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Controlling ultracold atoms with laser light has greatly advanced quantum science. The wavelength of light sets a typical length scale for most experiments to the order of 500 nanometers (nm) or greater. In this work, we implemented a super-resolution technique that localizes and arranges atoms on a sub–50-nm scale, without any fundamental limit in resolution. We demonstrate this technique by creating a bilayer of dysprosium atoms and observing dipolar interactions between two physically separated layers through interlayer sympathetic cooling and coupled collective excitations. At 50-nm distance, dipolar interactions are 1000 times stronger than at 500 nm. For two atoms in optical tweezers, this should enable purely magnetic dipolar gates with kilohertz speed. 

This paper investigates the application of the ℓp quasinorm, where 0 < p < 1, in contexts characterized by photon-limited signals such as medical imaging and night vision. In these environments, low-photon count images have typically been modeled using Poisson statistics. In related algorithms, the ℓ1 norm is commonly employed as a regularization method to promotes sparsity in the reconstruction. However, recent research suggests that using the ℓp quasi-norm may yield lower error results. In this paper, we investigate the use of negative binomial statistics, which are more general models than Poisson models, in conjunction with the ℓp quasi-norm for recovering sparse signals in low-photon count imaging settings. 

This study addresses the challenge of reconstructing sparse signals, a frequent occurrence in the context of overdispersed photon-limited imaging. While the noise behavior in such imaging settings is typically modeled using a Poisson distribution, the negative binomial distribution is more suitable in overdispersed scenarios where the noise variance exceeds the signal mean. Knowledge of the maximum and minimum signal intensity can be effectively utilized within the computational framework to enhance the accuracy of signal reconstruction. In this paper, we use a gradient-based method for sparse signal recovery that leverages a negative binomial distribution for noise modeling, enforces bound constraints to adhere to upper and lower signal intensity thresholds, and employs a sparsity-promoting regularization term. The numerical experiments we present demonstrate that the incorporation of these features significantly improves the reconstruction of sparse signals from overdispersed measurements.