Relativistic Quantum Information: Applications and foundations in Quantum Information, Relativity and Machine Learning

相对论量子信息：量子信息、相对论和机器学习的应用和基础

• 批准号: RGPIN-2020-04081
• 负责人: MartinMartinez, Eduardo
• 金额: $ 2.04万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=743687
• 关键词: Relativistic; Quantum; Information; Applications; foundations

Over the past decade, a new field of high research intensity has emerged: Relativistic Quantum Information (RQI). RQI brings together the two pillars of modern physics, general relativity and quantum theory, with information theory. The results in the field of RQI range from new insights into the laws of Nature (e.g. black hole physics and cosmology) all the way to concrete applications in quantum computing and quantum-secured communication. In particular, the study of relativistic effects in quantum information processing has recently become more important due to improvements both in theory and in technology. Experiments in quantum optics, superconducting circuits and Bose-Einstein condensates can now probe relativistic influences on quantum information. Protocols that distribute entanglement over hundreds of kilometers are now reaching regimes where relativistic effects are becoming important, and satellite experiments are being developed that will measure gravitational effects on quantum entanglement. This proposal seeks to investigate some of the key theoretical and applied questions in the field of RQI: Which relativistic settings have an advantage over non-relativistic ones in quantum information technologies such as quantum cryptography or quantum computing? Can the fluctuations of a quantum field provide a practical and renewable source of quantum entanglement?  Concretely, the proposed research investigates the processing of quantum information in still unexplored regimes in quantum field theory, ultra-fast quantum optics and superconducting circuits where both Einstein's relativity and quantum theory are necessary to understand the processing and flow of information. Furthermore, the proposed research explores the use of deep learning and other machine learning techniques to study measurements and correlations in quantum field theory. In particular this proposal will investigate how neural networks can be applied to the problem of classifying non-local features of QFTs using probe data from measurements obtained coupling particle detectors locally (in space and time) to the field. The long term objective is to develop a new way of exploring measurement theory in relativistic quantum mechanics and field theory connecting deep notions in theoretical physic such as holography in quantum gravity and the entanglement in quantum field theories with the latest techniques in data processing. These studies will advance our fundamental knowledge of quantum theory and relativity. At the same time, will potentially have a significant long-term impact on Canada's economy and industry developing the Canadian expertise in machine learning and in quantum technologies. Concretely, applying knowledge acquired through the theoretical studies to the development of applications in quantum computing, quantum-secured communication and the development of measurement devices with sensitivities much beyond the limits of current technologies.

在过去的十年中，出现了一个新的高研究强度领域：相对论量子信息（RQI）。 RQI汇集了现代物理学的两个支柱，一般相对论和量子理论，以及信息理论。 RQI领域的结果范围从对自然定律（例如黑洞物理学和宇宙学）的新见解一直到量子计算和量子固定通信中的具体应用。特别是，由于理论和技术方面的改进，对量子信息处理中相对论效应的研究最近变得越来越重要。量子光学，超导电路和玻色的凝结物进行的实验现在可以探测相对论的影响量子信息。分布数百公里的纠缠的协议现在已达到相对论效应变得重要的政权，并且正在开发卫星实验，以衡量对量子纠缠的重力影响。该提案旨在研究RQI领域中的一些关键理论和应用问题：哪些重新主义设置比量子信息技术（例如量子加密或量子计算）中的非相关性具有优势？量子场的波动能否提供量子纠缠的实用且可再生的来源？具体而言，拟议的研究调查了量子场理论中仍然意外的方案，超快速量子光学器件和超导电路中的量子信息的处理，在这种情况下，爱因斯坦的相对性和量子理论都是必要的，以了解信息的处理和信息流。此外，拟议的研究探讨了深度学习和其他机器学习技术的使用来研究量子场理论中的测量和相关性。特别地，该建议将研究如何使用从当地（时空和时间和时间）中耦合粒子探测器获得的耦合粒子检测器中获得的探针数据将神经网络应用于QFTS的非本地特征的问题。长期目标是在相对论量子力学和现场理论中开发一种新的方法，将理论物理中的深度注释（例如量子重力中的全息图和量子场理论中的纠缠与数据处理中的最新技术）连接起来。这些研究将提高我们对量子理论和相对论的基本知识。同时，可能会对加拿大的经济和行业产生重大的长期影响，发展加拿大机器学习和量子技术方面的专业知识。具体而言，将通过理论研究获得的知识应用于量子计算，量子保存通信的应用以及具有敏感性的测量设备的开发超出了当前技术的限制。