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arXiv · 2310.14499

Highly Efficient Creation and Detection of Deeply-bound Molecules via Invariant-based Inverse Engineering with Feasible Modified Drivings

Jiahui Zhang
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Abstract

Stimulated Raman Adiabatic Passage (STIRAP) and its variants, such as multi-state chainwise-STIRAP allow efficiently transferring the populations in multi-state system and have been widely used to prepare ultracold deeply-bound molecules. However, their transfer efficiencies are generally imperfect. The main obstacle is the presence of losses and the requirement to make the dynamics adiabatic. To this end, in the present paper a theoretical method for the efficient and robust creation and detection of deeply-bound molecules is proposed. The simple three- and five-level systems with states chainwise coupled by optical fields are considered. In the regime of large detuning, the major molecular losses are pre-suppressed by reducing the dynamics of the three- and five-level molecular systems to those of effective two- and three-level counterparts, respectively. Consequently, two-level counterpart can be directly compatible with two kinds of "Invariant-based Inverse Engineering" (IIE) recipes, the results show that both protocols give comparable performance and have good experimental feasibility. For the five-level case, by considering a relation among the four incident pulses, we show that the M-type structure can be generalized into an effective $Lambda$-type one with the simplest resonant coupling. Therefore, this generalized model can also be directly compatible with "IIE" recipe. Numerical calculations show that the weakly-bound molecules can be efficiently transferred to their deeply-bound states without strong laser intensity, and the stability against parameter variations is well preserved. Finally, the detection of ultracold deeply-bound molecules is discussed, the results show that all the protocols allow efficient detection of molecules.

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Jiahui Zhang. 2023-10-23. Highly Efficient Creation and Detection of Deeply-bound Molecules via Invariant-based Inverse Engineering with Feasible Modified Drivings. https://doi.org/10.1063/5.0183063

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