德国联邦物理技术研究院

Developments in ultrastable lasers have fueled remarkable advances in optical frequency metrology and quantum science. A key ingredient in further improving laser frequency stability is the use of low-noise mirror materials such as AlGaAs crystalline coatings. However, excess noise observed with these coatings limits the performance of cryogenic silicon cavities with AlGaAs mirrors to similar levels achieved with conventional dielectric coatings. With a new pair of crystalline coated mirrors in a 6-cm-long cryogenic silicon cavity operated at 17 K, we demonstrate a clear advantage of crystalline coatings over dielectric coatings. The achieved fractional frequency stability of 2.5×10^{-17} at 10 s is four times better than expected for dielectric mirrors and corresponds to more than a tenfold reduction in the coating mechanical loss factor. We also combine two silicon cavities to demonstrate optical frequency averaging for enhanced stability. In addition, we present a long-term frequency drift record of four cryogenic silicon cavities measured over several years. These results open up realistic prospects for cavity-stabilized lasers with 10^{-18} fractional stability, as well as an all-optical timescale with continuously operating optical local oscillators.

Photoplethysmography (PPG) is a widely used non-invasive physiological sensing technique, suitable for various clinical applications. Such clinical applications are increasingly supported by machine learning methods, raising the question of the most appropriate input representation and model choice. Comprehensive comparisons, in particular across different input representations, are scarce. We address this gap in the research landscape by a comprehensive benchmarking study covering three kinds of input representations, interpretable features, image representations and raw waveforms, across prototypical regression and classification use cases: blood pressure and atrial fibrillation prediction. In both cases, the best results are achieved by deep neural networks operating on raw time series as input representations. Within this model class, best results are achieved by modern convolutional neural networks (CNNs). but depending on the task setup, shallow CNNs are often also very competitive. We envision that these results will be insightful for researchers to guide their choice on machine learning tasks for PPG data, even beyond the use cases presented in this work.

Large pre-trained language models have become a crucial backbone for many downstream tasks in natural language processing (NLP), and while they are trained on a plethora of data containing a variety of biases, such as gender biases, it has been shown that they can also inherit such biases in their weights, potentially affecting their prediction behavior. However, it is unclear to what extent these biases also affect feature attributions generated by applying "explainable artificial intelligence" (XAI) techniques, possibly in unfavorable ways. To systematically study this question, we create a gender-controlled text dataset, GECO, in which the alteration of grammatical gender forms induces class-specific words and provides ground truth feature attributions for gender classification tasks. This enables an objective evaluation of the correctness of XAI methods. We apply this dataset to the pre-trained BERT model, which we fine-tune to different degrees, to quantitatively measure how pre-training induces undesirable bias in feature attributions and to what extent fine-tuning can mitigate such explanation bias. To this extent, we provide GECOBench, a rigorous quantitative evaluation framework for benchmarking popular XAI methods. We show a clear dependency between explanation performance and the number of fine-tuned layers, where XAI methods are observed to benefit particularly from fine-tuning or complete retraining of embedding layers.

We report the sympathetic cooling and Coulomb crystallization of xenon highly charged ions (HCIs) with laser-cooled Ca+ ions. The HCIs are produced in a compact electron beam ion trap, then charge selected, decelerated, and finally injected into a cryogenic linear Paul trap. There, they are captured into 40Ca+ Coulomb crystals, and cocrystallized within them, causing dark voids in their fluorescence images. Fine control over the number of trapped ions and HCIs allows us to realize mixed-species crystals with arbitrary ordering patterns. By investigating Xeq+ - Ca+ strings, we confirm the HCI charge states, measure their lifetime, and characterize the mixed-species motional modes. Our system effectively combines the established quantum control toolbox for Ca+ with the rich set of atomic properties of Xe highly charged ions, providing a resourceful platform for optical frequency metrology, searches for signatures of new physics, and quantum information science.

Single-ion optical clocks have shown systematic frequency uncertainties below 10^{−18}, but typically require more than one week of averaging to achieve a corresponding statistical uncertainty. This time can be reduced with longer probe times, but comes at the cost of a higher time-dilation shift due to motional heating of the ions in the trap. We show that sympathetic ground-state cooling using electromagnetically induced transparency of an Al^{+} clock ion via a cotrapped Ca^{+} ion during clock interrogation suppresses the heating of the ions. Al^{+} can be kept close to the motional ground state, independent from the chosen interrogation time, at a relative time-dilation shift of (−1.69±0.20)×10^{−18}. The Ca^{+} cooling light introduces an additional light shift on the Al^{+} clock transition of (−9.3±1.1)×10^{−18}. We project that the uncertainty of this light shift can be further reduced by nearly an order of magnitude. This sympathetic cooling enables seconds of interrogation time with 10^{−19} motional and cooling laser-induced uncertainties for Al^{+} and can be employed in other ion clocks as well.