Archive 13 March 2023

Rydberg atoms: a versatile tool for quantum technologies

Sylvain Schwartz

Laboratoire QTECH (ONERA)

Rydberg atoms are by definition atoms which have been excited to a state with a large principal quantum number, resulting in exaggerated properties such as a large atomic size, a long lifetime compared to other excited states and large matrix elements for the dipole operator. In practice, dipole-dipole interactions between Rydberg atoms are at the heart of quantum simulations, where they are used to create entangled atomic states. But the large dipole of Rydberg atoms can also result in a strong coupling with external electromagnetic fields, making these atoms good candidates to be used as very sensitive probes of electromagnetic environment in the GHz to THz range. I this talk, I will give a brief overview of the state of the art of quantum simulation and quantum metrology with Rydberg atoms, and present the ongoing project that we have in the QTech lab at ONERA about quantum metrology with cold Rydberg atoms trapped in optical potentials. Possible applications include electromagnetic intelligence, THz imaging and scientific applications such as the calibration of black-body shifts in state-of-the-art optical clocks (in collaboration with SYRTE and laboratoire Aimé Cotton).

Virtual material design

Maxime Moreaud

IFPEN, Solaize

Since 2017, IFPEN has fully entered the race for accelerated design of new materials with models creating links between synthesis and effective properties. Its AI and materials teams propose new tools for the numerical generation and characterization of materials microstructures.  
This approach realistically considers the microstructure to capture morphological and topological details at scales of interest. Numerical models link to synthesis or processing parameters, and estimate textural and usage properties. In this talk, we will discuss the general ideas of this approach, examples of multi-scale microstructures, and some recent work on numerical textural characterizations such as tortuosity and deep learning accelerated physisorption simulation.

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