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。准备充分的劳动力是实现这一承诺的关键。为了应对这一关键的劳动力需求，美国国家科学基金会授予科罗拉多矿业学院（Mines）和圣何塞州立大学（SJSU）的研究培训（NRT）奖，支持制定严格、综合、跨学科的培训计划，为硕士生和博士生在QIST的职业生涯做好准备。该计划有三个组成部分，以解决培训量子劳动力的关键挑战。首先，将开发新的培训材料，向不同学科的学生介绍QIST的基础知识。学员将来自物理学、电气工程、冶金与材料工程、应用数学与统计学、计算机科学和量子工程。第二，将与工业和国家实验室合作伙伴合作，为受训者提供尖端技术和研究经验。最后，将制定学生招聘和指导计划、无意识偏见培训、盟友研讨会和定期气候调查，以确保QIST劳动力的多样性。该项目将培养125名硕士和博士研究生，其中包括35名全额资助的实习生。这些努力将形成一个制度化的项目，为学术界、工业界、政府和国家实验室的职业生涯培养QIST的领导者。该研究计划解决了量子技术中的三大挑战：(1)如何使用现有或近期的量子计算架构实现可演示的量子加速？(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步？(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观念？这些跨学科的研究挑战需要基础物理、材料科学、电子工程和算法开发之间的紧密协同。矿业大学和上海外国语大学都在开发昆士兰科技大学的新课程和学位途径，其中包括将专业技能发展纳入研究生课程。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一个整体的观点。Mines和SJSU之间的创新教育协议将为学生提供完成硕士/博士学位的机会。从SJSU到Mines的桥梁项目。该项目将形成QIST研究生教育的模板，面向所有背景和机构类型的STEM学生，增加QIST的多样性。美国国家科学基金会研究实习生（NRT）计划旨在鼓励开发和实施大胆的、具有潜在变革性的STEM研究生教育培训新模式。该项目致力于通过创新、循证、适应不断变化的劳动力和研究需求的综合培训模式，在高优先级跨学科或融合研究领域对STEM研究生进行有效培训。该奖项反映了美国国家科学基金会的法定使命，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。

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