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Superconducting qubits are considered as a promising platform for implementing a fault tolerant quantum computing. However, surface defects of superconductors and the substrate leading to qubit state decoherence and fluctuations in qubit parameters constitute a significant problem. The amount and type of defects depend both on the chip materials and fabrication procedure. In this work, transmons produced by two different methods of aluminum etching: wet etching in a solution of weak acids and dry etching using a chlorine-based plasma are experimentally studied. The relaxation and coherence times for dry-etched qubits are more than twice as long as those for wet-etched ones. Additionally, the analysis of time fluctuations of qubit frequencies and relaxation times, which is an effective method to identify the dominant dielectric loss mechanisms, indicates a significantly lower impact of two-level systems in the dry-etched qubits compared to the wet-etched ones., Comment: 7 pages, 6 figures

Dynamical decoupling multipulse sequences can be applied to solid state spins for sensing weak oscillating fields from nearby single nuclear spins. By periodically reversing the probing system's evolution, other noises are counteracted and filtered out over the total evolution. However, the technique is subject to intricate interactions resulting in additional resonant responses, which can be misinterpreted with the actual signal intended to be measured. We experimentally characterized three of these effects present in single nitrogen vacancy centers in diamond, where we also developed a numerical simulations model without rotating wave approximations, showing robust correlation to the experimental data. Regarding centers with the $^{15}$N nitrogen isotope, we observed that a small misalignment in the bias magnetic field causes the precession of the nitrogen nuclear spin to be sensed by the electronic spin of the center. Another studied case of ambiguous resonances comes from the coupling with lattice $^{13}$C nuclei, where we reconstructed the interaction Hamiltonian based on echo modulation frequencies and used this Hamiltonian to simulate multipulse sequences. Finally, we also measured and simulated the effects from the free evolution of the quantum system during finite pulse durations. Due to the large data volume and the strong dependency of these ambiguous resonances with specific experimental parameters, we provide a simulations dataset with a user-friendly graphical interface, where users can compare simulations with their own experimental data for spectral disambiguation. Although focused with nitrogen vacancy centers and dynamical decoupling sequences, these results and the developed model can potentially be applied to other solid state spins and quantum sensing techniques., Comment: 15 pages, 9 figures, for associated dataset, see https://figshare.com/articles/dataset/Dataset_for_Ambiguous_Resonances_in_Multipulse_Quantum_Sensing_with_NVs/26245895

We propose a new topological tool for computer vision - Scalar Function Topology Divergence (SFTD), which measures the dissimilarity of multi-scale topology between sublevel sets of two functions having a common domain. Functions can be defined on an undirected graph or Euclidean space of any dimensionality. Most of the existing methods for comparing topology are based on Wasserstein distance between persistence barcodes and they don't take into account the localization of topological features. The minimization of SFTD ensures that the corresponding topological features of scalar functions are located in the same places. The proposed tool provides useful visualizations depicting areas where functions have topological dissimilarities. We provide applications of the proposed method to 3D computer vision. In particular, experiments demonstrate that SFTD as an additional loss improves the reconstruction of cellular 3D shapes from 2D fluorescence microscopy images, and helps to identify topological errors in 3D segmentation. Additionally, we show that SFTD outperforms Betti matching loss in 2D segmentation problems.

In this study, a method for fabrication of superconducting microstructures that are partially or completely isolated from the substrate has been proposed. Two configurations of suspended microbridges have been suggested, i. e., the first structure that features a T-shaped etching of the substrate and the second structure which is completely separated from the substrate through periodically positioned supports. The creation of suspended structures is based on the principle of gas-phase etching of amorphous silicon oxide in a mixture of hydrogen fluoride (HF) and ethanol gases. In the course of the experiments, it has been discovered that suspending micro-structures in the configuration of a micro-bridge results in a slight reduction in superconducting characteristics, ranging from 10 to 15% of the initial parameters. It has also been demonstrated that the thermal coupling between the film and the substrate significantly affects the dissipation of thermal energy. The power dissipated into the substrate at room temperature can vary by up to 250 times based on the value of the micro-bridge undercutting.

The Extreme Universe Space Observatory on a Super Pressure Balloon 2 (EUSO-SPB2) flew on May 13$^{\text{th}}$ and 14$^{\text{th}}$ of 2023. Consisting of two novel optical telescopes, the payload utilized next-generation instrumentation for the observations of extensive air showers from near space. One instrument, the fluorescence telescope (FT) searched for Ultra-High Energy Cosmic Rays (UHECRs) by recording the atmosphere below the balloon in the near-UV with a 1~$\mu$s time resolution using 108 multi-anode photomultiplier tubes with a total of 6,912 channels. Validated by pre-flight measurements during a field campaign, the energy threshold was estimated around 2~EeV with an expected event rate of approximately 1 event per 10 hours of observation. Based on the limited time afloat, the expected number of UHECR observations throughout the flight is between 0 and 2. Consistent with this expectation, no UHECR candidate events have been found. The majority of events appear to be detector artifacts that were not rejected properly due to a shortened commissioning phase. Despite the earlier-than-expected termination of the flight, data were recorded which provide insights into the detectors stability in the near-space environment as well as the diffuse ultraviolet emissivity of the atmosphere, both of which are impactful to future experiments.

The present study delves into exploring the impact of a thin protective layer of scandium nitride (ScN) on the superconductive properties of thin films of niobium nitride (NbN) generated through reactive magnetron deposition. This is the first time such an investigation has been carried out. The article offers a comprehensive investigation of the morphological, microstructural, and electrophysical features of thin films that have undergone high-temperature annealing in an oxygen-rich environment. The dependence of the critical transition temperature of the NbN thin film on the annealing temperature of the samples in an oxygen medium, with and without ScN coating, was determined. According to X-ray reflectometry studies, it has been observed that the ScN film serves as a protective layer, even when exposed to annealing temperatures of approximately 450{\deg}C, without significantly affecting the NbN layer density or thickness. It has been shown that adding a ScN coating to a thin NbN film increases its resistance to corrosive media compared to a film without the coating.

Integrated development environments (IDEs) are prevalent code-writing and debugging tools. However, they have yet to be widely adopted for launching machine learning (ML) experiments. This work aims to fill this gap by introducing JetTrain, an IDE-integrated tool that delegates specific tasks from an IDE to remote computational resources. A user can write and debug code locally and then seamlessly run it remotely using on-demand hardware. We argue that this approach can lower the entry barrier for ML training problems and increase experiment throughput., Comment: IDE workshop @ ICSE 2024

This paper presents the development of a superconducting nanowire single-photon detector (SNSPD) integrated into a distributed Bragg reflector (DBR) with a design center wavelength of 830 nm and a width of 200 nm. This SNSPD is made of a superconducting niobium nitride (NbN) thin film that is produced using plasma-enhanced atomic layer deposition (PEALD). The DBR is made of 15 alternating layers of silicon nitride and silicon oxide that are produced through plasma-enhanced chemical vapor deposition (PECVD). The reflection efficiency of the mirror is 90% at a wavelength of 830 nm. For sufficient optical coupling, an optical micro-connector optimized for multimode or single-mode optical fibers with a diameter of 128 {\mu}m was formed using two-photon polymerization techniques. The niobium nitride film was deposited onto the DBR surface in-situ in two separate reactors connected by a vacuum transfer. The in-situ technique of deposition of a superconducting niobium nitride film and a distributed Bragg reflector has allowed achieving a detection efficiency of 90% at a wavelength of 830 nm and a dark count rate of 10 s-1 at a temperature of 2.5 K. Additionally, the detector jitter was 50 ps.

Neural Architecture Search (NAS) methods are widely used in various industries to obtain high quality taskspecific solutions with minimal human intervention. Event Sequences find widespread use in various industrial applications including churn prediction customer segmentation fraud detection and fault diagnosis among others. Such data consist of categorical and real-valued components with irregular timestamps. Despite the usefulness of NAS methods previous approaches only have been applied to other domains images texts or time series. Our work addresses this limitation by introducing a novel NAS algorithm SeqNAS specifically designed for event sequence classification. We develop a simple yet expressive search space that leverages commonly used building blocks for event sequence classification including multihead self attention convolutions and recurrent cells. To perform the search we adopt sequential Bayesian Optimization and utilize previously trained models as an ensemble of teachers to augment knowledge distillation. As a result of our work we demonstrate that our method surpasses state of the art NAS methods and popular architectures suitable for sequence classification and holds great potential for various industrial applications., Comment: in IEEE Access