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Pedro Ribeiro

Publications and source records attributed to Pedro Ribeiro.

At least 55 records · Page 3Linked to original sources
arXiv2025

Quantum Liouvillian Tomography

Characterization of near-term quantum computing platforms requires the ability to capture and quantify dissipative effects. This is an inherently challenging task, as these effects are multifaceted, spanning a broad spectrum from Markovian to strongly non-Markovian dynamics. We introduce Quantum Liouvillian Tomography (QLT), a protocol to capture and quantify non-Markovian effects in time-continuous quantum dynamics. The protocol leverages gradient-based quantum process tomography to reconstruct dynamical maps and utilizes regression over the derivatives of Pauli string probability distributions to extract the Liouvillian governing the dynamics. We benchmark the protocol using synthetic data and quantify its accuracy in recovering Hamiltonians, jump operators, and dissipation rates for two-qubit systems. Finally, we apply QLT to analyze the evolution of an idling two-qubit system implemented on a superconducting quantum platform to extract characteristics of Hamiltonian and dissipative components and, as a result, detect inherently non-Markovian dynamics. Our work introduces the first protocol capable of retrieving generators of generic open quantum evolution from experimental data, thus enabling more precise characterization of many-body non-Markovian effects in near-term quantum computing platforms.

quant-ph↗
arXiv2025

Symmetry classes of classical stochastic processes

We perform a systematic symmetry classification of the Markov generators of classical stochastic processes. Our classification scheme is based on the action of involutive symmetry transformations of a real Markov generator, extending the Bernard-LeClair scheme to the arena of classical stochastic processes and leading to a set of up to fifteen allowed symmetry classes. We construct families of solutions of arbitrary matrix dimensions for five of these classes with a simple physical interpretation of particles hopping on multipartite graphs. In the remaining classes, such a simple construction is prevented by the positivity of entries of the generator particular to classical stochastic processes, which imposes a further requirement beyond the usual symmetry classification constraints. We partially overcome this difficulty by resorting to a stochastic optimization algorithm, finding specific examples of generators of small matrix dimensions in six further classes, leaving the existence of the final four allowed classes an open problem. Our symmetry-based results unveil new possibilities in the dynamics of classical stochastic processes: Kramers degeneracy of eigenvalue pairs, dihedral symmetry of the spectra of Markov generators, and time reversal properties of stochastic trajectories and correlation functions.

cond-mat.stat-mech↗
arXiv2025

Disorder-induced instability of a Weyl nodal loop semimetal towards a diffusive topological metal with protected multifractal surface states

Weyl nodal loop semimetals are gapless topological phases that, unlike their insulator counterparts, may be unstable to small perturbations that respect their topology-protecting symmetries. Here, we analyze a clean system perturbed by chiral off-diagonal disorder using numerically exact methods. We establish that the ballistic semimetallic phase is unstable towards the formation of an unconventional topological diffusive metal hosting topological multifractal surface states. Although, as in the clean case, surface states are exponentially localized along the direction perpendicular to the nodal loop, disorder induces a multifractal structure in the remaining directions. Surprisingly, the number of these states also increases with a small amount of disorder. Eventually, as disorder is further increased, the number of surface states starts decreasing.

cond-mat.dis-nn↗
arXiv2024

Dynamics of spin spirals in a voltage biased 1D conductor

We analyze the fate of spiral order in a one-dimensional system of localized magnetic moments coupled to itinerant electrons under a voltage bias. Within an adiabatic approximation for the dynamics of the localized spins, and in the presence of a phenomenological damping term, we demonstrate the occurrence of various dynamical regimes: At small bias a rigidly rotating non-coplanar magnetic structure is realized which, by increasing the applied voltage, transitions to a quasi-periodic and, finally, fully chaotic evolution. These phases can be identified by transport measurements. In particular, the rigidly rotating state results in an average transfer of spin polarization. We analyze in detail the dependence of the rotation axis and frequency on system's parameters and show that the spin dynamics slows down in the thermodynamic limit, when a static conical state persists to arbitrarily long times. Our results suggest the possibility of discovering non-trivial dynamics in other symmetry-broken quantum states under bias.

cond-mat.mes-hall↗
arXiv2024

Model Checking and Verification of Synchronisation Properties of Cobot Welding

This paper describes use of model checking to verify synchronisation properties of an industrial welding system consisting of a cobot arm and an external turntable. The robots must move synchronously, but sometimes get out of synchronisation, giving rise to unsatisfactory weld qualities in problem areas, such as around corners. These mistakes are costly, since time is lost both in the robotic welding and in manual repairs needed to improve the weld. Verification of the synchronisation properties has shown that they are fulfilled as long as assumptions of correctness made about parts outside the scope of the model hold, indicating limitations in the hardware. These results have indicated the source of the problem, and motivated a re-calibration of the real-life system. This has drastically improved the welding results, and is a demonstration of how formal methods can be useful in an industrial setting.

cs.RO↗
arXiv2024

Fractal Quasicondensation in One Dimension

We unveil a novel mechanism for quasicondensation of hard-core bosons in the presence of quasiperiodicity-induced multifractal single-particle states. The new critical state, here dubbed fractal quasicondensate, is characterized by natural orbitals with multifractal properties and by an occupancy of the lowest natural orbital, λ0 ~ Lγ, which grows with system size but with a nonuniversal scaling exponent, γ < 1/2. In contrast to fractal quasicondensates obtained when the chemical potential lies in a region of multifractal single-particle states, placing the chemical potential in regions of localized or delocalized states yields, respectively, no condensation or the usual 1D quasicondensation with γ = 1/2. Our findings are established by studying one-dimensional hardcore bosons subjected to various quasiperiodic potentials, including the well-known Aubry-Andre model, employing a mapping to non-interacting fermionics that allows for numerically exact results. We discuss how to test our findings in state-of-the-art ultracold atom experiments.

cond-mat.quant-gas↗
arXiv2024

Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs

Continuous-time dynamic graphs (CTDGs) are essential for modeling interconnected, evolving systems. Traditional methods for extracting knowledge from these graphs often depend on feature engineering or deep learning. Feature engineering is limited by the manual and time-intensive nature of crafting features, while deep learning approaches suffer from high inference latency, making them impractical for real-time applications. This paper introduces Deep-Graph-Sprints (DGS), a novel deep learning architecture designed for efficient representation learning on CTDGs with low-latency inference requirements. We benchmark DGS against state-of-the-art (SOTA) feature engineering and graph neural network methods using five diverse datasets. The results indicate that DGS achieves competitive performance while inference speed improves between 4x and 12x compared to other deep learning approaches on our benchmark datasets. Our method effectively bridges the gap between deep representation learning and low-latency application requirements for CTDGs.

cs.LG↗
arXiv2024

An analogue of a formula of Popov

Let $r_{k}(n)$ denote the number of representations of the positive integer $n$ as the sum of $k$ squares. We prove a new summation formula involving $r_{k}(n)$ and the Bessel functions of the first kind, which constitutes an analogue of a result due to the Russian mathematician A. I. Popov.

math.NT↗
arXiv2024

Poisson summation formula and Index Transforms

Following the work of the second author, a class of summation formulas attached to index transforms is studied in this paper. Our primary results concern summation and integral formulas with respect to the second index of the Whittaker function $W_{μ,ν}(z)$.

math.CA↗
arXiv2024

On the Energy Consumption of Rotary Wing and Fixed Wing UAVs in Flying Networks

Unmanned Aerial Vehicles (UAVs) are increasingly used to enable wireless communications. Due to their characteristics, such as the ability to hover and carry cargo, UAVs can serve as communications nodes, including Wi-Fi Access Points and Cellular Base Stations. In previous work, we proposed the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which focuses on the energy-efficient placement of multiple UAVs acting as Flying Access Points (FAPs). Additionally, we developed the Multi-UAV Energy Consumption (MUAVE) simulator to evaluate the UAV energy consumption, specifically when using the SUPPLY algorithm. However, MUAVE was initially designed to compute the energy consumption for rotary-wing UAVs only. In this paper, we propose eMUAVE, an enhanced version of the MUAVE simulator that allows the evaluation of the energy consumption for both rotary-wing and fixed-wing UAVs. Our energy consumption evaluation using eMUAVE considers reference and random networking scenarios. The results show that fixed-wing UAVs can be employed in the majority of networking scenarios. However, rotary-wing UAVs are typically more energy-efficient than fixed-wing UAVs when following the trajectories defined by SUPPLY.

cs.NI↗
arXiv2024

Voltage-Driven Breakdown of Electronic Order

The non-thermal breakdown of a Mott insulator has been a topic of great theoretical and experimental interest with technological relevance. Recent experiments have found a sharp non-equilibrium insulator-to-metal transition that is accompanied by hysteresis, a negative differential conductance and lattice deformations. However, a thorough understanding of the underlying breakdown mechanism is still lacking. Here, we examine a scenario in which the breakdown is induced by chemical pressure in a paradigmatic model of interacting spinless fermions on a chain coupled to metallic reservoirs (leads). For the Markovian regime, at infinite bias, we qualitatively reproduce several established results. Beyond infinite bias, we find a rich phase diagram where the nature of the breakdown depends on the coupling strength as the bias voltage is tuned up, yielding different current-carrying non-equilibrium phases. For weak to intermediate coupling, we find a conducting CDW phase with a bias-dependent ordering wave vector. At large interaction strength, the breakdown connects the system to a charge-separated insulating phase. We find instances of hysteretic behavior, sharp current onset and negative differential conductance. Our results can help to shed light on recent experimental findings that address current-induced Mott breakdown.

cond-mat.str-el↗
arXiv2024

Tricriticality in 4D U(1) Lattice Gauge Theory

The 4D compact U(1) gauge theory has a well-established phase transition between a confining and a Coulomb phase. In this paper, we revisit this model using state-of-the-art Monte Carlo simulations on anisotropic lattices. We map out the coupling-temperature phase diagram, and determine the location of the tricritical point, $T/K_0 \simeq 0.19$, below which the first-order transition is observed. We find the critical exponents of the high-temperature second-order transition to be compatible with those of the 3-dimensional $O(2)$ model. Our results at higher temperatures can be compared with literature results and are consistent with them. Surprisingly, below $T/K_0 \simeq 0.05$ we find strong indications of a second tricritical point where the first-order transition becomes continuous. These results suggest an unexpected second-order phase transition extending down to zero temperature, contrary to the prevailing consensus. If confirmed, these findings reopen the question of the detailed characterization of the transition including a suitable field theory description.

hep-lat↗
arXiv2024

SUPPLY: Sustainable multi-UAV Performance-aware Placement Algorithm for Flying Networks

Unmanned Aerial Vehicles (UAVs) are used for a wide range of applications. Due to characteristics such as the ability to hover and carry cargo on-board, rotary-wing UAVs have been considered suitable platforms for carrying communications nodes, including Wi-Fi Access Points and cellular Base Stations. This gave rise to the concept of Flying Networks (FNs), now making part of the so-called Non-Terrestrial Networks (NTNs) defined in 3GPP. In scenarios where the deployment of terrestrial networks is not feasible, the use of FNs has emerged as a solution to provide wireless connectivity. However, the management of the communications resources in FNs imposes significant challenges, especially regarding the positioning of the UAVs so that the Quality of Service (QoS) offered to the Ground Users (GUs) and devices is maximized. Moreover, unlike terrestrial networks that are directly connected to the power grid, UAVs typically rely on on-board batteries that need to be recharged. In order to maximize the UAVs' flying time, the energy consumed by the UAVs needs to be minimized. When it comes to multi-UAV placement, most state-of-the-art solutions focus on maximizing the coverage area and assume that the UAVs keep hovering in a fixed position while serving GUs. Also, they do not address the energy-aware multi-UAV placement problem in networking scenarios where the GUs may have different QoS requirements and may not be uniformly distributed across the area of interest. In this work, we propose the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm. SUPPLY defines the energy and performance-aware positioning of multiple UAVs in an FN. To accomplish this, SUPPLY defines trajectories that minimize UAVs' energy consumption, while ensuring the targeted QoS levels. The obtained results show up to 25% energy consumption reduction with minimal impact on throughput and delay.

cs.NI↗
arXiv2024

From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs

Many real-world datasets have an underlying dynamic graph structure, where entities and their interactions evolve over time. Machine learning models should consider these dynamics in order to harness their full potential in downstream tasks. Previous approaches for graph representation learning have focused on either sampling k-hop neighborhoods, akin to breadth-first search, or random walks, akin to depth-first search. However, these methods are computationally expensive and unsuitable for real-time, low-latency inference on dynamic graphs. To overcome these limitations, we propose graph-sprints a general purpose feature extraction framework for continuous-time-dynamic-graphs (CTDGs) that has low latency and is competitive with state-of-the-art, higher latency models. To achieve this, a streaming, low latency approximation to the random-walk based features is proposed. In our framework, time-aware node embeddings summarizing multi-hop information are computed using only single-hop operations on the incoming edges. We evaluate our proposed approach on three open-source datasets and two in-house datasets, and compare with three state-of-the-art algorithms (TGN-attn, TGN-ID, Jodie). We demonstrate that our graph-sprints features, combined with a machine learning classifier, achieve competitive performance (outperforming all baselines for the node classification tasks in five datasets). Simultaneously, graph-sprints significantly reduce inference latencies, achieving close to an order of magnitude speed-up in our experimental setting.

cs.LG↗
arXiv2023

Multilayer Quantile Graph for Multivariate Time Series Analysis and Dimensionality Reduction

In recent years, there has been a surge in the prevalence of high- and multi-dimensional temporal data across various scientific disciplines. These datasets are characterized by their vast size and challenging potential for analysis. Such data typically exhibit serial and cross-dependency and possess high dimensionality, thereby introducing additional complexities to conventional time series analysis methods. To address these challenges, a recent and complementary approach has emerged, known as network-based analysis methods for multivariate time series. In univariate settings, Quantile Graphs have been employed to capture temporal transition properties and reduce data dimensionality by mapping observations to a smaller set of sample quantiles. To confront the increasingly prominent issue of high dimensionality, we propose an extension of Quantile Graphs into a multivariate variant, which we term "Multilayer Quantile Graphs". In this innovative mapping, each time series is transformed into a Quantile Graph, and inter-layer connections are established to link contemporaneous quantiles of pairwise series. This enables the analysis of dynamic transitions across multiple dimensions. In this study, we demonstrate the effectiveness of this new mapping using a synthetic multivariate time series dataset. We delve into the resulting network's topological structures, extract network features, and employ these features for original dataset analysis. Furthermore, we compare our results with a recent method from the literature. The resulting multilayer network offers a significant reduction in the dimensionality of the original data while capturing serial and cross-dimensional transitions. This approach facilitates the characterization and analysis of large multivariate time series datasets through network analysis techniques.

cs.SI↗
arXiv2023

On the number of zeros of $L-$functions attached to cusp forms of half-integral weight

Meher et al. [Proc. Amer. Math. Soc. 147 (2019)] have recently established that $L-$functions attached to certain cusp forms of half-integral weight have infinitely many zeros on the critical line. Kim [J. Numb. Th. 253 (2023)] obtained analogous results for $L-$functions attached to cusp forms twisted by an additive character $e\left(\frac{p}{q}n\right)$, $\frac{p}{q}\in\mathbb{Q}$. We extend the results of these authors by giving a lower bound for the number of such zeros. We start by developing a variant of a method of de la Valée Poussin which seems to have interest as it avoids the evaluation of exponential sums. We finish the paper with an improvement of our first estimate by using Lekkerkerker's variant of the Hardy-Littlewood method.

math.NT↗