Quantum Machine Learning
量子机器学习
基本信息
- 批准号:RGPIN-2018-03969
- 负责人:
- 金额:$ 4.44万
- 依托单位:
- 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:
- 关键词:
项目摘要
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是滑铁卢大学副教授,圆周理论物理研究所副教授,加拿大计算量子多体物理研究主席。他是2016年加拿大物理学家协会“赫茨伯格”奖章的获得者,并在量子计算研究所和新成立的向量人工智能研究所任职。Melko在量子多体物理的复杂世界进行研究,强调先进的计算机模拟技术作为理论工具。他建议组建一个博士后和研究生团队,对现实世界的量子系统进行理论研究,并将其应用于材料科学和近期量子计算。这项研究将涉及开创性的发展,将Melko的团队在过去十年中开发的量子模拟的既定算法与新的机器学习技术相结合。机器学习是一种变革性的技术,正在推动当前信息技术领域的革命,从计算机视觉到自然语言理解,再到翻译等等。在这项发现资助期间,Melko计划将计算量子多体物理与先进的机器学习技术相结合,形成一个名为“量子机器学习”的新颖多学科领域。该计划将为21世纪计算凝聚态研究的进步提供新的强大技术,并培养同时擅长理论量子物理和现代机器学习理论与实践的新型人才。这项研究的一个结果是,我们的计算机模拟技术对凝聚态物质、量子材料和近期在实验室中建造的量子设备的科学破坏具有惊人的潜力。另一个是知识从物理学转移回技术领域的潜力,包括新的机器学习算法,以及将量子资源用于机器学习的可能性。由于在滑铁卢的“量子谷”和多伦多的人工智能温床都有联系,Melko的定位是为他的学生和博士后提供绝对独特的监督经验,研究课题对多伦多-滑铁卢创新走廊以及整个加拿大的量子机器学习的科学和技术发展具有广泛的潜在影响。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Melko, Roger其他文献
Quantum Boltzmann Machine
- DOI:
10.1103/physrevx.8.021050 - 发表时间:
2018-05-23 - 期刊:
- 影响因子:12.5
- 作者:
Amin, Mohammad H.;Andriyash, Evgeny;Melko, Roger - 通讯作者:
Melko, Roger
Destroying a topological quantum bit by condensing Ising vortices
- DOI:
10.1038/ncomms6781 - 发表时间:
2014-12-01 - 期刊:
- 影响因子:16.6
- 作者:
Hao, Zhihao;Inglis, Stephen;Melko, Roger - 通讯作者:
Melko, Roger
Melko, Roger的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Melko, Roger', 18)}}的其他基金
Quantum Machine Learning
量子机器学习
- 批准号:
RGPIN-2018-03969 - 财政年份:2022
- 资助金额:
$ 4.44万 - 项目类别:
Discovery Grants Program - Individual
Computational Quantum Many-Body Physics
计算量子多体物理
- 批准号:
CRC-2017-00264 - 财政年份:2022
- 资助金额:
$ 4.44万 - 项目类别:
Canada Research Chairs
Computational Quantum Many-Body Physics
计算量子多体物理
- 批准号:
CRC-2017-00264 - 财政年份:2021
- 资助金额:
$ 4.44万 - 项目类别:
Canada Research Chairs
Quantum Machine Learning
量子机器学习
- 批准号:
RGPIN-2018-03969 - 财政年份:2021
- 资助金额:
$ 4.44万 - 项目类别:
Discovery Grants Program - Individual
Computational Quantum Many-Body Physics
计算量子多体物理
- 批准号:
CRC-2017-00264 - 财政年份:2020
- 资助金额:
$ 4.44万 - 项目类别:
Canada Research Chairs
Quantum Machine Learning
量子机器学习
- 批准号:
RGPIN-2018-03969 - 财政年份:2019
- 资助金额:
$ 4.44万 - 项目类别:
Discovery Grants Program - Individual
Computational Quantum Many-Body Physics
计算量子多体物理
- 批准号:
CRC-2017-00264 - 财政年份:2019
- 资助金额:
$ 4.44万 - 项目类别:
Canada Research Chairs
Quantum Machine Learning
量子机器学习
- 批准号:
RGPIN-2018-03969 - 财政年份:2018
- 资助金额:
$ 4.44万 - 项目类别:
Discovery Grants Program - Individual
Computational Quantum Many-Body Physics
计算量子多体物理
- 批准号:
CRC-2017-00264 - 财政年份:2018
- 资助金额:
$ 4.44万 - 项目类别:
Canada Research Chairs
Entanglement and Emergence in Simulations of Quantum Matter
量子物质模拟中的纠缠和出现
- 批准号:
355283-2013 - 财政年份:2017
- 资助金额:
$ 4.44万 - 项目类别:
Discovery Grants Program - Individual
相似国自然基金
Understanding structural evolution of galaxies with machine learning
- 批准号:n/a
- 批准年份:2022
- 资助金额:10.0 万元
- 项目类别:省市级项目
相似海外基金
Quantum Machine Learning for Financial Data Streams
金融数据流的量子机器学习
- 批准号:
10073285 - 财政年份:2024
- 资助金额:
$ 4.44万 - 项目类别:
Feasibility Studies
Screening of environmentally friendly quantum-nanocrystals for energy and bioimaging applications by combining experiment and theory with machine learning
通过将实验和理论与机器学习相结合,筛选用于能源和生物成像应用的环保量子纳米晶体
- 批准号:
23K20272 - 财政年份:2024
- 资助金额:
$ 4.44万 - 项目类别:
Grant-in-Aid for Scientific Research (B)
REU Site: Quantum Machine Learning Algorithm Design and Implementation
REU 站点:量子机器学习算法设计与实现
- 批准号:
2349567 - 财政年份:2024
- 资助金额:
$ 4.44万 - 项目类别:
Standard Grant
Utilising Quantum Machine Learning and quantum computing for genomic research and development
利用量子机器学习和量子计算进行基因组研究和开发
- 批准号:
10083188 - 财政年份:2023
- 资助金额:
$ 4.44万 - 项目类别:
Small Business Research Initiative
Machine-learning quantum surrogate models to simulate energy transport across interfaces
机器学习量子替代模型来模拟跨界面的能量传输
- 批准号:
2886134 - 财政年份:2023
- 资助金额:
$ 4.44万 - 项目类别:
Studentship
Scalable Quantum Machine Learning with NISQ Devices: Theoretic and Algorithmic Foundations
使用 NISQ 设备的可扩展量子机器学习:理论和算法基础
- 批准号:
2882984 - 财政年份:2023
- 资助金额:
$ 4.44万 - 项目类别:
Studentship
Categorical Duality and Semantics Across Mathematics, Informatics and Physics and their Applications to Categorical Machine Learning and Quantum Computing
数学、信息学和物理领域的分类对偶性和语义及其在分类机器学习和量子计算中的应用
- 批准号:
23K13008 - 财政年份:2023
- 资助金额:
$ 4.44万 - 项目类别:
Grant-in-Aid for Early-Career Scientists
Development of an efficient method combining quantum chemistry and machine learning to evolve PCR technology and gene mutation analysis
开发一种结合量子化学和机器学习的有效方法来发展 PCR 技术和基因突变分析
- 批准号:
22KJ2450 - 财政年份:2023
- 资助金额:
$ 4.44万 - 项目类别:
Grant-in-Aid for JSPS Fellows
Efficient tuning of quantum devices using machine learning
使用机器学习有效调整量子器件
- 批准号:
2886876 - 财政年份:2023
- 资助金额:
$ 4.44万 - 项目类别:
Studentship
Security-first Federated Quantum Machine Learning for Genomics
安全第一的基因组学联合量子机器学习
- 批准号:
10072286 - 财政年份:2023
- 资助金额:
$ 4.44万 - 项目类别:
Feasibility Studies