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We present cosmological results from the measurement of clustering of galaxy, quasar and Lyman-$\alpha$ forest tracers from the first year of observations with the Dark Energy Spectroscopic Instrument (DESI Data Release 1). We adopt the full-shape (FS) modeling of the power spectrum, including the effects of redshift-space distortions, in an analysis which has been validated in a series of supporting papers. In the flat $\Lambda$CDM cosmological model, DESI (FS+BAO), combined with a baryon density prior from Big Bang Nucleosynthesis and a weak prior on the scalar spectral index, determines matter density to $\Omega_\mathrm{m}=0.2962\pm 0.0095$, and the amplitude of mass fluctuations to $\sigma_8=0.842\pm 0.034$. The addition of the cosmic microwave background (CMB) data tightens these constraints to $\Omega_\mathrm{m}=0.3056\pm 0.0049$ and $\sigma_8=0.8121\pm 0.0053$, while further addition of the the joint clustering and lensing analysis from the Dark Energy Survey Year-3 (DESY3) data leads to a 0.4% determination of the Hubble constant, $H_0 = (68.40\pm 0.27)\,{\rm km\,s^{-1}\,Mpc^{-1}}$. In models with a time-varying dark energy equation of state, combinations of DESI (FS+BAO) with CMB and type Ia supernovae continue to show the preference, previously found in the DESI DR1 BAO analysis, for $w_0>-1$ and $w_a<0$ with similar levels of significance. DESI data, in combination with the CMB, impose the upper limits on the sum of the neutrino masses of $\sum m_\nu < 0.071\,{\rm eV}$ at 95% confidence. DESI data alone measure the modified-gravity parameter that controls the clustering of massive particles, $\mu_0=0.11^{+0.45}_{-0.54}$, while the combination of DESI with the CMB and the clustering and lensing analysis from DESY3 constrains both modified-gravity parameters, giving $\mu_0 = 0.04\pm 0.22$ and $\Sigma_0 = 0.044\pm 0.047$, in agreement with general relativity. [Abridged.], Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/). 55 pages, 10 figures

We present the samples of galaxies and quasars used for DESI 2024 cosmological analyses, drawn from the DESI Data Release 1 (DR1). We describe the construction of large-scale structure (LSS) catalogs from these samples, which include matched sets of synthetic reference `randoms' and weights that account for variations in the observed density of the samples due to experimental design and varying instrument performance. We detail how we correct for variations in observational completeness, the input `target' densities due to imaging systematics, and the ability to confidently measure redshifts from DESI spectra. We then summarize how remaining uncertainties in the corrections can be translated to systematic uncertainties for particular analyses. We describe the weights added to maximize the signal-to-noise of DESI DR1 2-point clustering measurements. We detail measurement pipelines applied to the LSS catalogs that obtain 2-point clustering measurements in configuration and Fourier space. The resulting 2-point measurements depend on window functions and normalization constraints particular to each sample, and we present the corrections required to match models to the data. We compare the configuration- and Fourier-space 2-point clustering of the data samples to that recovered from simulations of DESI DR1 and find they are, generally, in statistical agreement to within 2\% in the inferred real-space over-density field. The LSS catalogs, 2-point measurements, and their covariance matrices will be released publicly with DESI DR1., Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/)

We present the measurements and cosmological implications of the galaxy two-point clustering using over 4.7 million unique galaxy and quasar redshifts in the range $0.1

We show simplified DPS-QKD using a SiN micro-ring resonator operated at 852 nm. A raw-key rate of up to 25.3 kb/s is reached at a QBER suitable for secure-key extraction. Short-reach QKD operation is maintained for zero-touch link layouts with C-band telecom fiber.

Due to its fundamental principles, quantum theory holds the promise to enhance the security of modern cryptography, from message encryption to anonymous communication, digital signatures, online banking, leader election, one-time passwords and delegated computation. While quantum key distribution (QKD) has already enabled secure key exchange over hundreds of kilometers, a myriad of other quantum-cryptographic primitives are being developed to secure future applications against quantum adversaries. This article surveys the theoretical and experimental developments in quantum cryptography beyond QKD over the past decades, along with advances in secure quantum computation. It provides an intuitive classification of the main quantum primitives and their security levels, summarizes their possibilities and limits, and discusses their implementation with current photonic technology., Comment: Submitted to Reviews of Modern Physics. Comments are welcome

The present paper reports on wind tunnel tests and analyses carried out to investigate the effect of the static angle of attack on the aerodynamic stability of a twin-box bridge deck section. It is found that the critical wind speed for onset of flutter increases with increasing positive static angles (nose-up) and that this effect relates mainly to a decrease in the loss of aerodynamic stiffness. A simplified flutter analysis, linking the slope of the static moment coefficient to the increase of flutter stability for increasing positive angles of attack. It is concluded that it is desirable to design twin-box bridge deck sections to have a positive moment coefficient at zero angle of attack and a positive, decreasing moment slope for increasing nose-up angles. With these requirements fulfilled, the present study show that the critical wind speeds for onset of flutter increase with increasing angles, and ensures that the elastically supported deck will always meet the wind at ever increasing angles for increasing wind speeds., Comment: 12 pages, 6 figures, submitted to proceedings of the 16th International Conference on Wind Engineering (ICWE16) 2023, Florence, Italy

The paper considers the problem of detecting and localizing changepoints in a sequence of independent observations. We propose to evaluate a local test statistic on a triplet of time points, for each such triplet in a particular collection. This collection is sparse enough so that the results of the local tests can simply be combined with a weighted Bonferroni correction. This results in a simple and fast method, {\sl Lean Bonferroni Changepoint detection} (LBD), that provides finite sample guarantees for the existance of changepoints as well as simultaneous confidence intervals for their locations. LBD is free of tuning parameters, and we show that LBD allows optimal inference for the detection of changepoints. To this end, we provide a lower bound for the critical constant that measures the difficulty of the changepoint detection problem, and we show that LBD attains this critical constant. We illustrate LBD for a number of distributional settings, namely when the observations are homoscedastic normal with known or unknown variance, for observations from a natural exponential family, and in a nonparametric setting where we assume only exchangeability for segments without a changepoint.

We present a detailed study of a scalar differential equation with threshold state-dependent delayed feedback. This equation arises as a simplification of a gene regulatory model. There are two monotone nonlinearities in the model: one describes the dependence of delay on state, and the other is the feedback nonlinearity. Both increasing and decreasing nonlinearities are considered. Our analysis is exhaustive both analytically and numerically as we examine the bifurcations of the system for various combinations of increasing and decreasing nonlinearities. We identify rich bifurcation patterns including Bautin, Bogdanov-Takens, cusp, fold, homoclinic, and Hopf bifurcations whose existence depend on the derivative signs of nonlinearities. Our analysis confirms many of these patterns in the limit where the nonlinearities are switch-like and change their value abruptly at a threshold. Perhaps one of the most surprising findings is the existence of a Hopf bifurcation to a periodic solution when the nonlinearity is monotone increasing and the time delay is a decreasing function of the state variable--something that is impossible in the case of a constant delay.

Emergent strongly-correlated electronic phenomena in atomically-thin transition metal dichalcogenides are an exciting frontier in condensed matter physics, with examples ranging from bilayer superconductivity~\cite{zhao2023evidence} and electronic Wigner crystals~\cite{smolenski2021signatures,zhou2021bilayer} to the ongoing quest for exciton condensation~\cite{wang2019evidence,ma2021strongly,shi2022bilayer}. Here, we experimentally investigate the properties of indirect excitons in naturally-grown MoS$_2$-homobilayer, integrated in a dual-gate device structure allowing independent control of the electron density and out-of-plane electric field. Under conditions when electron tunneling between the layers is negligible~\cite{pisoni2019absence}, upon electron doping the sample, we observe that the two excitons with opposing dipoles hybridize, displaying unusual behavior distinct from both conventional level crossing and anti-crossing. We show that these observations can be explained by static random coupling between the excitons, which increases with electron density and decreases with temperature. We argue that this phenomenon is indicative of a spatially fluctuating order parameter in the form of interlayer electron coherence, a theoretically predicted many-body state~\cite{zheng1997exchange} that has yet to be unambiguously established experimentally outside of the quantum Hall regime~\cite{sarma2008perspectives,spielman2000resonantly,kellogg2004vanishing,kellogg2002observation,spielman2001observation,fertig1989energy,shi2022bilayer}. Implications of our findings for future experiments and quantum optics applications are discussed.

In the manufacturing industry, the digital twin (DT) is becoming a central topic. It has the potential to enhance the efficiency of manufacturing machines and reduce the frequency of errors. In order to fulfill its purpose, a DT must be an exact enough replica of its corresponding physical object. Nevertheless, the physical object endures a lifelong process of degradation. As a result, the digital twin must be modified accordingly in order to satisfy the accuracy requirement. This article introduces the novel concept of "learning digital twin (LDT)," which concentrates on the temporal behavior of the physical object and highlights the digital twin's capacity for lifelong learning. The structure of a LDT is first described. Then, in-depth descriptions of various algorithms for implementing each component of a LDT are provided. The proposed LDT is validated on the simulated degradation process of an anisotropic non-ideal rotor system.

State-of-the-art laser frequency stabilization is limited by miniscule length changes caused by thermal noise. In this work, a cavity-length-insensitive frequency stabilization scheme is implemented using strong dispersion in a $21\,\mathrm{mm}$ long cavity with a europium-ion-doped spacer of yttrium orthosilicate. A number of limiting factors for slow light laser stabilization are evaluated, including the inhomogeneous and homogeneous linewidth of the ions, the deterioration of spectral windows, and the linewidth of the cavity modes. Using strong dispersion, the cavity modes were narrowed by a factor $1.6\cdot 10^5$, leading to a cavity linewidth of $3.0\,\mathrm{kHz}$ and a $Q$ factor of $1.7\cdot 10^{11}$. Frequency stabilization was demonstrated using a cavity mode in a spectral transparency region near the center of the inhomogeneous profile, showing an overlapping Allan deviation below $6\cdot 10^{-14}$ and a linear drift rate of $3.66\,\mathrm{Hz}/\mathrm{s}$. Considering improvements that could be implemented, this makes the europium-based slow light laser frequency reference a promising candidate for ultra-precise tabletop frequency stabilization., Comment: 15 pages, 7 figures

Quantum computing has brought a paradigm change in computer science, where non-classical technologies have promised to outperform their classical counterpart. Such an advantage was only demonstrated for tasks without practical applications, still out of reach for the state-of-art quantum technologies. In this context, a promising strategy to find practical use of quantum computers is to exploit hybrid quantum-classical models, where a quantum device estimates a hard-to-compute quantity, while a classical optimizer trains the parameters of the model. In this work, we demonstrate that single photons and linear optical networks are sufficient for implementing Variational Quantum Algorithms, when the problem specification, or ansatz, is tailored to this specific platform. We show this by a proof-of-principle demonstration of a variational approach to tackle an instance of a factorization task, whose solution is encoded in the ground state of a suitable Hamiltonian. This work which combines Variational Quantum Algorithms with hardware efficient ansatzes for linear-optics networks showcases a promising pathway towards practical applications for photonic quantum platforms.

Nature disasters play a key role in shaping human-urban infrastructure interactions. Effective and efficient response to natural disasters is essential for building resilience and a sustainable urban environment. Two types of information are usually the most necessary and difficult to gather in disaster response. The first information is about disaster damage perception, which shows how badly people think that urban infrastructure has been damaged. The second information is geolocation awareness, which means how people whereabouts are made available. In this paper, we proposed a novel disaster mapping framework, namely CVDisaster, aiming at simultaneously addressing geolocalization and damage perception estimation using cross-view Street-View Imagery (SVI) and Very High-Resolution satellite imagery. CVDisaster consists of two cross-view models, where CVDisaster-Geoloc refers to a cross-view geolocalization model based on a contrastive learning objective with a Siamese ConvNeXt image encoder, and CVDisaster-Est is a cross-view classification model based on a Couple Global Context Vision Transformer (CGCViT). Taking Hurricane IAN as a case study, we evaluate the CVDisaster framework by creating a novel cross-view dataset (CVIAN) and conducting extensive experiments. As a result, we show that CVDisaster can achieve highly competitive performance (over 80% for geolocalization and 75% for damage perception estimation) with even limited fine-tuning efforts, which largely motivates future cross-view models and applications within a broader GeoAI research community. The data and code are publicly available at: https://github.com/tum-bgd/CVDisaster.

The Three-Body Problem has fascinated scientists for centuries and it has been crucial in the design of modern space missions. Recent developments in Generative Artificial Intelligence hold transformative promise for addressing this longstanding problem. This work investigates the use of Variational Autoencoder (VAE) and its internal representation to generate periodic orbits. We utilize a comprehensive dataset of periodic orbits in the Circular Restricted Three-Body Problem (CR3BP) to train deep-learning architectures that capture key orbital characteristics, and we set up physical evaluation metrics for the generated trajectories. Through this investigation, we seek to enhance the understanding of how Generative AI can improve space mission planning and astrodynamics research, leading to novel, data-driven approaches in the field., Comment: SPAICE Conference 2024 (7 pages)

The interaction between a single emitter and a single photon is a fundamental aspect of quantum optics. This interaction allows for the study of various quantum processes, such as emitter-mediated single-photon scattering and effective photon-photon interactions. However, empirical observations of this scenario and its dynamics are rare, and in most cases, only partial approximations to the fully quantized case have been possible. Here, we demonstrate the resonant excitation of a solid-state quantum emitter using quantized input light. For this light-matter interaction, with both entities quantized, we observe single-photon interference introduced by the emitter in a coherent scattering process, photon-number-depended optical non-linearities, and stimulated emission processes involving only two photons. We theoretically reproduce our observations using a cascaded master equation model. Our findings demonstrate that a single photon is sufficient to change the state of a solid-state quantum emitter, and efficient emitter-mediated photon-photon interactions are feasible. These results suggest future possibilities ranging from enabling quantum information transfer in a quantum network to building deterministic entangling gates for photonic quantum computing., Comment: 13 pages, 8 figures

Recently, machine learning had a remarkable impact, from scientific to everyday-life applications. However, complex tasks often imply unfeasible energy and computational power consumption. Quantum computation might lower such requirements, although it is unclear whether enhancements are reachable by current technologies. Here, we demonstrate a kernel method on a photonic integrated processor to perform a binary classification. We show that our protocol outperforms state-of-the-art kernel methods including gaussian and neural tangent kernels, exploiting quantum interference, and brings a smaller improvement also by single photon coherence. Our scheme does not require entangling gates and can modify the system dimension through additional modes and injected photons. This result opens to more efficient algorithms and to formulating tasks where quantum effects improve standard methods.

We present a method for blind acoustic parameter estimation from single-channel reverberant speech. The method is structured into three stages. In the first stage, a variational auto-encoder is trained to extract latent representations of acoustic impulse responses represented as mel-spectrograms. In the second stage, a separate speech encoder is trained to estimate low-dimensional representations from short segments of reverberant speech. Finally, the pre-trained speech encoder is combined with a small regression model and evaluated on two parameter regression tasks. Experimentally, the proposed method is shown to outperform a fully end-to-end trained baseline model., Comment: Accepted for publication at IWAENC 2024

Entangled quantum states are essential ingredients for many quantum technologies, but they must be validated before they are used. As a full characterization is prohibitively resource-intensive, recent work has focused on developing methods to efficiently extract a few parameters of interest, in a so-called verification framework. Most existing approaches are based on preparing an ensemble of nominally identical and independent (IID) quantum states, and then measuring each copy of the ensemble. However, this leaves no states left for the intended quantum tasks and the IID assumptions do not always hold experimentally. To overcome these challenges, we experimentally implement quantum state certification (QSC), which measures only a subset of the ensemble, certifying the fidelity of the remaining states. We use active optical switches to randomly sample from sources of two-photon Bell states and three-photon GHZ states, reporting statistically-sound fidelities in real time without destroying the entire ensemble. Additionally, our QSC protocol removes the assumption that the states are identical, is device-independent, and can achieve close $N^{-1}$ scaling, in the number of states measured $N$. Altogether, these benefits make our QSC protocol suitable for benchmarking large-scale quantum computing devices and deployed quantum communication setups relying on entanglement in both standard and adversarial situations., Comment: 8 pages, 4 figures

Excitons in transition metal dichalcogenides (TMDs) have emerged as a promising platform for novel applications ranging from optoelectronic devices to quantum optics and solid state quantum simulators. While much progress has been made towards characterizing and controlling excitons in TMDs, manipulating their properties during the course of their lifetime - a key requirement for many optoelectronic device and information processing modalities - remains an outstanding challenge. Here we combine long-lived interlayer excitons in angle-aligned MoSe$_2$/WSe$_2$ heterostructures with fast electrical control to realize dynamical control schemes, in which exciton properties are not predetermined at the time of excitation but can be dynamically manipulated during their lifetime. Leveraging the out-of-plane exciton dipole moment, we use electric fields to demonstrate dynamical control over the exciton emission wavelength. Moreover, employing a patterned gate geometry, we demonstrate rapid local sample doping and toggling of the radiative decay rate through exciton-charge interactions during the exciton lifetime. Spatially mapping the exciton response reveals charge redistribution, offering a novel probe of electronic transport in twisted TMD heterostructures. Our results establish the feasibility of dynamical exciton control schemes, unlocking new directions for exciton-based information processing and optoelectronic devices, and the realization of excitonic phenomena in TMDs., Comment: 37 pages, 4 figures in main text, 6 figures in supplemental materials; (v2) corrected funding acknowledgements

The prediction of time series is a challenging task relevant in such diverse applications as analyzing financial data, forecasting flow dynamics or understanding biological processes. Especially chaotic time series that depend on a long history pose an exceptionally difficult problem. While machine learning has shown to be a promising approach for predicting such time series, it either demands long training time and much training data when using deep Recurrent Neural Networks. Alternative, when using a Reservoir Computing approach it comes with high uncertainty and typically a high number of random initializations and extensive hyper-parameter tuning. In this paper, we focus on the Reservoir Computing approach and propose a new mapping of input data into the reservoir's state space. Furthermore, we incorporate this method in two novel network architectures increasing parallelizability, depth and predictive capabilities of the neural network while reducing the dependence on randomness. For the evaluation, we approximate a set of time series from the Mackey-Glass equation, inhabiting non-chaotic as well as chaotic behavior as well as the SantaFe Laser dataset and compare our approaches in regard to their predictive capabilities to Echo State Networks, Autoencoder connected Echo State Networks and Gated Recurrent Units. For the chaotic time series, we observe an error reduction of up to $85.45\%$ compared to Echo State Networks and $90.72\%$ compared to Gated Recurrent Units. Furthermore, we also observe tremendous improvements for non-chaotic time series of up to $99.99\%$ in contrast to the existing approaches.

With the growth of digital monetary transactions and cashless payments, encouraged by the COVID-19 pandemic, use of e-payment applications is on the rise. It is thus imperative to understand and evaluate the current posture of e-payment applications from three major user-facing angles: security, privacy, and usability. To this, we created a high-fidelity prototype of an e-payment application that encompassed features that we wanted to test with users. We then conducted a pilot study where we recruited 12 participants who tested our prototype. We find that both security and privacy are important for users of e-payment applications. Additionally, some participants perceive the strength of security and privacy based on the usability of the application. We provide recommendations such as universal design of e-payment applications.

The paper provides a detailed proof that complicated motion exists in Shilnikov's scenario of a smooth vectorfield $V$ on $mathbb{R}^3$ with $V(0)=0$ so that the equation $x'=V(x)$ has a homoclinic solution $h$ with $\lim_{|t|\to\infty}h(t)=0$, and $DV(0)$ has eigenvalues $u>0$ and $\sigma\pm\mu$, $\sigma<0<\mu$, with $0<\sigma+u$., Comment: 33 pages, 8 figures

We experimentally and numerically study the collapse dynamics of a quantum vortex in a two-dimensional atomic superfluid following a fast interaction ramp from repulsion to attraction. We find the conditions and time scales for a superfluid vortex to radially converge into a quasi-stationary density profile, demonstrating the first spontaneous formation of a vortex soliton in an atomic Bose gas. We record an emergent universal dynamics of an azimuthal modulational instability, which amplifies initial density perturbations and leads to the eventual splitting of a vortex soliton or direct fragmentation of a superfluid into disordered, but roughly circular arrays of Townes soliton-like wavepackets. Our study sets the stage for exploring universal out-of-equilibrium dynamics of vortex quantum matter quenched to attractive interactions.

Maternal investment directly shapes early developmental conditions and therefore has longterm fitness consequences for the offspring. In oviparous species prenatal maternal investment is fixed at the time of laying. To ensure the best survival chances for most of their offspring, females must equip their eggs with the resources required to perform well under various circumstances, yet the actual mechanisms remain unknown. Here we describe the blue tit egg albumen and yolk proteomes and evaluate their potential to mediate maternal effects. We show that variation in egg composition (proteins, lipids, carotenoids) primarily depends on laying order and female age. Egg proteomic profiles are mainly driven by laying order, and investment in the egg proteome is functionally biased among eggs. Our results suggest that maternal effects on egg composition result from both passive and active (partly compensatory) mechanisms, and that variation in egg composition creates diverse biochemical environments for embryonic development.

We apply continuous similarity transformations (CSTs) to study the zero-temperature breakdown of long-range ordered quantum antiferromagnets. The CST flow equations are truncated in momentum space by the scaling dimension so that all operators with scaling dimension up to two are taken into account. We determine the quantum phase transition out of the N\'eel-ordered phase in the unfrustrated square lattice Heisenberg bilayer as well as the quantum melting of the N\'eel-ordered and columnar phase in the highly frustrated $J_1$-$J_2$ model on the square lattice. In all cases the CST is set up to isolate the ground state so that the stability of the flow equations, the ground-state energy, and the sublattice magnetization are used to explore the long-range ordered phases. We extract quantum-critical points which agree well with values in the literature. Further, we estimate the associated critical exponents $\alpha$ and $\beta$ which turns out to be a challenging task for the CST approach., Comment: 16 pages, 15 figures

This paper introduces quantum edge detection, aimed at locating boundaries of quantum domains where all particles share the same pure state. Focusing on the 1D scenario of a string of particles, we develop an optimal protocol for quantum edge detection, efficiently computing its success probability through Schur-Weyl duality and semidefinite programming techniques. We analyze the behavior of the success probability as a function of the string length and local dimension, with emphasis in the limit of long strings. We present a protocol based on square root measurement, which proves asymptotically optimal. Additionally, we explore a mixed quantum change point detection scenario where the state of particles transitions from known to unknown, which may find practical applications in detecting malfunctions in quantum devices, Comment: 13+6 pages, 2 figures

Rodents employ a broad spectrum of ultrasonic vocalizations (USVs) for social communication. As these vocalizations offer valuable insights into affective states, social interactions, and developmental stages of animals, various deep learning approaches have aimed to automate both the quantitative (detection) and qualitative (classification) analysis of USVs. Here, we present the first systematic evaluation of different types of neural networks for USV classification. We assessed various feedforward networks, including a custom-built, fully-connected network and convolutional neural network, different residual neural networks (ResNets), an EfficientNet, and a Vision Transformer (ViT). Paired with a refined, entropy-based detection algorithm (achieving recall of 94.9% and precision of 99.3%), the best architecture (achieving 86.79% accuracy) was integrated into a fully automated pipeline capable of analyzing extensive USV datasets with high reliability. Additionally, users can specify an individual minimum accuracy threshold based on their research needs. In this semi-automated setup, the pipeline selectively classifies calls with high pseudo-probability, leaving the rest for manual inspection. Our study focuses exclusively on neonatal USVs. As part of an ongoing phenotyping study, our pipeline has proven to be a valuable tool for identifying key differences in USVs produced by mice with autism-like behaviors.

Broad absorption line quasars (BALs) exhibit blueshifted absorption relative to a number of their prominent broad emission features. These absorption features can contribute to quasar redshift errors and add absorption to the Lyman-alpha (LyA) forest that is unrelated to large-scale structure. We present a detailed analysis of the impact of BALs on the Baryon Acoustic Oscillation (BAO) results with the LyA forest from the first year of data from the Dark Energy Spectroscopic Instrument (DESI). The baseline strategy for the first year analysis is to mask all pixels associated with all BAL absorption features that fall within the wavelength region used to measure the forest. We explore a range of alternate masking strategies and demonstrate that these changes have minimal impact on the BAO measurements with both DESI data and synthetic data. This includes when we mask the BAL features associated with emission lines outside of the forest region to minimize their contribution to redshift errors. We identify differences in the properties of BALs in the synthetic datasets relative to the observational data, as well as use the synthetic observations to characterize the completeness of the BAL identification algorithm, and demonstrate that incompleteness and differences in the BALs between real and synthetic data also do not impact the BAO results for the LyA forest., Comment: 32 pages, 8 figures, accepted by JCAP

The quantitative description of long-range order remains a challenge in quantum many-body physics. We provide zero-temperature results from two complementary methods for the ground-state energy per site, the sublattice magnetization, the spin gap, and the transverse spin correlation length for the spin-1/2 anisotropic quantum Heisenberg antiferromagnet on the square lattice. On the one hand, we use exact, large-scale quantum Monte Carlo (QMC) simulations. On the other hand, we use the semi-analytic approach based on continuous similarity transformations in terms of elementary magnon excitations. Our findings confirm the applicability and quantitative validity of both approaches along the full parameter axis from the Ising point to the symmetry-restoring phase transition at the Heisenberg point and further provide quantitative reference results in the thermodynamic limit. In addition, we analytically derive the relation between the dispersion and the correlation length at zero temperature in arbitrary dimension, and discuss improved second-moment QMC estimators., Comment: 12 pages, 5 figures

One of the most striking quantum phenomena is superposition, where one particle simultaneously inhabits different states. Most methods to verify coherent superposition are indirect, in that they require the distinct states to be recombined. Here, we adapt an XOR game, in which separated parties measure different parts of a superposed particle, and use it to verify superpositions with \textit{local measurements} and a second independent particle. We then turn this game into a resource-efficient verification scheme, obtaining a confidence that the particle is superposed which approaches unity exponentially fast. We demonstrate our scheme using a single photon, obtaining a 99\% confidence that the particle is superposed with only 37 copies. Our work shows the utility of XOR games to verify quantum resources, allowing us to efficiently detect quantum superposition without reinterfering the superposed states., Comment: 13 pages (incl. 8 pages appendix) , 7 figures

We implement a silicon-on-insulator light emitter as optical supply for a QKD transmitter and transfer it to an electronic BiCMOS wafer. A secure key is established over short reach in co-existence with shortwave data transmission.

We demonstrate QKD and data communication over an out-door free-space link where large-core fiber substitutes active alignment. We further prove E-band QKD as stable and robust under full daylight, despite the loss of spatial filtering.

In the past decade, the toolkit of quantum information has been expanded to include processes in which the basic operations do not have definite causal relations. Originally considered in the context of the unification of quantum mechanics and general relativity, these causally indefinite processes have been shown to offer advantages in a wide variety of quantum information processing tasks, ranging from quantum computation to quantum metrology. Here we overview these advantages and the experimental efforts to realise them. We survey both the different experimental techniques employed, as well as theoretical methods developed in support of the experiments, before discussing the interpretations of current experimental results and giving an outlook on the future of the field., Comment: 22 page review article. Comments welcome!

Given a matroid or flag of matroids we introduce several broad classes of polynomials satisfying Deletion-Contraction identities, and study their singularities. There are three main families of polynomials captured by our approach: matroidal polynomials on a matroid (including matroid basis polynomials, configuration polynomials, Tutte polynomials); flag matroidal polynomials on a flag matroid; and Feynman integrands. The last class includes under general kinematics the inhomogeneous Feynman diagram polynomials which naturally arise in the Lee--Pomeransky form of the Feynman integral attached to a Feynman diagram. Assuming that the primary underlying matroid is connected and of positive rank (and in the flag case, has rank at least two), we show: a) in positive characteristic, homogeneous matroidal polynomials are strongly $F$-regular; b) over an algebraically closed field of characteristic zero, the associated jet schemes of (flag) matroidal polynomials as well as those of Feynman integrands are irreducible. Consequently, all these polynomials have rational singularities (or are smooth).

The paper presents the second part of a precise description of the prototype that has been developed in the course of the ISAC project over the last two decades. This part describes the "specify-phase", while the first part describing the "solve-phase" is already published. In the specify-phase a student interactively constructs a formal specification. The ISAC prototype implements formal specifications as established in theoretical computer science, however, the input language for the construction avoids requiring users to have knowledge of logic; this makes the system useful for various engineering faculties (and also for high school). The paper discusses not only ISAC's design of the specify-phase in detail, but also gives a brief introduction to implementation with the aim of advertising the re-use of formal frameworks (inclusive respective front-ends) with their generic tools for language definition and their rich pool of software components for formal mathematics., Comment: In Proceedings ThEdu'23, arXiv:2404.03709

Distortion free negative group velocity pulse propagation is demonstrated in a rare-earth-ion-doped-crystal (RE) through the creation of a carefully designed spectral absorption structure in the inhomogeneous profile of Eu:YSO and subsequently inverting it. The properties of the RE system make it particularly well suited for this since it supports the creation of very sharp, arbitrarily tailored spectral features, which can be coherently inverted by a single pulse thanks to the long coherence time of the transition. All together these properties allow for a large time advancement of pulses without causing distortion. A pulse advancement of 10.9% with respect to the pulse full-width-half-maximum was achieved corresponding to a time-bandwidth product of 0.05. This to our knowledge is the largest time-bandwidth product achieved, with negligible shape distortion and attenuation. Our results show that the rare-earth platform is a powerful test bed for superluminal propagation in particular and dispersion profile programming in general, Comment: Main paper 4 pages, 2 figures, total 8 pages 6 figures

The ThEdu series pursues the smooth transition from an intuitive way of doing mathematics at secondary school to a more formal approach to the subject in STEM education, while favouring software support for this transition by exploiting the power of theorem-proving technologies. What follows is a brief description of how the present volume contributes to this enterprise. The 12th International Workshop on Theorem Proving Components for Educational Software(ThEdu'23), was a satellite event of the 29th international Conference on Automated Deduction (CADE 2023), July 1-4, 2023, Rome, Italy. ThEdu'23 was very successful, with one invited talk, by Yves Bertot (Inria, France), "The challenges of using Type Theory to teach Mathematics", and seven regular contributions. An open call for papers was then issued, to which eight contributions were submitted. Seven submissions have been accepted by our reviewers, who jointly produced at least three careful reports on each of the contributions. The resulting revised papers are collected in the present volume. We, the volume editors, hope that this collection of papers will further promote the development of theorem-proving based software, and that it will allow to improve the mutual understanding between computer scientists, mathematicians and stakeholders in education. PC Chairs:Julien Narboux (University of Strasbourg, France); Walther Neuper (JKU, Johannes Kepler University, Linz, Austria); Pedro Quaresma (University of Coimbra, Portugal)

We present cosmological results from the measurement of baryon acoustic oscillations (BAO) in galaxy, quasar and Lyman-$\alpha$ forest tracers from the first year of observations from the Dark Energy Spectroscopic Instrument (DESI), to be released in the DESI Data Release 1. DESI BAO provide robust measurements of the transverse comoving distance and Hubble rate, or their combination, relative to the sound horizon, in seven redshift bins from over 6 million extragalactic objects in the redshift range $0.1-1$ and $w_a<0$. This preference is 2.6$\sigma$ for the DESI+CMB combination, and persists or grows when SN~Ia are added in, giving results discrepant with the $\Lambda$CDM model at the $2.5\sigma$, $3.5\sigma$ or $3.9\sigma$ levels for the addition of Pantheon+, Union3, or DES-SN5YR datasets respectively. For the flat $\Lambda$CDM model with the sum of neutrino mass $\sum m_\nu$ free, combining the DESI and CMB data yields an upper limit $\sum m_\nu < 0.072$ $(0.113)$ eV at 95% confidence for a $\sum m_\nu>0$ $(\sum m_\nu>0.059)$ eV prior. These neutrino-mass constraints are substantially relaxed in models beyond $\Lambda$CDM. [Abridged.], Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers). 68 pages, 15 figures. Version accepted for publication in JCAP

We present the measurement of Baryon Acoustic Oscillations (BAO) from the Lyman-$\alpha$ (Ly$\alpha$) forest of high-redshift quasars with the first-year dataset of the Dark Energy Spectroscopic Instrument (DESI). Our analysis uses over $420\,000$ Ly$\alpha$ forest spectra and their correlation with the spatial distribution of more than $700\,000$ quasars. An essential facet of this work is the development of a new analysis methodology on a blinded dataset. We conducted rigorous tests using synthetic data to ensure the reliability of our methodology and findings before unblinding. Additionally, we conducted multiple data splits to assess the consistency of the results and scrutinized various analysis approaches to confirm their robustness. For a given value of the sound horizon ($r_d$), we measure the expansion at $z_{\rm eff}=2.33$ with 2\% precision, $H(z_{\rm eff}) = (239.2 \pm 4.8) (147.09~{\rm Mpc} /r_d)$ km/s/Mpc. Similarly, we present a 2.4\% measurement of the transverse comoving distance to the same redshift, $D_M(z_{\rm eff}) = (5.84 \pm 0.14) (r_d/147.09~{\rm Mpc})$ Gpc. Together with other DESI BAO measurements at lower redshifts, these results are used in a companion paper to constrain cosmological parameters., Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers). Minor changes in v4, version accepted for publication in JCAP

Baryon Acoustic Oscillations can be measured with sub-percent precision above redshift two with the Lyman-alpha forest auto-correlation and its cross-correlation with quasar positions. This is one of the key goals of the Dark Energy Spectroscopic Instrument (DESI) which started its main survey in May 2021. We present in this paper a study of the contaminants to the lyman-alpha forest which are mainly caused by correlated signals introduced by the spectroscopic data processing pipeline as well as astrophysical contaminants due to foreground absorption in the intergalactic medium. Notably, an excess signal caused by the sky background subtraction noise is present in the lyman-alpha auto-correlation in the first line-of-sight separation bin. We use synthetic data to isolate this contribution, we also characterize the effect of spectro-photometric calibration noise, and propose a simple model to account for both effects in the analysis of the lyman-alpha forest. We then measure the auto-correlation of the quasar flux transmission fraction of low redshift quasars, where there is no lyman-alpha forest absorption but only its contaminants. We demonstrate that we can interpret the data with a two-component model: data processing noise and triply ionized Silicon and Carbon auto-correlations. This result can be used to improve the modeling of the lyman-alpha auto-correlation function measured with DESI., Comment: 32 pages, 12 figures

The first year of data from the Dark Energy Spectroscopic Instrument (DESI) contains the largest set of Lyman-$\alpha$ (Ly$\alpha$) forest spectra ever observed. This data, collected in the DESI Data Release 1 (DR1) sample, has been used to measure the Baryon Acoustic Oscillation (BAO) feature at redshift $z=2.33$. In this work, we use a set of 150 synthetic realizations of DESI DR1 to validate the DESI 2024 Ly$\alpha$ forest BAO measurement. The synthetic data sets are based on Gaussian random fields using the log-normal approximation. We produce realistic synthetic DESI spectra that include all major contaminants affecting the Ly$\alpha$ forest. The synthetic data sets span a redshift range $1.8

We present the DESI 2024 galaxy and quasar baryon acoustic oscillations (BAO) measurements using over 5.7 million unique galaxy and quasar redshifts in the range 0.1