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This study investigates the conditions under which gravitational waves (GWs) are emitted during the merger of hot white dwarfs (WDs) in a binary system. Traditionally, these systems consist of two low-mass stars or a more massive WD paired with a less massive companion. In addition, recent work has investigated the possibility that double white dwarf (DWD) mergers are possibly the leading formation channel of massive, rapidly rotating, high-field magnetic WDs , particularly SDSS J221141.80 + 113604.4 (hereafter J2211+1136) and ZTF J190132.9 + 145808.7 (hereafter J1901 + 14588). Motivated by these findings and the Laser Interferometer Space Antenna (LISA) prospects, this study aims to calculate the tidal Love number, the dimensionless tidal deformability, as well as the frequency and amplitude of GWs of hot WDs. The results indicate that the tidal deformability is more pronounced in stars with higher central temperatures and lower masses, which would lead to reduced emission of GWs. In contrast, more massive stars exhibit less deformability, making them prime candidates for generating stronger GWs. Additionally, the analysis of frequency and amplitude reveals that the frequencies of high-mass binaries are smaller and evolve more rapidly, reaching a limit that aligns with the operational detection capabilities of LISA during its initial phase.

The computational generation of poems is a complex task, which involves several sound, prosodic and rhythmic resources. In this work we present PROPOE 2, with the extension of structural and rhythmic possibilities compared to the original system, generating poems from metered sentences extracted from the prose of Brazilian literature, with multiple rhythmic assembly criteria. These advances allow for a more coherent exploration of rhythms and sound effects for the poem. Results of poems generated by the system are demonstrated, with variations in parameters to exemplify generation and evaluation using various criteria. A gera\c{c}\~ao computacional de poemas \'e uma tarefa complexa, que envolve diversos recursos sonoros, pros\'odicos e r\'itmicos. Neste trabalho apresentamos PROPOE 2, com a amplia\c{c}\~ao de possibilidades estruturais e r\'itmicas em rela\c{c}\~ao ao sistema original, gerando poemas a partir de senten\c{c}as metrificadas extra\'idas da prosa da literatura brasileira, com m\'ultiplos crit\'erios r\'itmicos de montagem. Esses avan\c{c}os permitem uma explora\c{c}\~ao mais coerente de ritmos e efeitos sonoros para o poema. Resultados de poemas gerados pelo sistema s\~ao demonstrados, com varia\c{c}\~oes de par\^ametros para exemplificar a gera\c{c}\~ao e a avalia\c{c}\~ao pelos variados crit\'erios., Comment: in Portuguese language

Gravitational wave approximants are widely used tools in gravitational-wave astronomy. They allow for dense coverage of the parameter space of binary black hole (BBH) mergers for purposes of parameter inference, or, more generally, match filtering tasks, while avoiding the computationally expensive full evolution of numerical relativity simulations. However, this comes at a slight cost in terms of accuracy when compared to numerical relativity waveforms, depending on the approach. One way to minimize this is by constructing so-called~\textit{surrogate models} which, instead of using approximate physics or phenomenological formulae, rather interpolate within the space of numerical relativity waveforms. In this work, we introduce~\texttt{NRSurNN3dq4}, a surrogate model for non-precessing BBH merger waveforms powered by neural networks. By relying on the power of deep learning, this approximant is remarkably fast and competitively accurate, as it can generate millions of waveforms in a tenth of a second, while mismatches with numerical relativity waveforms are restrained below $10^{-3}$. We implement this approximant within the~\textsc{bilby} framework for gravitational-wave parameter inference, and show that it it is suitable for parameter estimation tasks.

In recent years, the fifth-generation (5G) mobile network has been developed worldwide to remarkably improve network performance and spectral efficiency. Very recently, reconfigurable intelligent surfaces (RISs) technology has emerged as an innovative solution for controlling the propagation medium of the forthcoming sixth-generation (6G) networks. Specifically, RIS takes advantage of the reflected rays on the propagation environment to redirect them to a desired target, improving wireless coverage. To further improve RIS performance, an interesting technique called synchronized transmission with advanced reconfigurable surfaces (STARS) has appeared to allow simultaneous transmission and reflection of intelligent omni-surfaces. With that in mind, this paper introduces an enhanced strategy for the network planning of 5G and beyond (B5G) mobile networks in dense urban scenarios focused on the city of Quito-Ecuador. The efficacy of RIS and its cutting-edge STARS concept is emphasized, providing useful insights into coverage, quality, and throughput results. In particular, this work considers the 3.5/28 GHz frequency bands, optimizing the radio network and anticipating their applicability in B5G networks. Finally, simulation results are also shown, which allow the identification of the benefits of STAR RIS in terms of coverage, signal quality, and data performance., Comment: 6 pages, 12 figures, 2024 IEEE 29th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), presented in Session 11: Federation of 6G Infrastructures and Experimentation Facilities in a Network of Networks, October 22th - 2024

Two-dimensional electronic spectroscopy (2DES) is a powerful tool for exploring quantum effects in energy transport within photosynthetic systems and investigating novel material properties. However, simulating the dynamics of these experiments poses significant challenges for classical computers due to the large system sizes, long timescales and numerous experiment repetitions involved. This paper introduces the probe qubit protocol (PQP)-for quantum simulation of 2DES on quantum devices-addressing these challenges. The PQP offers several enhancements over standard methods, notably reducing computational resources, by requiring only a single-qubit measurement per circuit run and achieving Heisenberg scaling in detection frequency resolution, without the need to apply expensive controlled evolution operators in the quantum circuit. The implementation of the PQP protocol requires only one additional ancilla qubit, the probe qubit, with one-to-all connectivity and two-qubit interactions between each system and probe qubits. We evaluate the computational resources necessary for this protocol in detail, demonstrating its function as a dynamic frequency-filtering method through numerical simulations. We find that simulations of the PQP on classical and quantum computers enable a reduction on the number of measurements, i.e. simulation runtime, and memory savings of several orders of magnitude relatively to standard quantum simulation protocols of 2DES. The paper discusses the applicability of the PQP on near-term quantum devices and highlights potential applications where this spectroscopy simulation protocol could provide significant speedups over standard approaches such as the quantum simulation of 2DES applied to the Fenna-Matthews-Olson (FMO) complex in green sulphur bacteria.

We present a non-trivial metric tensor field on the space of 2-by-2 real-valued, symmetric matrices whose Levi-Civita connection renders frames of eigenvectors parallel. This results in fundamental reimagining of the space of symmetric matrices as a curved manifold (rather than a flat vector space) and reduces the computation of eigenvectors of one-parameter-families of matrices to a single computation of eigenvectors at an initial point, while the rest are obtained by the parallel transport ODE. Our work has important applications to vibrations of physical systems whose topology is directly explained by the non-trivial holonomy of the spaces of symmetric matrices.

The celebrated canonical Ramsey theorem of Erd\H{o}s and Rado implies that for $2\leq k\in \mathbb{N}$, any colouring of the edges of $K_n$ with $n$ sufficiently large gives a copy of $C_{2k}$ which has one of three canonical colour patterns: monochromatic, rainbow or lexicographic. In this paper we show that if $p=\omega(n^{-1+1/(2k-1)}\log n)$, then ${\mathbf{G}}(n,p)$ will asymptotically almost surely also have the property that any colouring of its edges induces canonical copies of $C_{2k}$. This determines the threshold for the canonical Ramsey property with respect to even cycles, up to a $\log$ factor., Comment: 24 pages + 4 pages of appendix, 1 figure

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research., Comment: Updating author information, the submission remains largely unchanged. 98 pages total

By considering realistic equations of state (EoSs) to describe the ordinary matter of the stellar crust, in this study, we explore the effect of a dark energy core, made of Chaplygin Dark Fluid (CDF), on neutron stars (NSs). To accomplish this purpose, we solve the stellar structure equations and investigate the impact of the CDF parameters on the several macroscopic properties of NSs such as mass-radius ($M-R$) relation, and tidal deformabilities of a single star and of a binary system, the latter being of great importance when analyzing gravitational-wave signals coming from the merger of such compact objects. We also present an analysis of the radial oscillation modes for the rapid phase transition, with the aim of distinguishing regions consisting of dynamically stable stars from those of unstable ones. Specifically, our outcomes reveal that an increase in the energy density jump (controlled by a parameter $\alpha$) leads to an increase in the radial stability of the NS with a CDF core. Furthermore, our theoretical results are consistent with the observational $M-R$ measurements of millisecond pulsars from NICER data and tidal deformability constraints from the GW170817 event., Comment: 12 pages, 7 figures and 3 tables

This paper analyses the centralized fusion linear estimation problem in multi-sensor systems with multiple packet dropouts and correlated noises. Packet dropouts are modeled by independent Bernoulli distributed random variables. This problem is addressed in the tessarine domain under conditions of T1 and T2-properness, which entails a reduction in the dimension of the problem and, consequently, computational savings. The methodology proposed enables us to provide an optimal (in the least-mean-squares sense) linear fusion filtering algorithm for estimating the tessarine state with a lower computational cost than the conventional one devised in the real field. Simulation results illustrate the performance and advantages of the solution proposed in different settings.

Incorporating higher curvature terms into gravity theories modifies the classical field equations, potentially leading to theoretical issues like Shapiro time advancements that violate the Camanho, Edelstein, Maldacena, and Zhiboedov (CEMZ) causality criterion. We explore this criterion within the context of Generalised Quadratic Gravity (GQG), a higher curvature theory with a distinct graviton three-point coupling from General Relativity (GR). By constructing an exact shock wave solution of GQG and calculating the Shapiro time shift for a massless probe graviton, we demonstrate that it can remain strictly positive within a classically allowed parameter space of couplings, ensuring the theory's adherence to the CEMZ criterion. This finding indicates that GQG can offer a causal extension beyond GR, paving the way for further exploration into the consistency of classical higher curvature gravity theories., Comment: 5 pages, 2 figures

High-Energy Physics experiments are facing a multi-fold data increase with every new iteration. This is certainly the case for the upcoming High-Luminosity LHC upgrade. Such increased data processing requirements forces revisions to almost every step of the data processing pipeline. One such step in need of an overhaul is the task of particle track reconstruction, a.k.a., tracking. A Machine Learning-assisted solution is expected to provide significant improvements, since the most time-consuming step in tracking is the assignment of hits to particles or track candidates. This is the topic of this paper. We take inspiration from large language models. As such, we consider two approaches: the prediction of the next word in a sentence (next hit point in a track), as well as the one-shot prediction of all hits within an event. In an extensive design effort, we have experimented with three models based on the Transformer architecture and one model based on the U-Net architecture, performing track association predictions for collision event hit points. In our evaluation, we consider a spectrum of simple to complex representations of the problem, eliminating designs with lower metrics early on. We report extensive results, covering both prediction accuracy (score) and computational performance. We have made use of the REDVID simulation framework, as well as reductions applied to the TrackML data set, to compose five data sets from simple to complex, for our experiments. The results highlight distinct advantages among different designs in terms of prediction accuracy and computational performance, demonstrating the efficiency of our methodology. Most importantly, the results show the viability of a one-shot encoder-classifier based Transformer solution as a practical approach for the task of tracking.

Quantum machine learning, as an extension of classical machine learning that harnesses quantum mechanics, facilitates effiient learning from data encoded in quantum states. Training a quantum neural network typically demands a substantial labeled training set for supervised learning. Human annotators, often experts, provide labels for samples through additional experiments, adding to the training cost. To mitigate this expense, there is a quest for methods that maintain model performance over fully labeled datasets while requiring fewer labeled samples in practice, thereby extending few-shot learning to the quantum realm. Quantum active learning estimates the uncertainty of quantum data to select the most informative samples from a pool for labeling. Consequently, a QML model is supposed to accumulate maximal knowledge as the training set comprises labeled samples selected via sampling strategies. Notably, the QML models trained within the QAL framework are not restricted to specific types, enabling performance enhancement from the model architecture's perspective towards few-shot learning. Recognizing symmetry as a fundamental concept in physics ubiquitous across various domains, we leverage the symmetry inherent in quantum states induced by the embedding of classical data for model design. We employ an equivariant QNN capable of generalizing from fewer data with geometric priors. We benchmark the performance of QAL on two classification problems, observing both positive and negative results. QAL effectively trains the model, achieving performance comparable to that on fully labeled datasets by labeling less than 7\% of the samples in the pool with unbiased sampling behavior. Furthermore, we elucidate the negative result of QAL being overtaken by random sampling baseline through miscellaneous numerical experiments. (character count limit, see the main text), Comment: 12 pages, 10 figures

The interplay of spin and lattice fluctuations in two-dimensional magnets without inversion symmetry is investigated. We find a general form for the magnetoelastic coupling between magnons and existing chiral phonons based on the symmetries of the crystalline lattice. We show that in hexagonal lattices, the coupling of magnons and chiral phonons derives from an anisotropic exchange spin model containing topological phases of magnons. Using the Heisenberg-Kitaev-$\Gamma$ model, we show how magnon-polaron edge states with circular polarization arise from this interaction. Our findings exploit the polarization degrees of freedom in spin-lattice systems, thus setting the ground for the transfer of angular momentum between chiral phonons and magnons., Comment: 7 pages, 2 figures

Track reconstruction is a vital aspect of High-Energy Physics (HEP) and plays a critical role in major experiments. In this study, we delve into unexplored avenues for particle track reconstruction and hit clustering. Firstly, we enhance the algorithmic design effort by utilising a simplified simulator (REDVID) to generate training data that is specifically composed for simplicity. We demonstrate the effectiveness of this data in guiding the development of optimal network architectures. Additionally, we investigate the application of image segmentation networks for this task, exploring their potential for accurate track reconstruction. Moreover, we approach the task from a different perspective by treating it as a hit sequence to track sequence translation problem. Specifically, we explore the utilisation of Transformer architectures for tracking purposes. Our preliminary findings are covered in detail. By considering this novel approach, we aim to uncover new insights and potential advancements in track reconstruction. This research sheds light on previously unexplored methods and provides valuable insights for the field of particle track reconstruction and hit clustering in HEP.

We propose digital-analog quantum kernels for enhancing the detection of complex features in the classification of images. We consider multipartite-entangled analog blocks, stemming from native Ising interactions in neutral-atom quantum processors, and individual operations as digital steps to implement the protocol. To further improving the detection of complex features, we apply multiple quantum kernels by varying the qubit connectivity according to the hardware constraints. An architecture that combines non-trainable quantum kernels and standard convolutional neural networks is used to classify realistic medical images, from breast cancer and pneumonia diseases, with a significantly reduced number of parameters. Despite this fact, the model exhibits better performance than its classical counterparts and achieves comparable metrics according to public benchmarks. These findings demonstrate the relevance of digital-analog encoding, paving the way for surpassing classical models in image recognition approaching us to quantum-advantage regimes., Comment: 7 pages, 3 figures

In this work we study, within the framework of Cowling approximation, the effect of the electric charge on the gravitational wave frequency of fluid oscillation modes of strange quark stars. For this purpose, the dense matter of the stellar fluid is described by the MIT bag model equation of state (EoS), while for the electric charge profile, we consider that the electric charge density is proportional to the energy density. We find that the gravitational wave frequencies change with the increment of electric charge; these effects are more noticeable at higher total mass values. We obtain that the $f$-mode is very sensitive to the change in the electric charge of the star. Furthermore, in the case of the $p_1$ mode, the effect of the electric charge is not very significant. Our results reveal that the study of the fundamental pulsation mode of an electrically charged compact star is very important in distinguishing whether compact stars could contain electric charge. We also employ another electric charge distribution profile that follows a power law, and it is found that the $f$-mode change is more noticeable than the $p_1$-mode when the electric charge is incremented., Comment: 11 pages, 6 figures and 1 table

In the last two decades, much effort has been dedicated to studying curves and surfaces according to their angle with a given direction. However, most findings were obtained using a case-by-case approach, and it is often unclear what are consequences of the specificities of the ambient manifold and what could be generic. In this work, we propose a theoretical framework to unify parts of these findings. We study curves and surfaces by prescribing the angle they make with a parallel transported vector field. We show that the characterization of Euclidean helices in terms of their curvature and torsion is also valid in any Riemannian manifold. Among other properties, we prove that surfaces making a constant angle with a parallel transported direction are extrinsically flat ruled surfaces. We also investigate the relation between their geodesics and the so-called slant helices; we prove that surfaces of constant angle are the rectifying surface of a slant helix, i.e., the ruled surface with rulings given by the Darboux vector field of the directrix. We characterize rectifying surfaces of constant angle; in other words, when their geodesics are slant helices. As a corollary, we show that if every geodesic of a surface of constant angle is a slant helix, then the ambient manifold is flat. Finally, we characterize surfaces in the product of a Riemannian surface with the real line making a constant angle with the vertical real direction., Comment: 27 pages. Keywords: Helix, Slant helix, Surface of constant angle, Rectifying surface

We study three different strategies of vaccination in a SEIRS (Susceptible--Exposed--Infected--Recovered--Susceptible) seasonal forced model, which are: ($i$) continuous vaccination; ($ii$) periodic short time localized vaccination and ($iii$) periodic pulsed width campaign. Considering the first strategy, we obtain an expression for the basic reproduction number and infer a minimum vaccination rate necessary to ensure the stability of the disease-free equilibrium (DFE) solution. In the second strategy, the short duration pulses are added to a constant baseline vaccination rate. The pulse is applied according to the seasonal forcing phases. The best outcome is obtained by locating the intensive immunization at inflection of the transmissivity curve. There, a vaccination rate of $44.4\%$ of susceptible individuals is enough to ensure DFE. For the third vaccination proposal, additionally to the amplitude, the pulses have a prolonged time width. We obtain a non-linear relationship between vaccination rates and the duration of the campaign. Our simulations show that the baseline rates, as well as the pulse duration, can substantially improve the vaccination campaign effectiveness. These findings are in agreement with our analytical expression. We show a relationship between the vaccination parameters and the accumulated number of infected individuals, over the years and show the relevance of the immunisation campaign annual reaching for controlling the infection spreading. Regarding the dynamical behaviour of the model, our simulations shows that chaotic and periodic solutions, as well as bi-stable regions, depend on the vaccination parameters range.

Noise in quantum devices is generally considered detrimental to computational accuracy. However, the recent proposal of noise-assisted simulation has demonstrated that noise can be an asset in digital quantum simulations of open systems on Noisy Intermediate-Scale Quantum (NISQ) devices. In this context, we introduce an optimized decoherence rate control scheme that can significantly reduce computational requirements by multiple orders of magnitude, in comparison to the original noise-assisted simulation. We further extend this approach to encompass Lindblad equations with time-dependent coefficients, using only quantum error characterization and mitigation techniques. This extension allows for the perturbative simulation of non-Markovian dynamics on NISQ devices, eliminating the need for ancilla qubits or mid-circuit measurements. Our contributions are validated through numerical experiments on an emulated IBMQ device. Overall, our work offers valuable optimizations that bring current quantum processors closer to effectively simulating realistic open systems.

Conventional and recently developed approaches for estimating turbulent scalar fluxes under stable conditions are evaluated. The focus is on methods that do not require fast scalar sensors such as the relaxed eddy accumulation (REA) approach, the disjunct eddy-covariance (DEC) approach, and a novel mixing length parametrization labelled as A22. Using high-frequency measurements collected from two contrasting sites (Utqiagvik, Alaska and Wendell, Idaho "during winter"), it is shown that the REA and A22 models outperform the conventional Monin-Obukhov Similarity Theory (MOST) utilized in Earth System Models. With slow trace gas sensors used in disjunct eddy-covariance (DEC) approaches and the more complex signal filtering associated with REA devices (here simulated using filtered signals from fast-response sensors), A22 outperforms REA and DEC in predicting the observed unfiltered (total) eddy-covariance (EC) fluxes. However, REA and DEC can still capture the observed filtered EC fluxes computed with the filtered scalar signal. This finding motivates the development of a correction, blending the REA and DEC methods, for the underestimated net averaged fluxes to incorporate the effect of sensor filtering. The only needed parameter for this correction is the mean velocity at the instrument height, a surrogate of the advective timescale., Comment: 14 pages, 8 figures, 2 tables

In the realm of quantum control, reinforcement learning, a prominent branch of machine learning, emerges as a competitive candidate for computer-assisted optimal design for experiments. This study investigates the extent to which guidance from human experts is necessary for the effective implementation of reinforcement learning in designing quantum control protocols. Specifically, we focus on the engineering of a robust two-qubit gate within a nuclear magnetic resonance system, utilizing a combination of analytical solutions as prior knowledge and techniques from the field of computer science. Through extensive benchmarking of different models, we identify dropout, a widely-used method for mitigating overfitting in machine learning, as an especially robust approach. Our findings demonstrate the potential of incorporating computer science concepts to propel the development of advanced quantum technologies., Comment: 11 pages, 4 figures

We present an approach to achieve zero modes in lattice models that do not rely on any symmetry or topology of the bulk, which are robust against disorder in the bulk of any type and strength. Such symmetry-free zero modes (SFZMs) are formed by attaching a single site or small cluster with zero mode(s) to the bulk, which serves as the "nucleus" that expands to the entire lattice. We identify the requirements on the couplings between this boundary and the bulk, which reveals that this approach is intrinsically non-Hermitian. We then provide several examples with either an arbitrary or structured bulk, forming spectrally embedded zero modes in the bulk continuum, midgap zero modes, and even restoring the "zeroness" of coupling or disorder-shifted topological corner states. Focusing on viable realizations using photonic lattices, we show that the resulting SFZM can be observed as the single lasing mode when optical gain is applied to the boundary., Comment: 5 pages, 5 figures

We study the emergence of loose Hamilton cycles in subgraphs of random hypergraphs. Our main result states that the minimum $d$-degree threshold for loose Hamiltonicity relative to the random $k$-uniform hypergraph $H_k(n,p)$ coincides with its dense analogue whenever $p \geq n^{- (k-1)/2+o(1)}$. The value of $p$ is approximately tight for $d>(k+1)/2$. This is particularly interesting because the dense threshold itself is not known beyond the cases when $d \geq k-2$., Comment: 33 pages, 3 figures

In recent years, various Lattice Boltzmann models for electrodynamics have been developed as alternatives to classical methods such as Finite Difference Time Domain (FDTD) and Finite Element Methods (FEM). However, there has been a lack of systematic comparisons between these models. This paper addresses this gap by comparing two specific Lattice Boltzmann models, published by Mendoza and Mu\~noz (MM), and Hauser and Verhey (HV), respectively. To compare the models, we utilize time and memory as indicators, considering the same achieved error, in four standard tests: a dielectric pulse traveling through two interfaces, the skin effect, the Hertz dipole, and a dielectric pulse traveling through several interfaces. The results indicate that both methods accurately simulate the tests and exhibit convergence as the mesh is refined. However, the MM method outperforms the HV method regarding time, while its memory efficiency was lower. The modified Hauser-Verhey model demonstrates itself to be a promising alternative to the Mendoza-Mu\~noz model. These findings contribute to the ongoing development and optimization of numerical methods for electromagnetics simulations., Comment: 9 pages, 10 figures

We propose machine learning (ML) methods to characterize the memristive properties of single and coupled quantum memristors. We show that maximizing the memristivity leads to large values in the degree of entanglement of two quantum memristors, unveiling the close relationship between quantum correlations and memory. Our results strengthen the possibility of using quantum memristors as key components of neuromorphic quantum computing.

We introduce a novel methodology that leverages the strength of Physics-Informed Neural Networks (PINNs) to address the counterdiabatic (CD) protocol in the optimization of quantum circuits comprised of systems with $N_{Q}$ qubits. The primary objective is to utilize physics-inspired deep learning techniques to accurately solve the time evolution of the different physical observables within the quantum system. To accomplish this objective, we embed the necessary physical information into an underlying neural network to effectively tackle the problem. In particular, we impose the hermiticity condition on all physical observables and make use of the principle of least action, guaranteeing the acquisition of the most appropriate counterdiabatic terms based on the underlying physics. The proposed approach offers a dependable alternative to address the CD driving problem, free from the constraints typically encountered in previous methodologies relying on classical numerical approximations. Our method provides a general framework to obtain optimal results from the physical observables relevant to the problem, including the external parameterization in time known as scheduling function, the gauge potential or operator involving the non-adiabatic terms, as well as the temporal evolution of the energy levels of the system, among others. The main applications of this methodology have been the $\mathrm{H_{2}}$ and $\mathrm{LiH}$ molecules, represented by a 2-qubit and 4-qubit systems employing the STO-3G basis. The presented results demonstrate the successful derivation of a desirable decomposition for the non-adiabatic terms, achieved through a linear combination utilizing Pauli operators. This attribute confers significant advantages to its practical implementation within quantum computing algorithms., Comment: 28 pages, 10 figures, 1 algorithm, 1 table

In this work, we analyze the effects of fractional derivatives in the chaotic dynamics of a cancer model. We begin by studying the dynamics of a standard model, {\it i.e.}, with integer derivatives. We study the dynamical behavior by means of the bifurcation diagram, Lyapunov exponents, and recurrence quantification analysis (RQA), such as the recurrence rate (RR), the determinism (DET), and the recurrence time entropy (RTE). We find a high correlation coefficient between the Lyapunov exponents and RTE. Our simulations suggest that the tumor growth parameter ($\rho_1$) is associated with a chaotic regime. Our results suggest a high correlation between the largest Lyapunov exponents and RTE. After understanding the dynamics of the model in the standard formulation, we extend our results by considering fractional operators. We fix the parameters in the chaotic regime and investigate the effects of the fractional order. We demonstrate how fractional dynamics can be properly characterized using RQA measures, which offer the advantage of not requiring knowledge of the fractional Jacobian matrix. We find that the chaotic motion is suppressed as $\alpha$ decreases, and the system becomes periodic for $\alpha \lessapprox 0.9966$. We observe limit cycles for $\alpha \in (0.9966,0.899)$ and fixed points for $\alpha<0.899$. The fixed point is determined analytically for the considered parameters. Finally, we discover that these dynamics are separated by an exponential relationship between $\alpha$ and $\rho_1$. Also, the transition depends on a supper transient which obeys the same relationship.

For tokamaks with uniform magnetic shear, Martin and Taylor have proposed a symplectic map has been used to describe the magnetic field lines at the plasma edge perturbed by an ergodic magnetic limiter. We propose an analytical magnetic field line map, based on the Martin-Taylor map, for a tokamak with arbitrary safety factor profile. With the inclusion of a non-monotonic profile, we obtain a nontwist map which presents the characteristic properties of degenerate systems, as the twin islands scenario, the shearless curve and separatrix reconnection. We estimate the width of the islands and describe their changes of shape for large values of the limiter current. From our numerical simulations about the shearless curve, we show that its position and aspect depend on the control parameters.

The macroscopic properties of compact stars in modified gravity theories can be significantly different from the general relativistic (GR) predictions. Within the gravitational context of scalar-tensor theories, with a scalar field $\phi$ and coupling function $\Phi(\phi)= \exp[2\phi/\sqrt{3}]$, we investigate the hydrostatic equilibrium structure of neutron stars for the simple potential $V(\phi)= \omega\phi^2/2$ defined in the Einstein frame (EF). From the scalar field in the EF, we also interpret such theories as $f(R)$ gravity in the corresponding Jordan frame (JF). The mass-radius relations, proper mass, and binding energy are obtained for a polytropic equation of state (EoS) in the JF. Our results reveal that the maximum-mass values increase substantially as $\omega$ gets smaller, while the radius and mass decrease in the low-central-density region as we move further away from the pure GR scenario. Furthermore, a cusp is formed when the binding energy is plotted as a function of the proper mass, which indicates the appearance of instability. Specifically, we find that the central-density value where the binding energy is a minimum corresponds precisely to $dM/d\rho_c^J = 0$ on the $M(\rho_c^J)$-curve., Comment: 9 pages, 6 figures. Accepted for publication in Physics of the Dark Universe

Quantifying electron-phonon interactions for the surface states of topological materials can provide key insights into surface-state transport, topological superconductivity, and potentially how to manipulate the surface state using a structural degree of freedom. We perform time-resolved x-ray diffraction (XRD) and angle-resolved photoemission (ARPES) measurements on Bi$_2$Te$_3$ and Bi$_2$Se$_3$, following the excitation of coherent A$_{1g}$ optical phonons. We extract and compare the deformation potentials coupling the surface electronic states to local A$_{1g}$-like displacements in these two materials using the experimentally determined atomic displacements from XRD and electron band shifts from ARPES.We find the coupling in Bi$_2$Te$_3$ and Bi$_2$Se$_3$ to be similar and in general in agreement with expectations from density functional theory. We establish a methodology that quantifies the mode-specific electron-phonon coupling experimentally, allowing detailed comparison to theory. Our results shed light on fundamental processes in topological insulators involving electron-phonon coupling.

Structured light beams, in particular those carrying orbital angular momentum (OAM), have gained a lot of attention due to their potential for enlarging the transmission capabilities of communication systems. However, the use of OAM-carrying light in communications faces two major problems, namely distortions introduced during propagation in disordered media, such as the atmosphere or optical fibers, and the large divergence that high-order OAM modes experience. While the use of non-orthogonal modes may offer a way to circumvent the divergence of high-order OAM fields, artificial intelligence (AI) algorithms have shown promise for solving the mode-distortion issue. Unfortunately, current AI-based algorithms make use of large-amount data-handling protocols that generally lead to large processing time and high power consumption. Here we show that a low-power, low-cost image sensor can itself act as an artificial neural network that simultaneously detects and reconstructs distorted OAM-carrying beams. We demonstrate the capabilities of our device by reconstructing (with a 95$\%$ efficiency) individual Vortex, Laguerre-Gaussian (LG) and Bessel modes, as well as hybrid (non-orthogonal) coherent superpositions of such modes. Our work provides a potentially useful basis for the development of low-power-consumption, light-based communication devices.

Active Learning (AL) is a family of machine learning (ML) algorithms that predates the current era of artificial intelligence. Unlike traditional approaches that require labeled samples for training, AL iteratively selects unlabeled samples to be annotated by an expert. This protocol aims to prioritize the most informative samples, leading to improved model performance compared to training with all labeled samples. In recent years, AL has gained increasing attention, particularly in the field of physics. This paper presents a comprehensive and accessible introduction to the theory of AL reviewing the latest advancements across various domains. Additionally, we explore the potential integration of AL with quantum ML, envisioning a synergistic fusion of these two fields rather than viewing AL as a mere extension of classical ML into the quantum realm., Comment: 15 pages