Quantum Materials and Engineering

Contact persons: Marianne E. Bathen, Morten Hjorth-Jensen
Keywords: Materials science, quantum technologies, quantum mechanical many-body theories, deep learning, quantum machine learning    
Research groups: Semiconductor physics, CCSE
Department of Physics

In quantum materials (https://www.nature.com/articles/nphys4302), the effects of quantum mechanics are manifest over a broad range of energy and length scales. Examples of quantum materials include superconductors, graphene and topological insulators, in addition to effects of reduced dimensionality such as quantum dots and point defects in semiconductors. Quantum materials are currently receiving broad attention because of their importance for understanding the properties of matter, and because they form the foundation for the emerging quantum technologies: sensing, communication, and computing. We are interested in research on quantum materials from both a theoretical and methodological perspective, ideally with the potential of comparing to and guiding experimental work. To facilitate the development of quantum materials for a broad range of application domains, we believe that theoretical and numerical methods with strong ties to experimental work are essential. Research proposals may span various methodological approaches and applications within this scope.

Topics from methodological research:

  • Studies and development of quantum mechanical many-body methods for materials science, from first principles methods to mean-field approaches and molecular dynamics simulations.
  • Use and benchmarking of quantum mechanical many-body methods to simulate and model quantum systems and quantum materials. 
  • Machine learning and artificial intelligence for quantum technologies, from deep learning solutions of quantum mechanical problems to studies of candidate systems relevant for quantum technologies.
  • Development and studies of generative deep learning methods relevant for quantum technologies and quantum materials. 
  • Development of time-dependent many-body theories for studies of the dynamics and time evolution of quantum systems.  
  • Development of quantum mechanical many-body methods to study entanglement between quantum systems.  
  • Quantum engineering and quantum machine learning for designing optimal quantum circuits. This includes for example the realization of so-called quantum twins: developing theory and experiment tailored to specific experimental realizations. 

Topics from natural sciences or technology:  

  • Theoretical studies of decoherence for experimental realizations of quantum circuits and gates. 
  • Use of machine learni