Quantum Machine Learning

Melko是滑铁卢大学的副教授，Perimeter理论物理研究所的副教授，以及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的矢量人工智能研究所拥有联系。 Melko从事量子多体物理学复杂世界的研究，强调先进的计算机模拟技术作为理论工具。他建议让一个博士后研究员和研究生团队参与现实世界量子系统的理论研究，并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，结合Melko团队在过去十年中开发的量子模拟算法和新颖的机器学习技术。 机器学习是一种变革性技术，推动着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学与先进的机器学习技术相结合，形成一个新的多学科领域，称为“量子机器学习”。 该计划将产生新的，强大的技术，为计算凝聚态研究到21世纪世纪的进步，并培养一个新的人才，同时精通理论量子物理，现代机器学习的理论和实践。这项研究的一个结果是，在我们的计算机模拟技术中，凝聚态物质、量子材料和目前在实验室中建造的近期量子设备具有惊人的科学颠覆潜力。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，以及可能使用量子资源进行机器学习。Melko在滑铁卢的“量子谷”和多伦多的人工智能温床都有联系，他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。

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.