A simple mechanical polishing treatment of commercial solid-gold electrodes (SGEs) can renew the active gold surface, reduce manufacturing-related grooves, and markedly improve the repeatability of geometric-area estimation and the analytical performance in stripping voltammetry. The work focuses on the accurate determination of the geometric area of a SGE by two voltammetric techniques. Cyclic voltammetry (CV) at different scan rates, referred to as the Randles-& Scaron;ev & ccaron;ik equation, and voltage scans at different electrode rotation rates, based on the Levich equation, were performed. The geometric area of the SGE was also evaluated by scanning electron microscopy (SEM). Commercial SGEs show grooves on their surface, derived from the fabrication processes. The effects of these grooves on the voltammetric response were investigated. The measurements were carried out on the SGE both as received from the manufacturer and after a reduction in the grooves height by a drastic mechanical treatment. After the treatment, the estimated area values were lower and more precise (3.05 +/- 0.02 mm2). Moreover, the reduction in the grooves' height affected the area estimations in contrast with the meaning of the geometric area, as intended by the Randles-& Scaron;ev & ccaron;ik and Levich equations. Furthermore, the gold exposed surface was measured by CV in sulphuric acid. Finally, the SGE was tested for the detection of Hg in a NaCl solution by anodic stripping voltammetry: the repeatability of the response improved after the mechanical treatment, confirming the usefulness of this step before electrode usage.
Counting processes occur very often in several scientific and technological problems. The concept of numerousness and, consequently, the counting of a number of items are at the base of many high-level measurements, as well as in everyday life applications. The occurrence of counting errors is a real issue that needs to be addressed. It might occur, for example, that one fails in counting one or more objects because of some reasons, such as human or instrumental errors. In such a case, the measurand, i.e., the number of items intended to be counted, is underestimated. On the other hand, one may count non-existing objects, hence obtaining an overestimate of the measurand. In a previous paper, a general model for measurements by counting was proposed which allows an uncertainty evaluation consistent with the general framework of the “Guide to the expression of uncertainty in measurement”. The present work considers the same scenario but facing the problem from a Bayesian point of view. In particular, we propose a (discrete) likelihood function of the counted objects, modelling the counting errors, and derive the posterior probability mass function for some selected prior distributions. Incorporating prior information on the measurand and the available knowledge on the counting errors, the proposed Bayesian model is able to provide measurand estimates corrected for such errors and accompanied by appropriate measurement uncertainties.
Cluster states are a fundamental resource for continuous-variable quantum computing, enabling measurement-based protocols that can scale beyond the limitations of qubit-based architectures. Here, we demonstrate on-demand generation of multimode entangled microwave cluster states using a programmable Josephson Traveling-Wave Parametric Amplifier (JTWPA) operated in the three-wave mixing regime. By injecting a tailored, non-equidistant set of pump tones via an arbitrary waveform generator, we engineer frequency-specific nonlinear couplings between multiple frequency modes. The entanglement structure is verified via frequency-resolved heterodyne detection of quadrature nullifiers, confirming the target graph topology of the cluster state. Our approach allows reconfigurability through the pumps spectrum and supports scalability by leveraging the wide bandwidth and spatial homogeneity of the JTWPA. This platform opens new avenues for scalable measurement-based quantum information processing in the microwave domain, compatible with superconducting circuit architectures.
Reservoir computing (RC) is a computational framework where a fixed dynamical reservoir projects an input into a higher-dimensional state that is then analyzed by a readout , which is trained to map the reservoir state into the desired output. While the conventional RC paradigm is based on dynamics of in-silico implemented recurrent neural networks, this computing paradigm can be efficiently implemented in hardware by exploiting dynamics of a wide range of physical systems in a paradigm denoted as Physical RC (PRC), attracting interest from a broader research community spanning from computer scientists to physicists, and material scientists. Here, we present RCbench, an open-source RC benchmark toolkit that implements a standardized and comprehensive suite for benchmarking computational reservoir models and physical implementations of RC. RCbench integrates widely recognized metrics such as Memory capacity, Nonlinear autoregressive moving average of order N, Kernel rank , and generalization rank, along with nonlinear transformation tasks. It also allows testing and comparing different readout algorithms, the evaluation of computational capabilities with diverse accuracy metrics, and includes feature selection methods to unravel the effect of specific reservoir outputs on computational performances. In particular, the toolkit enables easy benchmarking of PRC systems, providing a comprehensive benchmark tool that can be easily integrated with experimental data acquisition processes. By standardizing performance assessments, RCbench aims to facilitate inter-study comparisons and to accelerate the exploration, characterization and optimization of RC systems.
Recovering both amplitude and phase information from a system is a fundamental goal of optical imaging. At the same time, it is crucial to operate at low photon doses to avoid altering the sample, particularly in biological applications. Quantum imaging provides a powerful route to extract more information per photon than classical techniques, which are ultimately limited by shot-noise. However, the trade-off between quantum noise reduction and spatial resolution has long been regarded as a major obstacle to the application of quantum techniques to small cellular and sub-cellular structures, where they could offer the greatest benefits. Here, we overcome this limitation by demonstrating sub-shot-noise quantitative phase imaging of biological cells based on the transport-of-intensity equation, enabling high-fidelity, label-free imaging of key cellular and sub-cellular features. We achieve high-resolution phase imaging limited only by the numerical aperture, while simultaneously obtaining a resolution-independent quantum advantage. Unlike other quantum imaging approaches, our method operates in a quasi-single-shot, wide-field configuration, retrieves both phase and amplitude information, and does not rely on interferometric measurements, making it intrinsically fast and stable. These results pave the way for the immediate application of sub-shot-noise imaging in biological microscopy.
