five (125) MS and Ph.D. students, including thirty-five (35) fully-funded trainees. These efforts will result in an institutionalized program that produces leaders in QIST for careers in academia, industry, government, and national labs. The research program addresses three grand challenges in quantum technology: (1) How can demonstrable quantum speed-up using existing or near-term quantum computing architectures be achieved? (2) How can materials and electrical engineering interface with quantum technology to provide meaningful advances in device development and fabrication? (3) How can new directions in quantum algorithms advance and challenge the notion that quantum technology can solve problems that are impossible to tackle with conventional technology? These interdisciplinary research challenges require close synergy between fundamental physics, materials science, electrical engineering, and algorithm development. New courses and degree pathways in QIST are being developed at both Mines and SJSU, which include integration of professional skills development into the graduate curriculum. Through classroom training and research, trainees will be exposed to the full range of quantum technology architectures, providing a holistic view of this rapidly developing field. An innovative educational agreement between Mines and SJSU will provide opportunities for students to complete an MS/Ph.D. bridge program from SJSU to Mines. The program will result in a template for QIST graduate education, which will be accessible to STEM students from all backgrounds and types of institutions, increasing diversity in QIST. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.

量子信息科学与技术（QIST）的最新进展有望从根本上重塑当今的领先技术，包括计算，通信和传感。一支准备充分的劳动力队伍对于实现这一承诺至关重要。为了应对这一关键的劳动力需求，这个国家科学基金会研究培训（NRT）奖给矿业（矿山）和圣何塞州立大学（SJSU）的科罗拉多学校支持严格的，综合的，跨学科的培训计划的发展，准备硕士和博士生在QIST的职业生涯。该计划有三个组成部分，解决了培训量子劳动力的关键挑战。首先，将开发新的培训材料，向来自不同学科的学生介绍QIST的基础知识。受训人员将来自物理学，电气工程，冶金和材料工程，应用数学和统计学，计算机科学和量子工程。其次，工业和国家实验室合作伙伴将参与为学员提供尖端技术和研究经验。最后，学生招聘和辅导计划，无意识的偏见培训，盟友研讨会，并定期气候调查将开发，以确保在QIST劳动力的多样性。该项目将培养一百二十五（125）名硕士和博士。学生，包括三十五（35）全额资助的受训人员。这些努力将导致一个制度化的计划，产生领导者在QIST的职业生涯在学术界，工业界，政府和国家实验室。该研究计划解决了量子技术的三大挑战：（1）如何使用现有或近期的量子计算架构实现可证明的量子加速？(2)材料和电气工程如何与量子技术相结合，为器件开发和制造提供有意义的进展？(3)量子算法的新方向如何推进和挑战量子技术可以解决传统技术无法解决的问题的概念？这些跨学科的研究挑战需要基础物理、材料科学、电气工程和算法开发之间的密切协同。在QIST的新课程和学位途径正在开发两个矿山和SJSU，其中包括专业技能发展到研究生课程的整合。通过课堂培训和研究，学员将接触到全方位的量子技术架构，为这个快速发展的领域提供全面的视角。矿业和SJSU之间的创新教育协议将为学生提供完成MS/博士学位的机会。从SJSU到Mines的桥梁程序。该计划将导致QIST研究生教育的模板，这将是从所有背景和机构类型的STEM学生访问，增加QIST的多样性。NSF研究培训（NRT）计划旨在鼓励为STEM研究生教育培训开发和实施大胆的，新的潜在变革模式。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求相一致的综合培训模式，在高优先级的跨学科或融合研究领域对STEM研究生进行有效培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Simulation of Single-shot Qubit Readout of a 2-Qubit Superconducting System with Noise Analysis

通过噪声分析模拟 2 量子位超导系统的单次量子位读出

• DOI: 10.1109/laedc54796.2022.9908196
• 发表时间: 2022
• 期刊: 2022 IEEE Latin American Electron Devices Conference (LAEDC
• 作者: Dhillon, Harsaroop;Rosen, Yaniv Jacob;Beck, Kristin;Wong, Hiu Yung
• 通讯作者: Wong, Hiu Yung

A simulation methodology for superconducting qubit readout fidelity

超导量子位读出保真度的模拟方法

• DOI: 10.1016/j.sse.2022.108582
• 发表时间: 2023
• 期刊: Solid-State Electronics
• 影响因子: 1.7
• 作者: Wong, Hiu Yung;Dhillon, Prabjot;Beck, Kristin M.;Rosen, Yaniv J.
• 通讯作者: Rosen, Yaniv J.

Building a Quantum Engineering Undergraduate Program

• DOI: 10.1109/te.2022.3144943
• 发表时间: 2021-08
• 期刊: IEEE Transactions on Education
• 影响因子: 2.6
• 作者: A. Asfaw;A. Blais;K. Brown;Jonathan Candelaria;Christoph M. Cantwell;L. Carr;J. Combes;D. Debroy;J. M. Donohue;S. Economou;Emily Edwards;M. F. Fox;S. Girvin;A. Ho;Hilary M. Hurst;Z. Jacob;Blake R. Johnson;E. Johnston-Halperin;R. Joynt;E. Kapit;J. Klein-Seetharaman;M. Laforest;H. Lewandowski;T. Lynn;C. McRae;C. Merzbacher;S. Michalakis;P. Narang;W. Oliver;J. Palsberg;D. Pappas;M. Raymer;D. Reilly;M. Saffman;T. Searles;J. Shapiro;C. Singh