Quantum Machine Learning

量子机器学习

• 批准号: RGPIN-2018-03969
• 负责人: Melko, Roger
• 金额: $ 4.44万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712441
• 关键词: Quantum; Machine; Learning

Melko is an Associate Professor at the University of Waterloo, Associate Faculty at the Perimeter Institute for Theoretical Physics, and the Canada Research Chair in Computational Quantum Many Body Physics. He is the recipient of the Canadian Association of Physicists "Herzberg" medal in 2016, and holds affiliations at the Institute for Quantum Computing, and the new Vector Institute for Artificial Intelligence. Melko undertakes research in the complex world of quantum many-body physics, emphasizing advanced computer simulation techniques as a theoretical tool. He proposes to engage a team of postdoctoral fellows and graduate students in the theoretical study of real-world quantum systems, with applications in materials science and near-term quantum computing. The research will involve pioneering developments combining established algorithms for quantum simulation, developed by Melko's group over the last decade, and novel machine learning techniques. Machine learning is a transformative technology driving the current revolution in the information technology sector, from computer vision, to natural language comprehension, to translation, and more. Over the duration of this Discovery Grant, Melko plans to integrate computational quantum many-body physics with advanced machine learning techniques to form a novel, multi-disciplinary field called "quantum machine learning". This program will produce new, powerful techniques for the advancement of computational condensed matter research into the 21st century, and train a new breed of personnel simultaneously adept in theoretical quantum physics, and the theory and practice of modern machine learning. One consequence of this research is the staggering potential for scientific disruption in our computer simulation technology for condensed matter, quantum materials, and near-term quantum devices being built in laboratories today. Another is the potential for knowledge transfer from physics back into the technology sector, including new machine learning algorithms, and the possible use of quantum resources for machine learning. With affiliations in both Waterloo's "quantum valley" as well as Toronto's artificial intelligence hotbed, Melko is positioned to give his students and postdocs an absolutely unique supervisory experience, on research topics with broad potential impact for the scientific and technological development of quantum machine learning in the Toronto-Waterloo Innovation Corridor, as well as Canada as a whole.

Melko是滑铁卢大学的副教授，理论物理学外围研究所的副教授，以及加拿大计算量子量子研究主席许多身体物理学。 他是2016年加拿大物理学家“赫兹伯格”奖章的获得者，并拥有量子计算研究所和新的人工智能研究所的隶属关系。 Melko在量子多体物理学的复杂世界中进行了研究，强调了先进的计算机仿真技术作为理论工具。他建议让一组博士后研究员和研究生团队参与现实世界中量子系统的理论研究，并在材料科学和近期量子计算中应用。 这项研究将涉及开创性的发展，结合了Melko小组在过去十年中开发的已建立算法以及新颖的机器学习技术。 机器学习是一种推动信息技术领域当前革命的变革技术，从计算机视觉到自然语言理解，再到翻译等等。 在这项发现赠款的持续时间内，Melko计划将计算量子多体物理学与先进的机器学习技术整合在一起，以形成一个新颖的多学科领域，称为“量子机器学习”。 该计划将为21世纪的计算凝结物研究发展提供新的，有力的技术，并同时培训一种新的人员，同时熟悉理论量子物理学以及现代机器学习的理论和实践。这项研究的结果之一是，我们的计算机模拟技术中的科学破坏潜力惊人的潜力，如今为实验室建造的凝结物质，量子材料和近期量子设备。另一个是知识转移到技术领域的潜力，包括新的机器学习算法，以及可能将量子资源用于机器学习。梅尔科（Melko）都有在滑铁卢的“量子谷”和多伦多人工智能温床的隶属关系以及多伦多人工智能温床的隶属关系的位置，可以使他的学生和博士后成为绝对独特的监督体验，这是对对量子机器在科学和技术开发中的广泛影响的研究主题，这些主题对量子机器学习的科学和技术发展具有广泛的影响。多伦多 - 沃特卢创新走廊以及整个加拿大。