Project Verallgemeinerung von Hopfield und feed-forward neuronalen Netzwerken in den Quantenbereich und deren…

Basic data

Verallgemeinerung von Hopfield und feed-forward neuronalen Netzwerken in den Quantenbereich und deren Implementation
01/04/2021 to 31/03/2024
Abstract / short description:
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 feedforward
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 nonequilibrium 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.

Involved staff


Institute for Theoretical Physics (ITP)
Department of Physics, Faculty of Science

Other staff

Institute for Theoretical Physics (ITP)
Department of Physics, Faculty of Science

Local organizational units

Institute for Theoretical Physics (ITP)
Department of Physics
Faculty of Science


Bonn, Nordrhein-Westfalen, Germany

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