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Quantum Key Distribution (QKD) promises to revolutionize the field of security in communication, with applications ranging from state secrets to personal data, making it a key player in the ongoing battle against cyber threats. Reference-Frame-Independent (RFI) QKD aims to simplify QKD implementations by allowing to reduce the requirements of alignment on a shared reference frame. This is done by performing two mutually unbiased measurements on the control states. In this work, we present a novel fully passive receiver for time-bin encoded RFI-QKD. Conversion of time-bin to polarization is employed to perform the required quantum measurement in a fully passive manner. Furthermore, to overcome experimental errors, we retrieved a complete description of our measurement apparatus by employing a recently introduced Quantum Detector Self-Characterization technique, without performing tomographic studies on the detection stage. In fact, the security analysis carried out in this work uses experimentally retrieved Positive Operator Valued Measurements, which consider our receiver defects, substituting the ideal expected operators and thus increasing the overall level of secrecy. Lastly, we conducted a proof-of-principle experiment that validated the feasibility of our method and its applicability to QKD applications., Comment: 13 pages, 6 figures, 1 table

One of the main statistical features of near-neutral atmospheric boundary layer (ABL) turbulence is the positive vertical velocity skewness $Sk_w$ above the roughness sublayer or the buffer region in smooth-walls. The $Sk_w$ variations are receiving renewed interest in many climate-related parameterizations of the ABL given their significance to cloud formation and to testing sub-grid schemes for Large Eddy Simulations (LES). The vertical variations of $Sk_w$ are explored here using high Reynolds number wind tunnel and flume experiments collected above smooth, rough, and permeable-walls in the absence of buoyancy and Coriolis effects. These laboratory experiments form a necessary starting point to probe the canonical structure of $Sk_w$ as they deal with a key limiting case (i.e. near-neutral conditions) that has received much less attention compared to its convective counterpart in atmospheric turbulence studies. Diagnostic models based on cumulant expansions, realizability constraints, and the now-popular constant mass flux approach routinely employed in the convective boundary layer as well as prognostic models based on third-order budgets are used to explain variations in $Sk_w$ for the idealized laboratory conditions. The failure of flux-gradient relations to model $Sk_w$ from the gradients of the vertical velocity variance $\sigma_w^2$ are explained and corrections based on models of energy transport offered. Novel links between the diagnostic and prognostic models are also featured, especially for the inertial term in the third order budget of the vertical velocity fluctuation. The co-spectral properties of $w'/\sigma_w$ versus $w'^2/\sigma_w^2$ are also presented for the first time to assess the dominant scales governing $Sk_w$ in the inner and outer layers, where $w'$ is the fluctuating vertical velocity and $\sigma_w$ is the vertical velocity standard deviation.

The remnant black hole from a binary coalescence emits ringdown gravitational waves characterized by quasi-normal modes, which depend solely on the remnant's mass and spin. In contrast, the ringdown amplitudes and phases are determined by the properties of the merging progenitors. Accurately modeling these amplitudes and phases reduces systematic biases in parameter estimation and enables the development and performance of rigorous tests of general relativity. We present a state-of-the-art, data-driven surrogate model for ringdown amplitudes and phases, leveraging Gaussian Process Regression trained against SXS numerical-relativity simulations. Focusing on non-precessing, quasi-circular binary black holes, our model offers the most comprehensive fit that includes 16 emission modes, incorporating overtones and quadratic contributions. Our surrogate model achieves reconstruction errors that are approximately two orders of magnitude smaller than the typical measurement errors of current gravitational-wave interferometers. An additional benefit of our approach is its flexibility, which allows for future extensions to include features such as eccentricity and precession, broadening the scope of its applicability to more generic astrophysical scenarios. Finally, we are releasing our model in a ready-to-use package called postmerger., Comment: 12 pages, 7 figures; code usage examples are available at https://github.com/cpacilio/postmerger

The recent availability of quantum annealers as cloud-based services has enabled new ways to handle machine learning problems, and several relevant algorithms have been adapted to run on these devices. In a recent work, linear regression was formulated as a quadratic binary optimization problem that can be solved via quantum annealing. Although this approach promises a computational time advantage for large datasets, the quality of the solution is limited by the necessary use of a precision vector, used to approximate the real-numbered regression coefficients in the quantum formulation. In this work, we focus on the practical challenge of improving the precision vector encoding: instead of setting an array of generic values equal for all coefficients, we allow each one to be expressed by its specific precision, which is tuned with a simple adaptive algorithm. This approach is evaluated on synthetic datasets of increasing size, and linear regression is solved using the D-Wave Advantage quantum annealer, as well as classical solvers. To the best of our knowledge, this is the largest dataset ever evaluated for linear regression on a quantum annealer. The results show that our formulation is able to deliver improved solution quality in all instances, and could better exploit the potential of current quantum devices.

Input variables in numerical models are often subject to several levels of uncertainty, usually modeled by probability distributions. In the context of uncertainty quantification applied to these models, studying the robustness of output quantities with respect to the input distributions requires: (a) defining variational classes for these distributions; (b) calculating boundary values for the output quantities of interest with respect to these variational classes. The latter should be defined in a way that is consistent with the information structure defined by the ``baseline'' choice of input distributions. Considering parametric families, the variational classes are defined using the geodesic distance in their Riemannian manifold, a generic approach to such problems. Theoretical results and application tools are provided to justify and facilitate the implementation of such robustness studies, in concrete situations where these distributions are truncated -- a setting frequently encountered in applications. The feasibility of our approach is illustrated in a simplified industrial case study.

We construct three dimensional non-semisimple topological field theories from the unrolled quantum group of the Lie superalgebra $\mathfrak{osp}(1 \vert 2)$. More precisely, the quantum group depends on a root of unity $q=e^{\frac{2 \pi \sqrt{-1}}{r}}$, where $r$ is a positive integer greater than $2$, and the construction applies when $r$ is not congruent to $4$ modulo $8$. The algebraic result which underlies the construction is the existence of a relative modular structure on the non-finite, non-semisimple category of weight modules for the quantum group. We prove a Verlinde formula which allows for the computation of dimensions and Euler characteristics of topological field theory state spaces of unmarked surfaces. When $r$ is congruent to $\pm 1$ or $\pm 2$ modulo $8$, we relate the resulting $3$-manifold invariants with physicists' $\widehat{Z}$-invariants associated to $\mathfrak{osp}(1 \vert 2)$. Finally, we establish a relation between $\widehat{Z}$-invariants associated to $\mathfrak{sl}(2)$ and $\mathfrak{osp}(1 \vert 2)$ which was conjectured in the physics literature., Comment: 38 pages

We provide empirical evidence that within the inertial sub layer of adiabatic turbulent flows over smooth walls, the skewness of the vertical velocity component $Sk_w$ displays universal behaviour, being constant and constrained within the range $Sk_w \approx 0.1-0.16$, regardless of flow configuration and Reynolds number. A theoretical model is proposed to explain the observed behaviour, including the observed range of variations of $Sk_w$. The model clarifies why $Sk_w$ cannot be predicted from down-gradient closure approximations routinely employed in meteorological and climate models whereby $Sk_w$ impacts cloud formation and dispersion processes. The model also offers an alternative and implementable approach.

This paper presents the Nordic-Estonian Quantum Computing e-Infrastructure Quest - NordIQuEst - an international collaboration of scientific and academic organizations from Denmark, Estonia, Finland, Norway, and Sweden, working together to develop a hybrid High-Performance and Quantum Computing (HPC+QC) infrastructure. The project leverages existing and upcoming classical high-performance computing and quantum computing systems, facilitating the development of interconnected systems. Our effort pioneers a forward-looking architecture for both hardware and software capabilities, representing an early-stage development in hybrid computing infrastructure. Here, we detail the outline of the initiative, summarizing the progress since the project outset, and describing the framework established. Moreover, we identify the crucial challenges encountered, and potential strategies employed to address them., Comment: 15 pages, 2 figures

Real 3-manifold triangulations can be uniquely represented by isomorphism signatures. Databases of these isomorphism signatures are generated for a variety of 3-manifolds and knot complements, using SnapPy and Regina, then these language-like inputs are used to train various machine learning architectures to differentiate the manifolds, as well as their Dehn surgeries, via their triangulations. Gradient saliency analysis then extracts key parts of this language-like encoding scheme from the trained models. The isomorphism signature databases are taken from the 3-manifolds' Pachner graphs, which are also generated in bulk for some selected manifolds of focus and for the subset of the SnapPy orientable cusped census with $<8$ initial tetrahedra. These Pachner graphs are further analysed through the lens of network science to identify new structure in the triangulation representation; in particular for the hyperbolic case, a relation between the length of the shortest geodesic (systole) and the size of the Pachner graph's ball is observed., Comment: 35 pages; 23 figures; 7 tables

Geospatial data has been transformative for the monitoring of the Earth, yet, as in the case of (geo)physical monitoring, the measurements can have variable spatial and temporal sampling and may be associated with a significant level of perturbations degrading the signal quality. Denoising geospatial data is, therefore, essential, yet often challenging because the observations may comprise noise coming from different origins, including both environmental signals and instrumental artifacts, which are spatially and temporally correlated, thus hard to disentangle. This study addresses the denoising of multivariate time series acquired by irregularly distributed networks of sensors, requiring specific methods to handle the spatiotemporal correlation of the noise and the signal of interest. Specifically, our method focuses on the denoising of geodetic position time series, used to monitor ground displacement worldwide with centimeter- to-millimeter precision. Among the signals affecting GNSS data, slow slip events (SSEs) are of interest to seismologists. These are transients of deformation that are weakly emerging compared to other signals. Here, we design SSEdenoiser, a multi-station spatiotemporal graph-based attentive denoiser that learns latent characteristics of GNSS noise to reveal SSE-related displacement with sub-millimeter precision. It is based on the key combination of graph recurrent networks and spatiotemporal Transformers. The proposed method is applied to the Cascadia subduction zone, where SSEs occur along with bursts of tectonic tremors, a seismic rumbling identified from independent seismic recordings. The extracted events match the spatiotemporal evolution of tremors. This good space-time correlation of the denoised GNSS signals with the tremors validates the proposed denoising procedure.

We investigate entanglement detection in quantum materials through criteria based on the simultaneous suppression of collective matter excitations. Unlike other detection schemes, these criteria can be applied to continuous and unbounded variables. By considering a system of interacting dipoles on a lattice, we show the detection of collective entanglement arising from two different physical mechanisms, namely, the ferroelectric ordering and the dressing of matter degrees of freedom by light. In the latter case, the detection shows the formation of a collective entangled phase not directly related to spontaneous symmetry breaking. These results open a new perspective for the entanglement characterization of competing orders in quantum materials, and have direct application to quantum paraelectrics with large polariton splittings., Comment: 5 pages, 3 figures

Gravitational waves emitted by a ringing black hole allow us to perform precision tests of General Relativity in the strong field regime. With improvements to our current gravitational wave detectors and upcoming next-generation detectors, developing likelihood-free parameter inference infrastructure is critical as we will face complications like non-standard noise properties, partial data and incomplete signal modeling that may not allow for an analytically tractable likelihood function. In this work, we present a proof-of-concept strategy to perform likelihood-free Bayesian inference on ringdown gravitational waves using simulation based inference. Specifically, our method is based on truncated sequential neural posterior estimation, which trains a neural density estimator of the posterior for a specific observed data segment. We setup the ringdown parameter estimation directly in the time domain. We show that the parameter estimation results obtained using our trained networks are in agreement with well-established Markov-Chain methods for simulated injections as well as analysis on real detector data corresponding to GW150914. Additionally, to assess our approach's internal consistency, we show that the density estimators pass a Bayesian coverage test., Comment: 12 pages, 7 figures

Turbulent flows over a large surface area (S) covered by n obstacles experience an overall drag due to the presence of the ground and the protruding obstacles into the flow. The drag partition between the roughness obstacles and the ground is analyzed using an analytical model proposed by Raupach (1992) and is hereafter referred to as R92. The R92 is based on the premise that the wake behind an isolated roughness element can be described by a shelter area A and a shelter volume V. The individual sizes of A and V without any interference from other obstacles can be determined from scaling analysis for the spread of wakes. To upscale from an individual roughness element to n/S elements where wakes may interact, R92 adopted a background stress re-normalizing instead of reducing A or V with each element addition. This work shows that R92's approach only converges to a linear reduction in A and V for small n/S where wakes have low probability of interacting with one another. This probabilistic nature suggests that up-scaling from individual to multiple roughness elements can be re-formulated using stochastic averaging methods proposed here. The two approaches are shown to recover R92 under plausible conditions. Comparisons between R92 and available data on blocks and vegetation-like roughness elements confirm the practical utility of R92 and its potential use in large-scale models provided the relevant parameters accommodate certain features of the roughness element type (cube versus vegetation-like) and, to a lesser extent, their configuration throughout S.

The study of verbal subgroups within a group is well-known for being an effective tool to obtain structural information about a group. Therefore, conditions that allow the classification of words in a free group are of paramount importance. One of the most studied problems is to establish which words are concise, where a word $w$ is said to be concise if the verbal subgroup $w(G)$ is finite in each group $G$ in which $w$ takes only a finite number of values. The purpose of this article is to present some results, in which a hierarchy among words is introduced, generalizing the concept of concise word.

Entanglement-breaking channels (equivalently, measure-and-prepare channels) are an important class of quantum operations noted for their ability to destroy multipartite spatial quantum correlations. Inspired by this property, they have also been employed in defining notions of "classical memory", under the assumption that such channels effectively act as a classical resource. We show that, in a single-system multi-time scenario, entanglement-breaking channels are still a quantum memory resource: a qudit going through an entanglement-breaking channel cannot be simulated by a classical system of same dimension. We provide explicit examples of memory-based output generation tasks where entanglement-breaking channels outperform classical memories of the same size. Our results imply that entanglement-breaking channels cannot be generally employed to characterize classical memory effects in temporal scenarios without additional assumptions., Comment: 7 pages, 3 figures

We study what can or cannot be certified in communication scenarios where the assumption of independence and identical distribution (iid) between experimental rounds fails. In this respect, we prove that membership tests for non-convex sets of correlations cannot be formulated in the non-iid regime. Similarly, it is impossible to self-test non-extreme quantum operations, such as mixed states, or noisy quantum measurements, unless one allows more than a single use thereof within the same experimental round. One consequence of our results is that non-classicality in causal networks without inputs cannot be experimentally demonstrated. By analyzing optimal non-iid strategies in the triangle scenario, we raise the need to take into account the prior communication required to set up a causal network.

In this paper we revisit the description of all verbal subgroups of the group of automorphisms of a regular rooted tree $\mathcal{T}_d$, for $d>2$ and odd.

Metasurfaces represent a new frontier in materials science paving for unprecedented methods of controlling electromagnetic waves, with a range of applications spanning from sensing to imaging and communications. For pulsed terahertz generation, metasurfaces offer a gateway to tuneable thin emitters that can be utilised for large-area imaging, microscopy and spectroscopy. In literature THz-emitting metasurfaces generally exhibit high absorption, being based either on metals or on semiconductors excited in highly resonant regimes. Here we propose the use of a fully dielectric semiconductor exploiting morphology-mediated resonances and inherent quadratic nonlinear response. Our system exhibits a remarkable 40-fold efficiency enhancement compared to the unpatterned at the peak of the optimised wavelength range, demonstrating its potential as scalable emitter design.

Integration of Quantum Key Distribution (QKD) in existing telecommunication infrastructure is crucial for the widespread adoption of this quantum technology, which offers the distillation of unconditionally secure keys between users. In this letter, we report a field trial between the Points of Presence (POPs) placed in Treviso and in Venezia - Mestre, Italy, exploiting the QuKy commercial polarization-based QKD platforms developed by ThinkQuantum srl and two different standards of single-mode optical fibers, i.e. G.652 and G.655, as a quantum channel. In this field trial, several configurations were tested, including the co-existence of classical and quantum signals over the same fiber, providing a direct comparison between the performances of the G.652 and G.655 fiber standards for QKD applications., Comment: 6 pages, 5 figures

Combining multiple gravitational-wave observations allows for stringent tests of general relativity, targeting effects that would otherwise be undetectable using single-event analyses. We highlight how the finite size of the observed catalog induces a significant source of variance. If not appropriately accounted for, general relativity can be excluded with arbitrarily large credibility even if it is the underlying theory of gravity. This effect is generic and holds for arbitrarily large catalogs. Moreover, we show that it cannot be suppressed by selecting "golden" observations with large signal-to-noise ratios. We present a mitigation strategy based on bootstrapping (i.e. resampling with repetition) that allows assigning uncertainties to one's credibility on the targeted test. We demonstrate our findings using both toy models and real gravitational-wave data. In particular, we quantify the impact of the catalog variance on the ringdown properties of black holes using the latest LIGO/Virgo catalog., Comment: 6 pages, 2 figures; v2: matches published version