Quantum computing is emerging as a promising tool for astrophysics and cosmology, with the potential to address computationally intensive problems more efficiently than classical approaches. In this review, we report recent advances from the INAF Spoke-10 initiative within the Italian National Research Center for High Performance Computing, Big Data and Quantum Computing (ICSC), focused on quantum algorithms and machine learning techniques for astronomical data analysis. We concentrate on two application areas: (1) quantum machine learning for high-energy transients, specifically the detection of Gamma-Ray Bursts (GRBs) with quantum deep-learning models; and (2) quantum algorithms for cosmology, including Quantum Markov Chain Monte Carlo and Quantum Genetic Algorithms, together with developments of Quantum Fourier Transform methods. Quantum machine learning models for GRB detection, such as autoencoders, are tested on simulated space telescope data, achieving performance comparable to classical deep learning methods and indicating potential benefits in data-limited or constrained scenarios. For cosmology, hybrid quantum-classical algorithms have been developed to determine best-fit parameters, sample posterior distributions for standard cosmological models, and perform Quantum Fast Fourier transforms for the Cosmic Microwave Background (CMB) radiation. These methods are validated on benchmark problems, providing results consistent with established classical methods. We discuss the implications for astrophysical and cosmological analysis. While the field is still in its early stages and no quantum advantage has yet been demonstrated in the presented problems, the progress summarized here highlights both the current capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices and the open challenges. For quantum machine learning, we demonstrated that these algorithms are more effective when data is scarce or highly complex, which might hint at future quantum advantages. We outline future directions for integrating quantum processors into astronomical pipelines and the steps required to realize practical quantum advantages in data-intensive astrophysics and cosmology.

The cross section for galaxy-galaxy strong lensing (GGSL) events in galaxy clusters has repeatedly been found to be higher in observations than in cosmological simulations. We revisit this discrepancy using updated simulation methodology and investigate the dependence of the GGSL probability, P_ GGSL, on host-cluster lensing properties, baryonic physics, and correlated and uncorrelated line-of-sight structure. We find that correlated material within ∼ 35 cMpc of the cluster along the line of sight enhances P_ GGSL by a few percent for typical systems and by up to ∼15% for the most efficient lenses. At fixed cluster mass, dark-matter-only simulations yield GGSL probabilities up to an order of magnitude lower than hydrodynamical simulations. We also find a strong dependence on the host-cluster Einstein radius, with an approximate scaling P_ GGSL∝ θ_ E^2. Matching simulated and observed clusters in both mass and Einstein radius seems to reduce the discrepancy relative to previous comparisons. However, our analysis does not clearly resolve the GGSL discrepancy, as the inferred tension depends strongly on the field-of-view definition: square fields are approximately consistent with simulations, while cluster member-bounded fields yield observed probabilities a factor of ∼ 2-3 higher. Until observations and simulations share a matched selection function and matched measurement methodology, the residual tension cannot be cleanly attributed to either astrophysics or cosmology.

An Amplitude-Encoded Quantum Genetic Algorithm (AEQGA) has been developed to minimize χ^2 functions of different cosmological probes (Supernovae Type Ia, Baryon Acoustic Oscillations, Cosmic Microwave Background Radiation), to find the best-fit value for two cosmological parameters, namely the Hubble Constant and the density matter content of the Universe today. Our main aim is to pave the way to testing the adoption of quantum optimization in the inference of the cosmological parameters that describe the universe evolution. AEQGA computes the merit function classically, and then uses a quantum circuit to entangle the population and perform crossover and mutation operations. The results show consistency with the isocontours of the objective functions. We then tested the general behavior of AEQGA as a function of its hyperparameters and compared it with a second quantum genetic algorithm found in the literature as well as with classical algorithms, finding consistent results.

In the standard cold dark matter (CDM) scenario, the density profiles of dark matter haloes are well described by analytical models linking their concentration to halo mass. Alternative scenarios, such as warm dark matter (WDM) and self-interacting dark matter (SIDM), modify the inner structure of haloes and predict different profile shapes and central slopes. We employ the AIDA-TNG simulations to investigate how alternative dark matter physics and baryonic processes jointly shape the internal structure of haloes. Using dark-matter-only and full-physics runs, we measured the dark matter density profiles of haloes spanning six orders of magnitude in mass, from 10(9.5) M-circle dot to 10(14.5) M-circle dot, considering radial bins that are well resolved above the spatial resolution of the simulations (r >= 4.4 kpc and r >= 1.7 kpc, for the 110.7 and 51.7 Mpc boxes, respectively). We fit the profiles with multiple analytical models and provide the distribution of the best-fitting parameters, as well as the concentration-mass relation in WDM and SIDM. The Einasto profile well reproduces the inner flattening produced in the WDM models, both in the collisionless and in the full-physics runs. In the SIDM dark-matter-only runs, haloes are better described by explicitly cored profiles, with core sizes that depend on mass and on the self-interaction model. When baryons are included, the differences between CDM and SIDM decrease, and such large dark-matter cores no longer form because adiabatic contraction in the baryon-dominated region counteracts self-interactions. Nevertheless, the coupling between baryons and self-interactions induces a broader range of inner slopes, including cases that are steeper than CDM at Milky Way masses. Alternative dark matter physics thus leaves clear signatures in the inner halo structure, even if baryons significantly reshape these differences. Our results are useful for future studies that need to predict halo properties in multiple dark matter models.

Galaxy cluster strong gravitational lensing plays a central role in precision cosmology, yet robust theoretical predictions have lagged behind an abundance of high-quality strong lensing observations. This shortfall reflects both a mismatch between the geometry of the strong-lensing problem and standard cubic simulation boxes, and the fundamental tension between simulation volume and resolution. Consequently, many current forecasts adopt hybrid approaches that extract individual lenses from simulations and combine them with analytic or observed source populations positioned near caustics. These methods often omit correlated and/or uncorrelated line-of-sight (LoS) structure, or include it in ways that do not preserve correlations across redshift. Here we present a fully simulation-based procedure that generates strong-lensing images directly from particle data, drawing the lens, source, and all intervening resolved objects self-consistently from the simulated large-scale structure. Our approach combines a structure-preserving remapping of the simulation volume into a lensing-appropriate geometry with multi-plane ray tracing, enabling the use of uniform simulation boxes that resolve both cluster-scale primary lenses and high-redshift source galaxies. We demonstrate the method by generating example light cones and images using IllustrisTNG data, then use these results to conservatively quantify the impact of LoS structure on image configurations and critical-curve morphology. We find that uncorrelated LoS structure can shift the relative positions of lensed images by several arcseconds, introduces a ∼ 6% scatter in the area of a cluster's primary critical curve, and changes the total critical area within 100^'' of the cluster potential minimum by 16^+20%_-14% at a source plane redshift of z_s=4.

The large-scale structure (LSS) of the Universe, characterized by the distribution of clusters, filaments, walls, and voids, encodes fundamental information about cosmology and structure formation. Standard cosmological analyses, based on abundances as well as two-point and higher-order correlation functions, have been widely used to extract information from LSS. While powerful, these techniques can only access a subset of the full information content, leaving much of the encoded structure underexploited. This motivates the use of more flexible approaches capable of learning directly from the raw, graph-like nature of the cosmic web without relying on strong compression or dimensionality reduction. Recent advances in machine learning, particularly graph neural networks (GNNs), provide a natural framework for this task by capturing the non-Euclidean connectivity of cosmic structures. Building on these developments, recent progress in quantum machine learning (QML) offers an intriguing opportunity to further enhance representation power through quantum-enhanced models. In this work, we investigate the potential of quantum graph neural networks (QGNNs) as an alternative computational framework for analyzing LSS, focusing on structure identification and classification with an emphasis on distinguishing clusters and voids. Leveraging the hybrid quantum-classical paradigm implemented in PennyLane, we construct variational quantum circuits to encode graph representations of cosmological data and compare their performance against state-of-the-art classical GNNs. Our study evaluates the accuracy and expressivity of QGNNs relative to classical models, with particular attention to their ability to capture complex correlations in sparse, high-dimensional astrophysical datasets. Preliminary results indicate that QGNNs achieve comparable classification performance, while highlighting both the opportunities and current limitations of quantum-enhanced approaches in cosmological applications.

In this series of papers, we present dynamical models of cluster members in strong lensing (SL) galaxy clusters to independently probe the persistent discrepancy reported between SL models and cosmological simulations, in terms of total mass properties for the cluster subhalos. In this work, we focused our study on early-type galaxies within Abell 2744 (z=0.309) and MACS J0416.1-2403 (z=0.397). We took advantage of deep MUSE spectroscopic data, complemented with HFF photometry. We used a pipeline based on spectral fitting to perform kinematic measurements of the LOS velocity dispersion profiles of 109 cluster members. We modeled the galaxies assuming a dPIE total mass density distribution and a Jaffe stellar mass density distribution. From the models, we inferred the values of the central stellar velocity dispersion, σ_0, and the truncation radius, r_t, for the galaxies in our sample. We found that σ_0 is accurately recovered for all of the cluster members, while r_t is reliably measured for a fraction of galaxies in our sample, with sufficiently extended radial kinematic coverage. Our dynamical models predicted LOS velocity dispersion profiles that fit the measured ones better than those inferred from SL models. We then exploited the σ_0 measurements obtained from the dynamical models to calibrate the Faber-Jackson scaling relations for the cluster members in both galaxy clusters. When comparing our relations to those obtained in previous kinematics and SL works, we found systematically higher normalization and compatible slope and scatter values. We conclude that our dynamical measurements of σ_0 and r_t, along with calibrated scaling relations, are more robust than previous kinematic estimates which are biased by not taking into account the effects of the PSF, and should therefore be adopted as improved initial prescriptions in future SL models.

Variations in dynamical states of galaxy clusters can introduce biases and scatter in observable-mass relations. The dynamical state of a cluster is an emergent feature of its mass accretion history (MAH), it is therefore useful to constrain the MAH of the cluster. In this work, we characterize 305 massive clusters from The300 project by connecting features from their projected stellar distributions to their mass accretion histories (MAH). As a baseline, we first correlate host dark matter halo dynamical state indicators at z=0 with their MAH via the Spearman rank correlation coefficient ρ_sp. Both substructure mass fraction and center-of-mass offset measurements correlate strongly with the MAH measured between 0.1≲ z≲ 1. We repeat this exercise with morphological measurements of projected stellar density maps, many of which exhibit moderate correlation strength with different times in the MAH. Broadly, core morphological measurements (r ≤ 30 kpc) correlate better with early-time MAH. Core-excised (50 kpc≤ r ≤ 1 Mpc) morphological measurements correlate better with late-time MAH. We further quantify the MAH prediction power of both traditional dynamical state indicators and morphological parameters using Multivariable Conditional Abundance Matching (MultiCAM). MultiCAM employs simple rank-ordering operations, making it straightforward to translate to observed datasets. We find reasonable (ρ_sp≥ 0.6) performance for predictions of the mass fraction between 1≲ z≲ 0.1, though with notable information loss when using projected quantities. In one example application of our methodology, we use the coefficients of the MultiCAM models to select subsamples of galaxy clusters that have accreted more (or less) of their z = 0 mass budget over a given time frame.

The mass of galaxy clusters estimated from weak-lensing observations is affected by projection effects, leading to a systematic underestimation compared to the true cluster mass, varying with both mass and redshift. The magnitude depends on the criteria used to select clusters and the spatial scale over which their mass is measured. We leverage hydrodynamical simulations of galaxy clusters carried out with GadgetX and GIZMO-SIMBA as part of the Three Hundred project. We used them to quantify weak-lensing mass biases with respect also to the results from dark matter-only simulations. We also investigate how the biases propagate into the richness-mass relation. We aim to shed light on the effect of the presence of baryons on the weak-lensing mass bias and also whether this bias depends on the galaxy formation recipe; we seek to model the richness-mass relation that can be used as guidelines for observational experiments for cluster cosmology. We produced weak-lensing simulations of random projections to model the expected excess surface mass density profile of clusters up to redshift z=1. We then estimated the observed richness by counting the number of galaxies in a cylinder and correcting by projected contaminants. We derived the weak-lensing mass-richness relation and found consistency across hydrodynamical simulations. The intercept parameter of the relation is independent of redshift but varies with the minimum of the stellar mass to define the richness. At the same time, the slope is relatively constant up to z=0.55. The scatter in observed richness at a fixed weak-lensing mass increases linearly with redshift at a fixed stellar mass cut. As expected, we observed that the scatter in richness at a given true mass is smaller than at a given weak-lensing mass. Our results for the weak-lensing mass-richness relation align well with SDSS redMaPPer cluster analyses. [Abridged]

We present a new parametric strong-lensing analysis of the galaxy cluster MACS J0138.0-2155 at z(L) = 0.336. This is the first lens cluster known to show two multiply imaged supernova (SN) siblings, SN Requiem and SN Encore at z(SNe) = 1.949, enabling a new measurement of the Hubble constant value from the measured relative time delays. We exploited Hubble Space Telescope and James Webb Space Telescope multi-band imaging in synergy with new Multi Unit Spectroscopic Explorer follow-up spectroscopy to develop an improved lens mass model. Specifically, we included 84 cluster members (similar to 60% of which are spectroscopically confirmed) and two perturber galaxies along the line of sight. Our observables consisted of 23 spectroscopically confirmed multiple images from eight background sources, spanning a fairly wide redshift range from 0.767 to 3.420. To accurately characterise the sub-halo mass component, we calibrated the Faber-Jackson scaling relation based on the stellar kinematic measurements of a subset of 14 bright cluster galaxies. We built several lens models by implementing different cluster total mass parametrisations to assess the statistical and systematic uncertainties on the predicted values of the position and magnification of the observed and future multiple images of SN Requiem and SN Encore. Our reference best-fit lens model reproduces the observed positions of the multiple images with a root mean square offset of 0(.)('')36 and the multiple-image positions of the SNe and their host galaxy with a remarkable mean precision of only 0(.)('')05. We measure a projected total mass of M(<60 kpc) = 2.89(-0.03)(+0.04) x 10(13) M-circle dot, which is consistent with that independently derived from the X-ray analysis of archival data from the Chandra observatory. We also demonstrate the reliability of the new lens model by reconstructing the extended surface-brightness distribution of the multiple images of the host galaxy. The significant discrepancy between the magnification values predicted by our reference model and those from previous studies, which is critical for understanding the intrinsic properties of the two SNe and their host galaxy, further underscores the need to combine cutting-edge observations with a detailed lens modelling.

Context. The Euclid mission of the European Space Agency will deliver weak gravitational lensing and galaxy clustering surveys that can be used to constrain the standard cosmological model and extensions thereof. Aims. We present forecasts from the combination of the Euclid photometric galaxy surveys (weak lensing, galaxy clustering, and their crosscorrelations) and its spectroscopic redshift survey with respect to their sensitivity to cosmological parameters. We include the summed neutrino mass, Sigma m (v), and the e ffective number of relativistic species, N-e ff, in the standard Lambda alpha CDM scenario and in the dynamical dark energy (w (0) w(alpha)CDM) scenario. Methods. We compared the accuracy of di fferent algorithms predicting the non-linear matter power spectrum for such models. We then validated several pipelines for Fisher matrix and Markov chain Monte Carlo (MCMC) forecasts, using di fferent theory codes, algorithms for numerical derivatives, and assumptions on the non-linear cut-o ff scale. Results. The Euclid primary probes alone will reach a sensitivity of sigma(Sigma m (v) = 60 meV) = 56 meV in the Lambda CDM +Sigma m (v) model, whereas the combination with cosmic microwave background (CMB) data from Planck is expected to achieve sigma(Sigma m (v)) = 23 meV, o ffering evidence of a non-zero neutrino mass to at least the 2:6 sigma level. This could be pushed to a 4 sigma detection if future CMB data from LiteBIRD and CMB Stage-IV were included. In combination with Planck, Euclid will also deliver tight constraints on Delta N-e ff < 0:144 (95%CL) in the Lambda CDM +Sigma m (v)+N-e ff model or even Delta N-e ff < 0:063 when future CMB data are included. When floating the dark energy parameters, we find that the sensitivity to Ne ff remains stable, but for Sigma m (v), it gets degraded by up to a factor of 2, at most. Conclusions. This work illustrates the complementarity among the Euclid spectroscopic and photometric surveys and among Euclid and CMB constraints. Euclid will o ffer great potential in measuring the neutrino mass and excluding well-motivated scenarios with additional relativistic particles.

We provide an early assessment of the imaging capabilities of the Euclid space mission to probe deeply into nearby star-forming regions and associated very young open clusters, and in particular to check to what extent it can shed light on the new-born free-floating planet population. This paper focuses on a low-reddening region observed in just one Euclid pointing where the dust and gas has been cleared out by the hot sigma Orionis star. One late-M and six known spectroscopically confirmed L-type substellar members in the sigma Orionis cluster are used as benchmarks to provide a high-purity procedure to select new candidate members with Euclid. The exquisite angular resolution and depth delivered by the Euclid instruments allow us to focus on bona-fide point sources. A cleaned sample of sigma Orionis cluster substellar members has been produced and the initial mass function (IMF) has been estimated by combining Euclid and Gaia data. Our sigma Orionis substellar IMF is consistent with a power-law distribution with no significant steepening at the planetary-mass end. No evidence of a low-mass cutoff is found down to about 4 Jupiter masses at the young age (3 Myr) of the sigma Orionis open cluster.

This paper presents a search for high redshift galaxies from the Euclid Early Release Observations program `Magnifying Lens.' The 1.5\,$ area covered by the twin Abell lensing cluster fields is comparable in size to the few other deep near-infrared surveys such as COSMOS, and so provides an opportunity to significantly increase known samples of rare UV-bright galaxies at $z UV Beyond their still uncertain role in reionisation, these UV-bright galaxies are ideal laboratories from which to study galaxy formation and constrain the bright-end of the UV luminosity function. Of the sources detected from a combined and NISP detection image, 168 do not have any appreciable VIS/ flux. These objects span a range in spectral colours, separated into two classes: 139 extremely red sources; and 29 Lyman-break galaxy candidates. Best-fit redshifts and spectral templates suggest the former is composed of both $z dusty star-forming galaxies and $z quiescent systems. The latter is composed of more homogeneous Lyman-break galaxies at $z In both cases, contamination by L- and T-type dwarfs cannot be ruled out with images alone. Additional contamination from instrumental persistence is investigated using a novel time series analysis. This work lays the foundation for future searches within the Euclid Deep Fields, where thousands more $z Lyman-break systems and extremely red sources will be identified.

We present an analysis of Euclid observations of a 0.5 deg$^2$ field in the central region of the Fornax galaxy cluster that were acquired during the performance verification phase. With these data, we investigate the potential of Euclid for identifying GCs at 20 Mpc, and validate the search methods using artificial GCs and known GCs within the field from the literature. Our analysis of artificial GCs injected into the data shows that Euclid's data in $I_{\rm E}$ band is 80% complete at about $I_{\rm E} \sim 26.0$ mag ($M_{V\rm } \sim -5.0$ mag), and resolves GCs as small as $r_{\rm h} = 2.5$ pc. In the $I_{\rm E}$ band, we detect more than 95% of the known GCs from previous spectroscopic surveys and GC candidates of the ACS Fornax Cluster Survey, of which more than 80% are resolved. We identify more than 5000 new GC candidates within the field of view down to $I_{\rm E}$ mag, about 1.5 mag fainter than the typical GC luminosity function turn-over magnitude, and investigate their spatial distribution within the intracluster field. We then focus on the GC candidates around dwarf galaxies and investigate their numbers, stacked luminosity distribution and stacked radial distribution. While the overall GC properties are consistent with those in the literature, an interesting over-representation of relatively bright candidates is found within a small number of relatively GC-rich dwarf galaxies. Our work confirms the capabilities of Euclid data in detecting GCs and separating them from foreground and background contaminants at a distance of 20 Mpc, particularly for low-GC count systems such as dwarf galaxies.

As part of the Early Release Observations (ERO) programme, we analysed deep, wide-field imaging from the VIS and NISP instruments of two Milky Way globular clusters (GCs), namely NGC 6254 (M10) and NGC 6397, to look for observational evidence of their dynamical interaction with the Milky Way. We searched for such an interaction in the form of structural and morphological features in the clusters' outermost regions, which would be suggestive of the development of tidal tails on scales larger than those sampled by the ERO data. From our multi-band photometric analysis, we obtained deep and well-behaved colour--magnitude diagrams that, in turn, enabled an accurate membership selection. The surface brightness profiles built from these samples of member stars are the deepest ever obtained for these two Milky Way GCs, reaching down to $ mag/arcsec$^2$, which is $ mag/arcsec$^2$ lower than before. The investigation of the two-dimensional density map of NGC 6254 reveals an elongated morphology of the cluster peripheries in the direction and with the amplitude predicted by $N$-body simulations of the cluster's dynamical evolution, at high statistical significance. We interpret this as strong evidence for the first detection of tidally induced morphological distortion around this cluster. The density map of NGC 6397 reveals a slightly elliptical morphology, in agreement with previous studies, which requires further investigation on larger scales to be properly interpreted. This ERO project thus demonstrates the power of in studying the outer regions of GCs at an unprecedented level of detail, thanks to the combination of the large field of view, high spatial resolution, and depth enabled by the telescope. Our results highlight the future survey as the ideal dataset for investigating GC tidal tails and stellar streams.

Gravitational redshift and Doppler effects give rise to an antisymmetric component of the galaxy correlation function when cross-correlating two galaxy populations or two different tracers. In this paper, we assess the detectability of these effects in the Euclid spectroscopic galaxy survey. We model the impact of gravitational redshift on the observed redshift of galaxies in the Flagship mock catalogue using a Navarro-Frenk-White profile for the host haloes. We isolate these relativistic effects, largely subdominant in the standard analysis, by splitting the galaxy catalogue into two populations of faint and bright objects and estimating the dipole of their cross-correlation in four redshift bins. In the simulated catalogue, we detect the dipole signal on scales below 30 h(-1) Mpc, with detection significances of 4 sigma and 3 sigma in the two lowest redshift bins, respectively. At higher redshifts, the detection significance drops below 2 sigma. Overall, we estimate the total detection significance in the Euclid spectroscopic sample to be approximately 6 sigma. We find that on small scales, the major contribution to the signal comes from the nonlinear gravitational potential. Our study on the Flagship mock catalogue shows that this observable can be detected in Euclid Data Release 2 and beyond.

Extragalactic globular clusters (EGCs) are an abundant and powerful tracer of galaxy dynamics and formation, and their own formation and evolution is also a matter of extensive debate. The compact nature of globular clusters means that they are hard to spatially resolve and thus study outside the Local Group. In this work we have examined how well EGCs will be detectable in images from the Euclid telescope, using both simulated pre-launch images and the first early-release observations of the Fornax galaxy cluster. The Euclid Wide Survey will provide high-spatial resolution VIS imaging in the broad IE band as well as near-infrared photometry (YE, JE, and HE). We estimate that the galaxies within 100 Mpc in the footprint of the Euclid survey host around 830 000 EGCs of which about 350 000 are within the survey's detection limits. For about half of these EGCs, three infrared colours will be available as well. For any galaxy within 50Mpc the brighter half of its GC luminosity function will be detectable by the Euclid Wide Survey. The detectability of EGCs is mainly driven by the residual surface brightness of their host galaxy. We find that an automated machine-learning EGC-classification method based on real Euclid data of the Fornax galaxy cluster provides an efficient method to generate high purity and high completeness GC candidate catalogues. We confirm that EGCs are spatially resolved compared to pure point sources in VIS images of Fornax. Our analysis of both simulated and first on-sky data show that Euclid will increase the number of GCs accessible with high-resolution imaging substantially compared to previous surveys, and will permit the study of GCs in the outskirts of their hosts. Euclid is unique in enabling systematic studies of EGCs in a spatially unbiased and homogeneous manner and is primed to improve our understanding of many understudied aspects of GC astrophysics.