Quantum generalisations and implementations of Hopfield and feed-forward neural networks

Hopfield 和前馈神经网络的量子推广和实现

• 批准号: 449905436
• 负责人: Professor Igor Lesanovsky
• 金额: --
• 依托单位: Institut für Theoretische Physik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/449905436?language=en
• 关键词: Quantum; generalisations; implementations; Hopfield; feed

The field of artificial intelligence and machine learning is currently witnessing a revolution. Recent breath-taking developments in image and speech recognition as well as in analysing and categorising large amounts of data, have a tremendous impact on policy making, economics and society. At the same time there is an ongoing revolution at the technological level that concerns our ability to control and harness the exotic properties of quantum matter. Experimental progress and an increased theoretical understanding of how to exploit quantum physics in applications have led to the emergence of the field of quantum technologies, which bears a promise to revolutionise time keeping, sensing, communication as well as data storage and processing.The goal of this proposal is to build a bridge between machine learning concepts and quantum technologies by developing a framework of quantum generalised neural networks. The research focusses on two architectures. The first is a so-called rotor Hopfield neural network which represents a model of an associative memory. It is based on spin degrees of freedom and information being stored in the physical interaction between the spins. The second architecture is given by layered networks assembled by perceptrons. In these feed-forward neural networks information is propagated between adjacent layers and learned behaviour is encoded in interlayer couplings which are suitably adjusted via learning strategies. The advantage of both architectures is that they allow a systematic generalisation into the quantum domain from a well-defined classical limit. Moreover, they offer a direct connection to the physics of many-particle systems: quantum rotor Hopfield neural networks are strongly interacting non-equilibrium spin systems, and feed-forward neural networks are closely related to open cellular automata and driven-dissipative quantum dynamics. Both their dynamical and steady-state behaviour, e.g. the retrieval of stored information or the implementation of learning strategies, can be understood and classified from the perspective of phases and phase transitions. Both physical architectures of dynamically coupled quantum neurons, which we envision are complementary to approaches that realise quantum neural networks as quantum algorithms in the form of variational quantum circuits, or as wave function ansatzes. The proposed research will not only deliver insights in how to exploit quantum effects in neural networks to enhance machine learning. It will also yield proposals for implementing the necessary strategies on physical platforms, such as cold atomic gases and trapped ions. All this is achieved through a new collaboration between the Eberhard Karls University of Tübingen and the Forschungszentrum Jülich and the merging of the theoretical expertise on machine learning, quantum information, quantum many-body physics and atomic physics, present at those two institutions.

人工智能和机器学习领域目前正在经历一场革命。最近，图像和语音识别以及大量数据分析和分类方面取得了令人惊叹的发展，对政策制定、经济和社会产生了巨大影响。与此同时，技术层面正在发生一场革命，涉及我们控制和利用量子物质奇异特性的能力。实验进展和对如何在应用中利用量子物理的理论理解的加深导致了量子技术领域的出现，它有望彻底改变计时、传感、通信以及数据存储和处理。该提案的目标是通过开发量子广义神经网络框架，在机器学习概念和量子技术之间架起一座桥梁。该研究重点关注两种架构。第一个是所谓的转子霍普菲尔德神经网络，它代表联想记忆的模型。它基于自旋自由度和存储在自旋之间的物理相互作用中的信息。第二种架构是由感知器组装的分层网络给出的。在这些前馈神经网络中，信息在相邻层之间传播，学习的行为被编码在层间耦合中，层间耦合可通过学习策略进行适当调整。两种架构的优点在于它们允许从明确定义的经典极限系统地推广到量子域。此外，它们提供了与多粒子系统物理学的直接联系：量子转子霍普菲尔德神经网络是强相互作用的非平衡自旋系统，前馈神经网络与开放元胞自动机和驱动耗散量子动力学密切相关。它们的动态和稳态行为，例如存储信息的检索或学习策略的实施，可以从阶段和阶段转变的角度来理解和分类。我们设想，这两种动态耦合量子神经元的物理架构与将量子神经网络实现为变分量子电路形式的量子算法或波函数模拟的方法是互补的。拟议的研究不仅将提供有关如何利用神经网络中的量子效应来增强机器学习的见解。它还将提出在物理平台上实施必要策略的建议，例如冷原子气体和捕获离子。所有这一切都是通过图宾根埃伯哈德·卡尔斯大学和于利希研究中心之间的新合作以及融合这两个机构在机器学习、量子信息、量子多体物理和原子物理方面的理论专业知识而实现的。