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We study the eigenfunctions of the classical Liouville operator and investigate the conditions they must obey to be separable as a product state. We point out that the conditions for separability are equivalent to requirements of Liouville's integrability theorem, this is, the eigenfunctions are separable if and only if the system is integrable. On the other hand, if the classical system is not integrable, then the eigenfunctions are entangled in all canonical coordinates. This results in a link between the classical notions of chaos and integrability with mathematical concepts that are usually restricted to quantum mechanics.

We show that recent experiments in hybrid qubit-oscillator devices that measure the phase-space characteristic function of the oscillator via the qubit can be seen through the lens of functional calculus and path integrals, drawing a clear analogy with the generating functional of a quantum field theory. This connection suggests an expansion of the characteristic function in terms of Feynman diagrams, exposing the role of the real-time bosonic propagator, and identifying the external source functions with certain time-dependent couplings that can be controlled experimentally. By applying maximum-likelihood techniques, we show that the ``measurement'' of these Feynman diagrams can be reformulated as a problem of multi-parameter point estimation that takes as input a set of Ramsey-type measurements of the qubit. By numerical simulations that consider leading imperfections in trapped-ion devices, we identify the optimal regimes in which Feynman diagrams could be reconstructed from measured data with low systematic and stochastic errors. We discuss how these ideas can be generalized to finite temperatures via the Schwinger-Keldysh formalism, contributing to a bottom-up approach to probe quantum simulators of lattice field theories by systematically increasing the qubit-oscillator number.

This paper introduces a comprehensive extension of the path integral formalism to model stochastic processes with arbitrary multiplicative noise. To do so, It\^o diffusive process is generalized by incorporating a multiplicative noise term $(\eta(t))$ that affects the diffusive coefficient in the stochastic differential equation. Then, using the Parisi-Sourlas method, we estimate the transition probability between states of a stochastic variable $(X(t))$ based on the cumulant generating function $(\mathcal{K}_{\eta})$ of the noise. A parameter $\gamma\in[0,1]$ is introduced to account for the type of stochastic calculation used and its effect on the Jacobian of the path integral formalism. Next, the Feynman-Kac functional is then employed to derive the Fokker-Planck equation for generalized It\^o diffusive processes, addressing issues with higher-order derivatives and ensuring compatibility with known functionals such as Onsager-Machlup and Martin-Siggia-Rose-Janssen-De Dominicis in the white noise case. The general solution for the Fokker-Planck equation is provided when the stochastic drift is proportional to the diffusive coefficient and $\mathcal{K}_{\eta}$ is scale-invariant. Finally, the Brownian motion ($BM$), the geometric Brownian motion ($GBM$), the Levy $\alpha$-stable flight ($LF(\alpha)$), and the geometric Levy $\alpha$-stable flight ($GLF(\alpha)$) are simulated with thresholds, providing analytical comparisons for the probability density, Shannon entropy, and entropy production rate. It is found that restricted $BM$ and restricted $GBM$ exhibit quasi-steady states since the rate of entropy production never vanishes. It is also worth mentioning that in this work the $GLF(\alpha)$ is defined for the first time in the literature and it is shown that its solution is found without the need for It\^o's lemma., Comment: 20 pages, 6 figures, for code of article see https://github.com/fsabrilb/Fokker_Planck_Distribution

Motivated by the wide presence of multilayer networks in both natural and human-made systems, within a random matrix theory (RMT) approach, in this study we compute eigenfunction and spectral properties of multilayer directed random networks (MDRNs) in two setups composed by $M$ layers of size $N$: A line and a complete graph (node-aligned multiplex network). First, we numerically demonstrate that the normalized localization length $\beta$ of the eigenfunctions of MDRNs follows a simple scaling law given by $\beta=x^*/(1+x^*)$, with $x^*\propto (b_{\rm eff}^2/L)^\delta$, $\delta\sim 1$ and $b_{\rm eff}$ being the effective bandwidth of the adjacency matrix of the network of size $L=M\times N$. Here, $b_{\rm eff}$ incorporates both intra- and inter-layer edges. Then, we show that other eigenfunction and spectral RMT measures (the inverse participation ratio of eigenfunctions, the ratio between nearest- and next-to-nearest-neighbor eigenvalue distances, and the ratio between consecutive singular-value spacings) of MDRNs also scale with $x^*$., Comment: 11 pages, 8 figures. arXiv admin note: substantial text overlap with arXiv:2406.15426, arXiv:1611.06695

The generalization of photon spheres by considering the trajectories of massive particles leads to the definition of Massive Particle Surfaces (MPS). These surfaces are built with the trajectories of massive particles, and have a partial umbilicity property. Using the geodesic and Gaussian curvature of the Jacobi metric (a Riemannian metric) we derive a general condition for the existence of a Massive Particle Surface defined for an asymptotically flat spacetime metric. Our results can be applied to the worldlines of charged massive particle surfaces. We provide a simple characterization for timelike and null trajectories using a Riemannian geometric approach. We are able to recover the results for the existence of Light Rings (LR's) and timelike circular orbits (TCO's). We show how an event horizon gets characterized using the curvatures of a Riemannian metric. We discuss several examples, where we derive conditions for the existence of photon sphere and a massive particle surface. We calculate the radius of the photon sphere and the radius of the Innermost Stable Circular Orbits (ISCO)., Comment: 19 pages

The phenomenon of particle production for quantum field theories in curved spacetimes is crucial to understand the large-scale structure of a universe from an inflationary epoch. In contrast to the free and fixed-background case, the production of particles with strong interactions and back reaction is not completely understood, especially in situations that require going beyond perturbation theory. In this work, we present advances in this direction by focusing on a self-interacting field theory of Dirac fermions in an expanding Friedmann-Robertson-Walker universe. By using a Hamiltonian lattice regularization with continuous conformal time and rescaled fields, this model becomes amenable to either a cold-atom analogue-gravity quantum simulation, or a dynamical variational approach. Leveraging a family of variational fermionic Gaussian states, we investigate how dynamical mass generation and the formation of fermion condensates associated to certain broken symmetries modify some well-known results of the free field theory. In particular, we study how the non-perturbative condensates arise and, more importantly, how their real-time evolution has an impact on particle production. Depending on the Hubble expansion rate, we find an interesting interplay of interactions and particle production, including a non-trivial back reaction on the condensates and a parity-breaking spectrum of produced particles., Comment: 23 pages, 14 figures

In this study, given the inherent nature of dissipation in realistic dynamical systems, we explore the effects of dissipation within the context of fractional dynamics. Specifically, we consider the dissipative versions of two well known fractional maps: the Riemann-Liouville (RL) and the Caputo (C) fractional standard maps (fSMs). Both fSMs are two-dimensional nonlinear maps with memory given in action-angle variables $(I_n,\theta_n)$; $n$ being the discrete iteration time of the maps. In the dissipative versions these fSMs are parameterized by the strength of nonlinearity $K$, the fractional order of the derivative $\alpha\in(1,2]$, and the dissipation strength $\gamma\in(0,1]$. In this work we focus on the average action $\left< I_n \right>$ and the average squared action $\left< I_n^2 \right>$ when~$K\gg1$, i.e. along strongly chaotic orbits. We first demonstrate, for $|I_0|>K$, that dissipation produces the exponential decay of the average action $\left< I_n \right> \approx I_0\exp(-\gamma n)$ in both dissipative fSMs. Then, we show that while $\left< I_n^2 \right>_{RL-fSM}$ barely depends on $\alpha$ (effects are visible only when $\alpha\to 1$), any $\alpha< 2$ strongly influences the behavior of $\left< I_n^2 \right>_{C-fSM}$. We also derive an analytical expression able to describe $\left< I_n^2 \right>_{RL-fSM}(K,\alpha,\gamma)$., Comment: 8 pages, 7 figures

While the nitrile group is by far the most prevalent one among interstellar molecules, the existence of interstellar dinitriles (molecules containing two -CN groups) has recently been proven. Here we report the discovery of two new dinitriles in the cold dense cloud TMC-1. These newly identified species are malononitrile, CH2(CN)2, and maleonitrile, the Z isomer of NC-CH=CH-CN, which can be seen as the result of substituting two H atoms with two -CN groups in methane and ethylene, respectively. These two molecules were detected using data from the ongoing QUIJOTE line survey of TMC-1 that is being carried out with the Yebes 40m telescope. We derive column densities of 1.8e11 cm-2 and 5.1e10 cm-2 for malononitrile and maleonitrile, respectively. This means that they are eight and three times less abundant than HCC-CH2-CN and (E)-HCC-CH=CH-CN, respectively, which are analog molecules detected in TMC-1 in which one -CN group is converted into a -CCH group. This is in line with previous findings in which -CCH derivatives are more abundant than the -CN counterparts in TMC-1. We examined the potential chemical pathways to these two dinitriles, and we find that while maleonitrile can be efficiently formed through the reaction of CN with CH2CHCN, the formation of malononitrile is not clear because the neutral-neutral reactions that could potentially form it are not feasible under the physical conditions of TMC-1., Comment: Accepted for publication in A&A Letters

The dust- and gas-rich protoplanetary disks around young stellar systems play a key role in star and planet formation. While considerable progress has recently been made in probing these disks on large scales of a few tens of astronomical units (au), the central au needs to be more investigated. We aim at unveiling the physical processes at play in the innermost regions of the strongly accreting T Tauri Star S CrA N by means of near-infrared interferometric observations. The K-band continuum emission is well reproduced with an azimuthally-modulated dusty ring. As the star alone cannot explain the size of this sublimation front, we propose that magnetospheric accretion is an important dust-heating mechanism leading to this continuum emission. The differential analysis of the Hydrogen Br$\gamma$ line is in agreement with radiative transfer models combining magnetospheric accretion and disk winds. Our observations support an origin of the Br$\gamma$ line from a combination of (variable) accretion-ejection processes in the inner disk region.

Transient grating spectroscopy has become a mainstay among metal and semiconductor characterization techniques. Here we extend the technique towards the shortest achievable timescales by using tabletop high-harmonic generation of attosecond extreme ultraviolet (XUV) pulses that diffract from transient gratings generated with 500-1000 nm sub-5 fs near-infrared (NIR) pulses. We demonstrate the power of attosecond transient grating spectroscopy (ATGS) by investigating the ultrafast photoexcited dynamics in an Sb semimetal thin film. ATGS provides an element-specific, background-free signal, unfettered by spectral congestion, in contrast to transient absorption spectroscopy. With the ATGS measurements in Sb polycrystalline thin films, we observe the generation of coherent phonons and investigate the lattice and carrier dynamics. Among the latter processes, we extract carrier thermalization, hot carrier cooling, and electron-hole recombination, which are on the order of 20 fs, 50 fs, and 2 ps, timescales, respectively. Furthermore, simultaneous collection of transient absorption and transient grating data allows us to extract the total complex dielectric constant in the sample dynamics, including the real-valued refractive index, from which we are also able to investigate carrier-phonon interactions and longer-lived phonon dynamics. The outlined experimental technique expands the capabilities of transient grating spectroscopy and attosecond spectroscopies by providing a wealth of information concerning carrier and lattice dynamics with an element-selective technique, at the shortest achievable timescales.

Hyperspectral image (HI) analysis approaches have recently become increasingly complex and sophisticated. Recently, the combination of spectral-spatial information and superpixel techniques have addressed some hyperspectral data issues, such as the higher spatial variability of spectral signatures and dimensionality of the data. However, most existing superpixel approaches do not account for specific HI characteristics resulting from its high spectral dimension. In this work, we propose a multiscale superpixel method that is computationally efficient for processing hyperspectral data. The Simple Linear Iterative Clustering (SLIC) oversegmentation algorithm, on which the technique is based, has been extended hierarchically. Using a novel robust homogeneity testing, the proposed hierarchical approach leads to superpixels of variable sizes but with higher spectral homogeneity when compared to the classical SLIC segmentation. For validation, the proposed homogeneity-based hierarchical method was applied as a preprocessing step in the spectral unmixing and classification tasks carried out using, respectively, the Multiscale sparse Unmixing Algorithm (MUA) and the CNN-Enhanced Graph Convolutional Network (CEGCN) methods. Simulation results with both synthetic and real data show that the technique is competitive with state-of-the-art solutions.

Gate set tomography (GST) allows for a self-consistent characterization of noisy quantum information processors. The standard device-agnostic approach treats the QIPs as black boxes that are only constrained by the laws of physics, attaining full generality at a considerable resource cost: numerous circuits built from the gate set must be run in order to amplify each of the gate set parameters. In this work, we show that a microscopic parametrization of quantum gates under time-correlated noise on the driving phase, motivated by recent experiments with trapped-ion gates, reduces the required resources enabling a more efficient version of GST. By making use of the formalism of filter functions over the noise spectral densities, we discuss the minimal parametrizations of the gate set that include the effect of finite correlation times and non-Markovian quantum evolutions during the individual gates. We compare the estimated gate sets obtained by our method and the standard long-sequence GST, discussing their accuracies in terms of established metrics, as well as showcasing the advantages of the parametrized approach in terms of the sampling complexity for specific examples.

We performed numerical simulations of the common envelope (CE) interaction between two intermediate-mass asymptotic giant branch (AGB) stars and their low-mass companions. For the first time, formation and growth of dust in the envelope is calculated explicitly. We find that the first dust grains appear as early as $\sim$1-3 yrs after the onset of the CE, and are smaller than grains formed later. As the simulations progress, a high-opacity dusty shell forms, resulting in the CE photosphere being up to an order of magnitude larger than it would be without the inclusion of dust. At the end of the simulations, the total dust yield is $0.0082~M_{\odot}$ ($0.022~M_{\odot}$) for a CE with a $1.7~M_{\odot}$ ($3.7~M_{\odot}$) AGB star. Dust formation does not substantially lead to more mass unbinding or substantially alter the orbital evolution., Comment: 6 pages, 6 figures, contributed talk to the Proceedings IAU Symposium No. 384

We introduce VOODOO XP: a 3D-aware one-shot head reenactment method that can generate highly expressive facial expressions from any input driver video and a single 2D portrait. Our solution is real-time, view-consistent, and can be instantly used without calibration or fine-tuning. We demonstrate our solution on a monocular video setting and an end-to-end VR telepresence system for two-way communication. Compared to 2D head reenactment methods, 3D-aware approaches aim to preserve the identity of the subject and ensure view-consistent facial geometry for novel camera poses, which makes them suitable for immersive applications. While various facial disentanglement techniques have been introduced, cutting-edge 3D-aware neural reenactment techniques still lack expressiveness and fail to reproduce complex and fine-scale facial expressions. We present a novel cross-reenactment architecture that directly transfers the driver's facial expressions to transformer blocks of the input source's 3D lifting module. We show that highly effective disentanglement is possible using an innovative multi-stage self-supervision approach, which is based on a coarse-to-fine strategy, combined with an explicit face neutralization and 3D lifted frontalization during its initial training stage. We further integrate our novel head reenactment solution into an accessible high-fidelity VR telepresence system, where any person can instantly build a personalized neural head avatar from any photo and bring it to life using the headset. We demonstrate state-of-the-art performance in terms of expressiveness and likeness preservation on a large set of diverse subjects and capture conditions.

Here we introduce the non-Hermitian diluted banded random matrix (nHdBRM) ensemble as the set of $N\times N$ real non-symmetric matrices whose entries are independent Gaussian random variables with zero mean and variance one if $|i-j|

The essence of the \textit{memory burden} effect is that a load of information carried by a system stabilizes it. This universal effect is especially prominent in systems with a high capacity of information storage, such as black holes and other objects with maximal microstate degeneracy, the entities universally referred to as \textit{saturons}. The phenomenon has several implications. The memory burden effect suppresses a further decay of a black hole, the latest, after it has emitted about half of its initial mass. As a consequence, the light primordial black holes (PBHs), that previously were assumed to be fully evaporated, are expected to be present as viable dark matter candidates. In the present paper, we deepen the understanding of the memory burden effect. We first identify various memory burden regimes in generic Hamiltonian systems and then establish a precise correspondence in solitons and in black holes. We make transparent, at a microscopic level, the fundamental differences between the stabilization by a quantum memory burden versus the stabilization by a long-range classical hair due to a spin or an electric charge. We identify certain new features of potential observational interest, such as the model-independent spread of the stabilized masses of initially degenerate PBHs., Comment: 30 pages, 6 figures, video summary at https://youtu.be/boDpRXJnT5E

In this paper we study networks of nodes characterised by binary traits that change both endogenously and through nearest-neighbour interaction. Our analytical results show that those traits can be ranked according to the noisiness of their transmission using only measures of order in the stationary state. Crucially, this ranking is independent of network topology. As an example, we explain why, in line with a long-standing hypothesis, the relative stability of the structural traits of languages can be estimated from their geospatial distribution. We conjecture that similar inferences may be possible in a more general class of Markovian systems. Consequently, in many empirical domains where longitudinal information is not easily available the propensities of traits to change could be estimated from spatial data alone., Comment: 6+78 pages, 4+23 figures

Stellar convection plays an important role in atmospheric dynamics, wind formation and the mass-loss processes in Asymptotic Giant Branch (AGB) stars. However, a direct characterization of convective surface structures in terms of size, contrast, and life-span is quite challenging. Spatially resolving these features requires the highest angular resolution. In this work, we aim at characterizing the size of convective structures on the surface of the O-rich AGB star R Car to test different theoretical predictions, based on mixing-length theory from solar models. We used infrared low-spectral resolution (R~35) interferometric data in the H-band (~1.76 $\mu$m) with the instrument PIONIER at the Very Large Telescope Interferometer (VLTI) to image the star's surface at two epochs separated by ~6 years. Using a power spectrum analysis, we estimate the horizontal size of the structures on the surface of R Car. The sizes of the stellar disk, at different phases of a pulsation cycle, were obtained using parametric model-fitting in the Fourier domain. Our analysis supports that the sizes of the structures in R Car are correlated with variations of the pressure scale height in the atmosphere of the target, as predicted by theoretical models based on solar convective processes. We observe that these structures grow in size when the star expands within a pulsation cycle. While the information is still scarce, this observational finding highlights the role of convection in the dynamics of those objects. New interferometric imaging campaigns with the renewed capabilities of the VLTI are envisioned to expand our analysis to a larger sample of objects., Comment: Accepted by A&A on 15/05/2024, 11 pages, 7 figures

Our purpose is to study the effect of binary companions located within the first 10 stellar radii from the primary AGB star. In this work, we target the mass-losing carbon star V Hydrae (V Hya), looking for signatures of its companion in the dust forming region of the atmosphere. The star was observed in the L- and N-bands with the VLTI/MATISSE instrument at low spectral resolution. We reconstructed images of V Hya's photosphere and surroundings using the two bands and compared our interferometric observables with VLTI/MIDI and VISIR archival data. To constrain the dust properties, we used DUSTY to model the spectral energy distribution. The star is dominated by dust emission in the L- and N- bands. The VISIR image confirms the presence of a large-scale dusty circumstellar envelope surrounding V Hya. The MATISSE reconstructed images show asymmetric and elongated structures in both infrared bands. In the L-band, we detected an elongated shape of approximately 15 mas, likely to be of photospheric origin. In the N-band, we found a 20 mas extension North-East from the star, and perpendicular to the L-band elongated axis. The position angle and the size of the N-band extension match the prediction of the companion position at MATISSE epoch. By comparing MATISSE N-band with MIDI data, we deduce that the elongation axis in the N-band has rotated since the previous interferometric measurements 13 years ago, supporting the idea that the particle enhancement is related to the dusty clump moving along with the companion. The MATISSE images unveil the presence of a dust enhancement at the companion position, opening new doors for further analysis on the binary interaction with an AGB component., Comment: Accepted for publication in Astronomy & Astrophysics

Singular-value statistics (SVS) has been recently presented as a random matrix theory tool able to properly characterize non-Hermitian random matrix ensembles [PRX Quantum {\bf 4}, 040312 (2023)]. Here, we perform a numerical study of the SVS of the non-Hermitian adjacency matrices $\mathbf{A}$ of directed random graphs, where $\mathbf{A}$ are members of diluted real Ginibre ensembles. We consider two models of directed random graphs: Erd\"os-R\'enyi graphs and random regular graphs. Specifically, we focus on the ratio $r$ between nearest neighbor singular values and the minimum singular value $\lambda_{min}$. We show that $\langle r \rangle$ (where $\langle \cdot \rangle$ represents ensemble average) can effectively characterize the transition between mostly isolated vertices to almost complete graphs, while the probability density function of $\lambda_{min}$ can clearly distinguish between different graph models., Comment: 7 pages, 6 figures

In recent years, there has been a growing interest in using Machine Learning (ML), especially Deep Learning (DL) to solve Network Intrusion Detection (NID) problems. However, the feature distribution shift problem remains a difficulty, because the change in features' distributions over time negatively impacts the model's performance. As one promising solution, model pretraining has emerged as a novel training paradigm, which brings robustness against feature distribution shift and has proven to be successful in Computer Vision (CV) and Natural Language Processing (NLP). To verify whether this paradigm is beneficial for NID problem, we propose SwapCon, a ML model in the context of NID, which compresses shift-invariant feature information during the pretraining stage and refines during the finetuning stage. We exemplify the evidence of feature distribution shift using the Kyoto2006+ dataset. We demonstrate how pretraining a model with the proper size can increase robustness against feature distribution shifts by over 8%. Moreover, we show how an adequate numerical embedding strategy also enhances the performance of pretrained models. Further experiments show that the proposed SwapCon model also outperforms eXtreme Gradient Boosting (XGBoost) and K-Nearest Neighbor (KNN) based models by a large margin., Comment: accepted by ICMLCN24

We observed the planet-hosting system PDS 70 with the James Webb Interferometer, JWST's Aperture Masking Interferometric (AMI) mode within NIRISS. Observing with the F480M filter centered at 4.8 $\mu$m, we simultaneously fit a geometric model to the outer disk and the two known planetary companions. We re-detect the protoplanets PDS 70 b and c at an SNR of 21 and 11, respectively. Our photometry of both PDS 70 b and c provide evidence for circumplanetary disk emission through fitting SED models to these new measurements and those found in the literature. We also newly detect emission within the disk gap at an SNR of $\sim$4, at a position angle of $207^{+11}_{-10}$ degrees, and an unconstrained separation within $\sim$200 mas. Follow-up observations will be needed to determine the nature of this emission. We place a 5$\sigma$ upper limit of $\Delta$mag = 7.56 on the contrast of the candidate PDS 70 d at 4.8 $\mu$m, which indicates that if the previously observed emission at shorter wavelengths is due to a planet, this putative planet has a different atmospheric composition than PDS 70 b or c. Finally, we place upper limits on emission from any additional planets in the disk gap. We find an azimuthally averaged 5$\sigma$ upper limit of $\Delta$mag $\approx$ 7.5 at separations greater than 125 mas. These are the deepest limits to date within $\sim$250 mas at 4.8 $\mu$m and the first space-based interferometric observations of this system., Comment: Submitted to ApJ

We introduce Lindblad-like quantum tomography (L$\ell$QT) as a quantum characterization technique of time-correlated noise in quantum information processors. This approach enables the estimation of time-local master equations, including their possible negative decay rates, by maximizing a likelihood function subject to dynamical constraints. We discuss L$\ell$QT for the dephasing dynamics of single qubits in detail, which allows for a neat understanding of the importance of including multiple snapshots of the quantum evolution in the likelihood function, and how these need to be distributed in time depending on the noise characteristics. By a detailed comparative study employing both frequentist and Bayesian approaches, we assess the accuracy and precision of L$\ell$QT of a dephasing quantum dynamical map that goes beyond the Lindblad limit, focusing on two different microscopic noise models that can be realised in either trapped-ion or superconducting-circuit architectures. We explore the optimization of the distribution of measurement times to minimize the estimation errors, assessing the superiority of each learning scheme conditioned on the degree of non-Markovinity of the noise, and setting the stage for future experimental designs of non-Markovian quantum tomography.

The Caputo fractional standard map (C-fSM) is a two-dimensional nonlinear map with memory given in action-angle variables $(I,\theta)$. It is parameterized by $K$ and $\alpha\in(1,2]$ which control the strength of nonlinearity and the fractional order of the Caputo derivative, respectively. In this work we perform a scaling study of the average squared action $\left< I^2 \right>$ along strongly chaotic orbits, i.e. when $K\gg1$. We numerically prove that $\left< I^2 \right>\propto n^\mu$ with $0\le\mu(\alpha)\le1$, for large enough discrete times $n$. That is, we demonstrate that the C-fSM displays subdiffusion for $1<\alpha<2$. Specifically, we show that diffusion is suppressed for $\alpha\to1$ since $\mu(1)=0$, while standard diffusion is recovered for $\alpha=2$ where $\mu(2)=1$. We describe our numerical results with a phenomenological analytical estimation. We also contrast the C-fSM with the Riemann-Liouville fSM and Chirikov's standard map., Comment: 5 pages, 3 figures

We present three statistical descriptions for systems of classical particles and consider their extension to hybrid quantum-classical systems. The classical descriptions are ensembles on configuration space, ensembles on phase space, and a Hilbert space approach using van Hove operators which provides an alternative to the Koopman-von Neumann formulation. In all cases, there is a natural way to define classical observables and a corresponding Lie algebra that is isomorphic to the usual Poisson algebra in phase space. We show that in the case of classical particles, the three descriptions are equivalent and indicate how they are related. We then modify and extend these descriptions to introduce hybrid models where a classical particle interacts with a quantum particle. The approach of ensembles on phase space and the Hilbert space approach, which are novel, lead to equivalent hybrid models, while they are not equivalent to the hybrid model of the approach of ensembles on configuration space. Thus, we end up identifying two inequivalent types of hybrid systems, making different predictions, especially when it comes to entanglement. These results are of interest regarding ``no-go'' theorems about quantum systems interacting via a classical mediator which address the issue of whether gravity must be quantized. Such theorems typically require assumptions that make them model dependent. The hybrid systems that we discuss provide concrete examples of inequivalent models that can be used to compute simple examples to test the assumptions of the ``no-go'' theorems and their applicability., Comment: 18 pages. To be published in The European Physics Journal Plus as a contribution to the Focus Point on "Mathematics and Physics at the Quantum-Classical Interface.'' The changes in this replacement include substantial revisions in the sections that address the problem of constructing appropriate solutions as well as the addition of new Appendices

Characterizing the dynamics of quantum systems is a central task for the development of quantum information processors (QIPs). It serves to benchmark different devices, learn about their specific noise, and plan the next hardware upgrades. However, this task is also very challenging, for it requires a large number of measurements and time-consuming classical processing. Moreover, when interested in the time dependence of the noise, there is an additional overhead since the characterization must be performed repeatedly within the time interval of interest. To overcome this limitation while, at the same time, ordering the learned sources of noise by their relevance, we focus on the inference of the dynamical generators of the noisy dynamics using Lindbladian quantum tomography (LQT). We propose two different improvements of LQT that alleviate previous shortcomings. In the weak-noise regime of current QIPs, we manage to linearize the maximum likelihood estimation of LQT, turning the constrained optimization into a convex problem to reduce the classical computation cost and to improve its robustness. Moreover, by introducing compressed sensing techniques, we reduce the number of required measurements without sacrificing accuracy. To illustrate these improvements, we apply our LQT tools to trapped-ion experiments of single- and two-qubit gates, advancing in this way the previous state of the art.

The aim of this work is to introduce a thermo-electromagnetic model for calculating the temperature and the power dissipated in cylindrical pieces whose geometry var\'ies with time and undergoes large deformations; the motion will be a known data. The work will be a first step towards building a complete thermoelectromagnetic-mechanical model suitable for simulating electrically assisted forming processes, which is the main motivation of the work. The electromagnetic model will be obtained from the time-harmonic eddy current problem with an inplane current; the source will be given in terms of currents or voltages defined at sorne parts of the boundary. Finite element methods based on a Lagrangian weak formulation will be used for the numerical solution. This approach will avoid the need to compute and remesh the thermo-electromagnetic domain along the time. The numerical tools will be implemented in FEniCS and validated by using a suitable test also solved in Eulerian coordinates.

This work proposes a methodology combining distributed and lumped models to simulate the current distribution in an indirect heat resistance furnace and, in particular, to compute the current to be supplied in order to obtain a desired power. The distributed model is a time-harmonic eddy current problem which has been numerically solved by using a finite element method. The lumped model is based on the computation of a reduced impedance associated to an equivalent circuit model. The effectiveness of the method has been assessed by numerical simulations and plant measurements. The good correlation between the results reveals this approximation is well-suited in order to aid the design and improve the efficiency of the furnace in a short-time.

The Riemann-Liouville fractional standard map (RL-fSM) is a two-dimensional nonlinear map with memory given in action-angle variables $(I,\theta)$. The RL-fSM is parameterized by $K$ and $\alpha\in(1,2]$ which control the strength of nonlinearity and the fractional order of the Riemann-Liouville derivative, respectively. In this work, we present a scaling study of the average squared action $\left< I^2 \right>$ of the RL-fSM along strongly chaotic orbits, i.e. for $K\gg1$. We observe two scenarios depending on the initial action $I_0$, $I_0\ll K$ or $I_0\gg K$. However, we can show that $\left< I^2 \right>/I_0^2$ is a universal function of the scaled discrete time $nK^2/I_0^2$ ($n$ being the $n$th iteration of the RL-fSM). In addition, we note that $\left< I^2 \right>$ is independent of $\alpha$ for $K\gg1$. Analytical estimations support our numerical results., Comment: 5 pages, 3 figures

Noise is both ubiquitous and generally deleterious in settings where precision is required. This is especially true in the quantum technology sector where system utility typically decays rapidly under its influence. Understanding the noise in quantum devices is thus a prerequisite for efficient strategies to mitigate or even eliminate its harmful effects. However, this requires resources that are often prohibitive, such that the typically-used noise models rely on simplifications that sometimes depart from experimental reality. Here we derive a compact microscopic error model for single-qubit gates that only requires a single experimental input - the noise power spectral density. Our model goes beyond standard depolarizing or Pauli-twirled noise models, explicitly including non-Clifford and non-Markovian contributions to the dynamical error map. We gauge our predictions for experimentally relevant metrics against established characterization techniques run on a trapped-ion quantum computer. In particular, we find that experimental estimates of average gate errors measured through randomized benchmarking and reconstructed via quantum process tomography are tightly lower-bounded by our analytical estimates, while the depolarizing model overestimates the gate error. Our noise modeling including non-Markovian contributions can be readily applied to established frameworks such as dynamical decoupling and dynamically-corrected gates, or to provide more realistic thresholds for quantum error correction., Comment: 32 pages, 8 figures

One of the challenges in robotics is to enable robotic units with the reasoning capability that would be robust enough to execute complex tasks in dynamic environments. Recent advances in LLMs have positioned them as go-to tools for simple reasoning tasks, motivating the pioneering work of Liang et al. [35] that uses an LLM to translate natural language commands into low-level static execution plans for robotic units. Using LLMs inside robotics systems brings their generalization to a new level, enabling zero-shot generalization to new tasks. This paper extends this prior work to dynamic environments. We propose InCoRo, a system that uses a classical robotic feedback loop composed of an LLM controller, a scene understanding unit, and a robot. Our system continuously analyzes the state of the environment and provides adapted execution commands, enabling the robot to adjust to changing environmental conditions and correcting for controller errors. Our system does not require any iterative optimization to learn to accomplish a task as it leverages in-context learning with an off-the-shelf LLM model. Through an extensive validation process involving two standardized industrial robotic units -- SCARA and DELTA types -- we contribute knowledge about these robots, not popular in the community, thereby enriching it. We highlight the generalization capabilities of our system and show that (1) in-context learning in combination with the current state-of-the-art LLMs is an effective way to implement a robotic controller; (2) in static environments, InCoRo surpasses the prior art in terms of the success rate; (3) in dynamic environments, we establish new state-of-the-art for the SCARA and DELTA units, respectively. This research paves the way towards building reliable, efficient, intelligent autonomous systems that adapt to dynamic environments.

Topological quantum error correction is a milestone in the scaling roadmap of quantum computers, which targets circuits with trillions of gates that would allow running quantum algorithms for real-world problems. The square-lattice surface code has become the workhorse to address this challenge, as it poses milder requirements on current devices both in terms of required error rates and small local connectivities. In some platforms, however, the connectivities are kept even lower in order to minimise gate errors at the hardware level, which limits the error correcting codes that can be directly implemented on them. In this work, we make a comparative study of possible strategies to overcome this limitation for the heavy-hexagonal lattice, the architecture of current IBM superconducting quantum computers. We explore two complementary strategies: the search for an efficient embedding of the surface code into the heavy-hexagonal lattice, as well as the use of codes whose connectivity requirements are naturally tailored to this architecture, such as subsystem-type and Floquet codes. Using noise models of increased complexity, we assess the performance of these strategies for IBM devices in terms of their error thresholds and qubit footprints. An optimized SWAP-based embedding of the surface code is found to be the most promising strategy towards a near-term demonstration of quantum error correction advantage.

From a non-central panorama, 3D lines can be recovered by geometric reasoning. However, their sensitivity to noise and the complex geometric modeling required has led these panoramas being very little investigated. In this work we present a novel approach for 3D layout recovery of indoor environments using single non-central panoramas. We obtain the boundaries of the structural lines of the room from a non-central panorama using deep learning and exploit the properties of non-central projection systems in a new geometrical processing to recover the scaled layout. We solve the problem for Manhattan environments, handling occlusions, and also for Atlanta environments in an unified method. The experiments performed improve the state-of-the-art methods for 3D layout recovery from a single panorama. Our approach is the first work using deep learning with non-central panoramas and recovering the scale of single panorama layouts., Comment: arXiv admin note: substantial text overlap with arXiv:2401.17058

In this article we present a visual gyroscope based on equirectangular panoramas. We propose a new pipeline where we take advantage of combining three different methods to obtain a robust and accurate estimation of the attitude of the camera. We quantitatively and qualitatively validate our method on two image sequences taken with a $360^\circ$ dual-fisheye camera mounted on different aerial vehicles.

Convolution kernels are the basic structural component of convolutional neural networks (CNNs). In the last years there has been a growing interest in fisheye cameras for many applications. However, the radially symmetric projection model of these cameras produces high distortions that affect the performance of CNNs, especially when the field of view is very large. In this work, we tackle this problem by proposing a method that leverages the calibration of cameras to deform the convolution kernel accordingly and adapt to the distortion. That way, the receptive field of the convolution is similar to standard convolutions in perspective images, allowing us to take advantage of pre-trained networks in large perspective datasets. We show how, with just a brief fine-tuning stage in a small dataset, we improve the performance of the network for the calibrated fisheye with respect to standard convolutions in depth estimation and semantic segmentation., Comment: Previously presented at BMVC: https://proceedings.bmvc2023.org/721/

Temporal variability in the photometric and spectroscopic properties of protoplanetary disks is common in YSO. However, evidence pointing toward changes in their morphology over short timescales has only been found for a few sources, mainly due to a lack of high cadence observations at mas resolution. We combine GRAVITY multi-epoch observations of HD98922 at mas resolution with PIONIER archival data covering a total time span of 11 years. We interpret the interferometric visibilities and spectral energy distribution with geometrical models and through radiative transfer techniques. We investigated high-spectral-resolution quantities to obtain information on the properties of the HI BrG-line-emitting region. The observations are best fitted by a model of a crescent-like asymmetric dust feature located at 1 au and accounting for 70% of the NIR emission. The feature has an almost constant magnitude and orbits the central star with a possible sub-Keplerian period of 12 months, although a 9 month period is another, albeit less probable, solution. The radiative transfer models show that the emission originates from a small amount of carbon-rich (25%) silicates, or quantum-heated particles located in a low-density region. Among different possible scenarios, we favor hydrodynamical instabilities in the inner disk that can create a large vortex. The high spectral resolution differential phases in the BrG-line show that the hot-gas component is offset from the star and in some cases is located between the star and the crescent feature. The scale of the emission does not favor magnetospheric accretion as a driving mechanism. The scenario of an asymmetric disk wind or a massive accreting substellar or planetary companion is discussed. With this unique observational data set for HD98922, we reveal morphological variability in the innermost 2 au of its disk region., Comment: 45 pages, 20 figures, accepted by and to be published in Astronomy & Astrophysics (A&A)

Omnidirectional images are one of the main sources of information for learning based scene understanding algorithms. However, annotated datasets of omnidirectional images cannot keep the pace of these learning based algorithms development. Among the different panoramas and in contrast to standard central ones, non-central panoramas provide geometrical information in the distortion of the image from which we can retrieve 3D information of the environment [2]. However, due to the lack of commercial non-central devices, up until now there was no dataset of these kinds of panoramas. In this data paper, we present the first dataset of non-central panoramas for indoor scene understanding. The dataset is composed by {\bf 2574} RGB non-central panoramas taken in around 650 different rooms. Each panorama has associated a depth map and annotations to obtain the layout of the room from the image as a structural edge map, list of corners in the image, the 3D corners of the room and the camera pose. The images are taken from photorealistic virtual environments and pixel-wise automatically annotated.

Omnidirectional and 360{\deg} images are becoming widespread in industry and in consumer society, causing omnidirectional computer vision to gain attention. Their wide field of view allows the gathering of a great amount of information about the environment from only an image. However, the distortion of these images requires the development of specific algorithms for their treatment and interpretation. Moreover, a high number of images is essential for the correct training of computer vision algorithms based on learning. In this paper, we present a tool for generating datasets of omnidirectional images with semantic and depth information. These images are synthesized from a set of captures that are acquired in a realistic virtual environment for Unreal Engine 4 through an interface plugin. We gather a variety of well-known projection models such as equirectangular and cylindrical panoramas, different fish-eye lenses, catadioptric systems, and empiric models. Furthermore, we include in our tool photorealistic non-central-projection systems as non-central panoramas and non-central catadioptric systems. As far as we know, this is the first reported tool for generating photorealistic non-central images in the literature. Moreover, since the omnidirectional images are made virtually, we provide pixel-wise information about semantics and depth as well as perfect knowledge of the calibration parameters of the cameras. This allows the creation of ground-truth information with pixel precision for training learning algorithms and testing 3D vision approaches. To validate the proposed tool, different computer vision algorithms are tested as line extractions from dioptric and catadioptric central images, 3D Layout recovery and SLAM using equirectangular panoramas, and 3D reconstruction from non-central panoramas.