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Register a new userApplying machine learning optimization methods to the production of a quantum gas
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We apply three machine learning strategies to optimize the atomic cooling processes utilized in the production of a Bose-Einstein condensate (BEC). For the first time, we optimize both laser cooling and evaporative cooling mechanisms simultaneously. We present the results of an evolutionary optimization method (Differential Evolution), a method based on non-parametric inference (Gaussian Process regression) and a gradient-based function approximator (Artificial Neural Network). Online optimization is performed using no prior knowledge of the apparatus, and the learner succeeds in creating a BEC from completely randomized initial parameters. Optimizing these cooling processes results in a factor of four increase in BEC atom number compared to our manually-optimized parameters. This automated approach can maintain close-to-optimal performance in long-term operation. Furthermore, we show that machine learning techniques can be used to identify the main sources of instability within the apparatus.
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Phasonic degrees of freedom are unique to quasiperiodic structures, and play a central role in poorly-understood properties of quasicrystals from excitation spectra to wavefunction statistics to electronic transport. However, phasons are challenging to access dynamically in the solid state due to their complex long-range character and the effects of disorder and strain. We report phasonic spectroscopy of a quantum gas in a one-dimensional quasicrystalline optical lattice. We observe that strong phasonic driving produces a nonperturbative high-harmonic plateau strikingly different from the effects of standard dipolar driving. Tuning the potential from crystalline to quasicrystalline, we identify spectroscopic signatures of quasiperiodicity and interactions and map the emergence of a multifractal energy spectrum, opening a path to direct imaging of the Hofstadter butterfly.
We report on an efficient production scheme for a large quantum degenerate sample of fermionic lithium. The approach is based on our previous work on narrow-line $ 2S_{1/2}rightarrow 3P_{3/2} $ laser cooling resulting in a high phase-space density of up to $3times10^{-4}$. This allows utilizing a large volume crossed optical dipole trap with a total power of $45,textrm{W}$, leading to high loading efficiency and $8times10^6$ trapped atoms. The same optical trapping configuration is used for rapid adiabatic transport over a distance of $25,textrm{cm}$ in $0.9,textrm{s}$, and subsequent evaporative cooling. With optimized evaporation we achieve a degenerate Fermi gas with $1.7times 10^{6}$ atoms at a temperature of $60 , textrm{nK}$, corresponding to $T/T_{text{F}}=0.16left(2 right)$. Furthermore, the performance is demonstrated by evaporation near a broad Feshbach resonance creating a molecular Bose-Einstein condensate of $3times10^5$ lithium dimers.
Quantum fluctuations are the origin of genuine quantum many-body effects, and can be neglected in classical mean-field phenomena. Here we report on the observation of stable quantum droplets containing $sim$ 800 atoms which are expected to collapse at the mean-field level due to the essentially attractive interaction. By systematic measurements on individual droplets we demonstrate quantitatively that quantum fluctuations stabilize them against the mean-field collapse. We observe in addition interference of several droplets indicating that this stable many-body state is phase coherent.
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