Structural and electrical properties of ultrathin niobium nitride films grown by atomic layer deposition

We studied and optimized the properties of ultrathin superconducting niobium nitride films fabricated with a plasma-enhanced atomic layer deposition (PEALD) process. By adjusting process parameters, the chemical embedding of undesired oxygen into the films was minimized and a film structure consisting of mainly polycrystalline niobium nitride with a small fraction of amorphous niobium oxide and niobium oxo-nitrides were formed. For this composition a critical temperature of 13.8 K and critical current densities of 7×106 A cm–2 at 4.2 K were measured on 40nm thick films. A fundamental correlation between these superconducting properties and the crystal lattice size of the cubic δ-niobium-nitride grains were found. Moreover, the film thickness variation between 40 and 2 nm exhibits a pronounced change of the electrical conductivity at room temperature and reveals a superconductor–insulator-transition in thevicinity of 3 nm film thickness at low temperatures. The thicker films with resistances up to 5kΩ per square in the normal state turn to the superconducting one at low temperatures. The perfect thickness control and film homogeneity of the PEALD growth make such films extremely promising candidates for developing novel devices on the coherent quantum phase slip effect

Direct Josephson coupling between superconducting flux qubits

We have demonstrated strong antiferromagnetic coupling between two three-junction flux qubits based on a shared Josephson junction, and therefore not limited by the small inductances of the qubit loops. The coupling sign and magnitude were measured by coupling the system to a high-quality superconducting tank circuit. Design modifications allowing to continuously tune the coupling strength and/or make the coupling ferromagnetic are discussed.Comment: REVTeX 4, 4 pages, 5 figures; v2: completely rewritten, added finite-temperature results and proposals for ferromagnetic galvanic couplin

Four-qubit device with mixed couplings

We present the first experimental results on a device with more than two superconducting qubits. The circuit consists of four three-junction flux qubits, with simultaneous ferro- and antiferromagnetic coupling implemented using shared Josephson junctions. Its response, which is dominated by the ground state, is characterized using low-frequency impedance measurement with a superconducting tank circuit coupled to the qubits. The results are found to be in excellent agreement with the quantum-mechanical predictions.Comment: REVTeX 4, 5pp., 7 EPS figure files. N.B.: "Alec" is my first, and "Maassen van den Brink" my family name. v2: final published version, with changed title, different sample micrograph, and several clarification

The COSINUS Underground Cryogenic Facility

Cryogenic Observatory for SIgnatures seen in Next-generation Underground Searches (COSINUS) will use cryogenic sodium iodide (NaI) calorimeters to search for dark matter. Recently, the construction of an underground facility at Laboratori Nazionali del Gran Sasso (LNGS) for COSINUS has been completed. The features of the COSINUS facility allow for a low background environment for rare event searches. This facility will house a dry dilution refrigerator, which will sit in a drywell inside a water tank. The water tank will be instrumented with photomultiplier tubes and serve as an active muon veto as well as a passive shield

Feasibility studies for the measurement of time-like proton electromagnetic form factors from p¯ p→ μ+μ- at P ¯ ANDA at FAIR

This paper reports on Monte Carlo simulation results for future measurements of the moduli of time-like proton electromagnetic form factors, | GE| and | GM| , using the p¯ p→ μ+μ- reaction at P ¯ ANDA (FAIR). The electromagnetic form factors are fundamental quantities parameterizing the electric and magnetic structure of hadrons. This work estimates the statistical and total accuracy with which the form factors can be measured at P ¯ ANDA , using an analysis of simulated data within the PandaRoot software framework. The most crucial background channel is p¯ p→ π+π-, due to the very similar behavior of muons and pions in the detector. The suppression factors are evaluated for this and all other relevant background channels at different values of antiproton beam momentum. The signal/background separation is based on a multivariate analysis, using the Boosted Decision Trees method. An expected background subtraction is included in this study, based on realistic angular distributions of the background contribution. Systematic uncertainties are considered and the relative total uncertainties of the form factor measurements are presented

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG- bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood develop- ment, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google- internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting