Welcome to the website of the Theory Division

The MPL Theory Division, led by Florian Marquardt, deals with both the quantum and classical dynamics of systems relevant for modern optics research, especially at the interface between nanophysics and quantum optics. Topics include the theory of optomechanics, neural networks and machine learning for physics, quantum optics in superconducting circuits, transport in photonic systems, fundamental questions of quantum many-body theory, nonequilibrium nonlinear dynamics, and decoherence. The group applies a variety of approaches, ranging from analytical studies to numerical simulations.


Our independent theory research group led by Mario Krenn (founded in September 2021), investigates how new artificial intelligence (AI) can make conceptual advances in physics, particularly quantum physics and quantum optics. Read more here.


Our independent theory research group dedicated to non-Hermitian topological phenomena, led by Flore Kunst, started in November 2021. Read more here.


We are part of the following research networks:

 

In our research, we apply tools from condensed matter theory and from quantum optics to a range of questions at the interface of nanophysics and quantum optics, addressing both quantum and classical dynamics. In our approach, we often try to identify the salient features of experimentally relevant situations and condense them into minimalist models which can then be attacked with all the state-of-the-art theoretical tools. At the same time, we also care about the direct contact with experiments, down to designing the classical electromagnetic and acoustic properties of specific structures.

 

Get all Research Information

Ising machines: Hardware solvers for combinatorial optimization problems

Naeimeh Mohseni, Peter McMahon, Tim Byrnes

Nature Reviews Physics (2022) | Journal | PDF


Modern applications of machine learning in quantum sciences

Anna Dawid, Julian Arnold, Borja Requena, Alexander Gresch, Marcin Płodzień, Kaelan Donatella, Kim Nicoli, Paolo Stornati, Rouven Koch, et al.

arXiv 2204.04198 Preprint | PDF


On scientific understanding with artificial intelligence

Mario Krenn, Robert Pollice, Si Yue Guo, Matteo Aldeghi, Alba Cervera-Lierta, Pascal Friederich, Gabriel dos Passos Gomes, Florian Häse, Adrian Jinich, et al.

arXiv 2204.01467 (2022) | PDF


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Current issue: Newsletter No 16 - February 2021

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