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Quantum computing holds the promise of solving classically intractable problems. Enabling this requires scalable and hardware-efficient quantum processors with vanishing error rates. This perspective manuscript describes how bosonic codes, particularly grid state encodings, offer a pathway to scalable fault-tolerant quantum computing in superconducting circuits. By leveraging the large Hilbert space of bosonic modes, quantum error correction can operate at the single physical unit level, therefore reducing drastically the hardware requirements to bring fault-tolerant quantum computing to scale. Going beyond the well-known Gottesman-Kitaev-Preskill (GKP) code, we discuss how using multiple bosonic modes to encode a single qubit offers increased protection against control errors and enhances its overall error-correcting capabilities. Given recent successful demonstrations of critical components of this architecture, we argue that it offers the shortest path to achieving fault tolerance in gate-based quantum computing processors with a MHz logical clock rate., Comment: This is a perspective paper presenting Nord Quantique's vision towards hardware-efficient FTQC

Super-resolution (SR) models in cosmological simulations use deep learning (DL) to rapidly enhance low-resolution (LR) runs with statistically correct fine details. These models preserves large-scale structures by conditioning on an LR version of the simulation. On smaller scales, the generative process is inherently stochastic, producing multiple possible SR realizations with distinct small-scale structures. Validation of reconstructed SR runs from LR simulations requires ensuring that specific statistics of interest are accurately reproduced by comparing SR outputs with target high resolution (HR) runs. In this study, we develop an emulator designed to reproduce the small-scale structures of target HR simulation with high fidelity. By processing an SR realization alongside the high-resolution initial condition (HRIC), we transform the SR output to emulate the result of a full simulation with that HRIC. By comparing various metrics, from visualization to individual halo measurements, we demonstrate that the emulated SR runs closely align with the target HR simulation, even at length scales an order of magnitude smaller than the corresponding LR run. These results show the potential of this method for efficiently generating accurate simulations and mock observations for large galaxy surveys., Comment: 18 pages, 16 figures

Supermassive black holes (SMBHs) can be ejected from their galactic centers due to gravitational wave recoil or the slingshot mechanism following a galaxy merger. If an ejected SMBH retains its inner accretion disk, it may be visible as an off-nuclear active galactic nucleus (AGN). At present, only a handful of offset AGNs that are recoil or slingshot candidates have been found, and none have been robustly confirmed. Compiling a large sample of runaway SMBHs would enable us to constrain the mass and spin evolution of binary SMBHs and study feedback effects of displaced AGNs. We adapt the method of varstrometry -- which was developed for Gaia observations to identify off-center, dual, and lensed AGNs -- in order to quickly identify off-nuclear AGNs in optical survey data by looking for an excess of blue versus red astrometric jitter. We apply this to the Pan-STARRS1 3$\pi$ Survey and report on five new runaway AGN candidates. We focus on ZTF18aajyzfv: a luminous quasar offset by 6.7 $\pm$ 0.2 kpc from an adjacent galaxy at $z$=0.224, and conclude after Keck LRIS spectroscopy and comparison to ASTRID simulation analogs that it is likely a dual AGN. This selection method can be easily adapted to work with data from the soon-to-be commissioned Vera C. Rubin Telescope Legacy Survey of Space and Time (LSST). LSST will have a higher cadence and deeper magnitude limit than Pan-STARRS1, and should permit detection of many more runaway SMBH candidates., Comment: Accepted for publication by ApJ. 13 pages, 12 figures, 3 tables

We present mock JWST observations for more than 250,000 different galaxies from the Astrid simulation with $3 \leq z \leq 6$. The mock observations are made using the BPASS stellar SED model, and a simple dust model. They are then viewed through NIRCam filters, convolved with a PSF, have noise added, and are drizzled together to emulate the Cosmic Evolution Early Release Science (CEERS) survey. We analyse this dataset by computing a number of morphological measures and find our catalog to have comparable statistics to similar mock catalogs, and the first release of CEERS data. We find that most of the Sersic indices of galaxies in our redshift range are lower than observed, with most having n less than one. Additionally, we observe the sizes of galaxies of all masses to increase from redshift z=6 to redshift z=3 consistent with other results. The number of galaxies in our catalog allows us to examine how relationships like the mass-size relation evolve with redshift, and compare the accuracy of a variety of traditional galaxy classification techniques (Sersic fit, Asymmetry-Concentration, and Gini-$M_{20}$) within our redshift range. We find the mass-size relation to be nearly flat at redshift z=6, and consistently increases as redshift decreases, and find the galaxy classification methods have minimal correlation with each other in our redshift range. We also investigate the impact that different stages of our imaging pipeline have on these morphological measures to determine how robust mock catalogs are to different choices at each step. Finally, we test the addition of incorporating light from AGNs into our pipeline and find that while the population of galaxies that have significant AGN luminosity is low, those galaxies do tend to have higher Sersic indices once the AGN luminosity is added, rectifying some of the systematic bias towards lower Sersic indices present in our dataset., Comment: 17 pages, 14 figures

We study dual AGN host galaxy morphologies at $z=2$ using the ASTRID simulation, selecting black hole (BH) pairs with small separation ($\Delta r<30\rm{kpc}$), high mass ($M_{\text{BH,12}}>10^7M_\odot$), and luminosity ($L_{\text{bol,12}}>10^{43}\rm{erg/s}$). We kinematically decompose (using MORDOR) $\sim1000$ dual AGN hosts into standard components - a `disk' (thin and thick disk, pseudo-bulge) and 'bulge' (bulge and halo) and define disk-dominated galaxies by the disk-to-total $D/T\geq0.5$. In ASTRID, $60.9\pm2.1\%$ of dual AGN hosts (independent of separation) are disk-dominated, with the $D/T$ distribution peaking at $\sim0.7$. Notably, hosts of BH pairs have similar morphologies (most either both disk or bulge-dominated). In dual-AGN hosts, the $D/T$ increases from $\sim17\% $ at $M_{\rm *}\sim 10^{9} M_{\odot}$ to $ 64\% $ for $M_{\rm *} \sim 10^{11.5} M_{\odot}$, and the pseudo-bulge is the dominant component of the disk fraction at the high mass end. Moreover, dual AGN hosts exhibit a higher fraction of disk/large pseudo-bulge than single-AGN hosts. The Disk-to-Total ratio is approximately constant with BH mass or AGN luminosity. We also create mock images of dual AGN host galaxies, employing morphological fitting software Statmorph to calculate morphological parameters and compare them with our kinematic decomposition results. Around $83.3\pm2.4\%$ of galaxies display disk-like profiles, of which $\sim60.7\pm2.2\%$ are kinematically confirmed as disks. Se\'rsic indices and half-mass radii of dual AGN host galaxies align with observational measurements from HST at $z\sim2$. Around $34\%$ are identified as mergers from the $\text{Gini}-M_{20}$ relation. We find two dual AGN hosted by galaxies that exhibit disk-like se\'rsic index $n_{12}<1$ and $(D/T)_{12}>0.5$, which are in remarkable agreement with properties of recently discovered dual quasars in disk galaxies at $z\sim 2$., Comment: 17 pages, 14 figures, submitted to the Open Journal of Astrophysics

The Gottesman-Kitaev-Preskill (GKP) code encodes a logical qubit into a bosonic system with resilience against single-photon loss, the predominant error in most bosonic systems. Here we present experimental results demonstrating quantum error correction of GKP states based on reservoir engineering of a superconducting device. Error correction is made autonomous through an unconditional reset of an auxiliary transmon qubit. The lifetime of the logical qubit is shown to be increased from quantum error correction, therefore reaching the point at which more errors are corrected than generated., Comment: 6 pages, 3 figures + 26 pages, 12 figures

Biases in large-scale image datasets are known to influence the performance of computer vision models as a function of geographic context. To investigate the limitations of standard Internet data collection methods in low- and middle-income countries, we analyze human-centric image geo-diversity on a massive scale using geotagged Flickr images associated with each nation in Africa. We report the quantity and content of available data with comparisons to population-matched nations in Europe as well as the distribution of data according to fine-grained intra-national wealth estimates. Temporal analyses are performed at two-year intervals to expose emerging data trends. Furthermore, we present findings for an ``othering'' phenomenon as evidenced by a substantial number of images from Africa being taken by non-local photographers. The results of our study suggest that further work is required to capture image data representative of African people and their environments and, ultimately, to improve the applicability of computer vision models in a global context., Comment: 35 pages, AI, Ethics, and Society Conference (AIES'23)

We look for simulated star-forming linear wakes such as the one recently discovered by van Dokkum et al. (2023) in the cosmological hydrodynamical simulation ASTRID. Amongst the runaway black holes in ASTRID, none are able to produce clear star-forming wakes. Meanwhile, fly-by encounters, typically involving a compact galaxy (with a central black hole) and a star-forming galaxy (with a duo of black holes) reproduce remarkably well many of the key properties (its length and linearity; recent star formation, etc.) of the observed star-forming linear feature. We predict the feature to persist for approximately 100 Myr in such a system and hence constitute a rare event. The feature contains a partly stripped galaxy (with $M_{\rm gal}=10^9 \sim 10^{10}M_\odot$) and a dual BH system ($M_{\rm BH}=10^5 \sim 10^7\,M_\odot$) in its brightest knot. X-ray emission from AGN in the knot should be detectable in such systems. After $100\sim 200\,{\rm Myrs}$ from the first fly-by, the galaxies merge leaving behind a triple black hole system in a (still) actively star-forming early-type remnant of mass $\sim 5\times 10^{10}\,M_\odot$. Follow-up JWST observations may be key for revealing the nature of these linear features by potentially detecting the older stellar populations constituting the bright knot. Confirmation of such detections may therefore help discriminate a fly-by encounter from a massive BH wake to reveal the origin of such features., Comment: 8 pages, 5 figures, comments welcome

In this work, we extend our recently developed super-resolution (SR) model for cosmological simulations to produce fully time consistent evolving representations of the particle phase-space distribution. We employ a style-based constrained generative adversarial network (Style-GAN) where the changing cosmic time is an input style parameter to the network. The matter power spectrum and halo mass function agree well with results from high-resolution N-body simulations over the full trained redshift range ($10 \le z \le 0$). Furthermore, we assess the temporal consistency of our SR model by constructing halo merger trees. We examine progenitors, descendants and mass growth along the tree branches. All statistical indicators demonstrate the ability of our SR model to generate satisfactory high-resolution simulations based on low-resolution inputs., Comment: 12 pages, 11 figures, code and movie available in https://github.com/sagasv5-xw/map2map on styled srsgan branch

This report summarizes the work carried out by the authors during the Twelfth Montreal Industrial Problem Solving Workshop, held at Universit\'e de Montr\'eal in August 2022. The team tackled a problem submitted by CBC/Radio-Canada on the theme of Automatic Text Simplification (ATS).

AI super-resolution, combining deep learning and N-body simulations has been shown to successfully reproduce the large scale structure and halo abundances in the Lambda Cold Dark Matter cosmological model. Here, we extend its use to models with a different dark matter content, in this case Fuzzy Dark Matter (FDM), in the approximation that the difference is encoded in the initial power spectrum. We focus on redshift z = 2, with simulations that model smaller scales and lower masses, the latter by two orders of magnitude, than has been done in previous AI super-resolution work. We find that the super-resolution technique can reproduce the power spectrum and halo mass function to within a few percent of full high resolution calculations. We also find that halo artifacts, caused by spurious numerical fragmentation of filaments, are equally present in the super-resolution outputs. Although we have not trained the super-resolution algorithm using full quantum pressure FDM simulations, the fact that it performs well at the relevant length and mass scales means that it has promise as technique which could avoid the very high computational cost of the latter, in some contexts. We conclude that AI super-resolution can become a useful tool to extend the range of dark matter models covered in mock catalogs., Comment: 7 pages, 4 figures

Mechanical ventilation is one of the most widely used therapies in the ICU. However, despite broad application from anaesthesia to COVID-related life support, many injurious challenges remain. We frame these as a control problem: ventilators must let air in and out of the patient's lungs according to a prescribed trajectory of airway pressure. Industry-standard controllers, based on the PID method, are neither optimal nor robust. Our data-driven approach learns to control an invasive ventilator by training on a simulator itself trained on data collected from the ventilator. This method outperforms popular reinforcement learning algorithms and even controls the physical ventilator more accurately and robustly than PID. These results underscore how effective data-driven methodologies can be for invasive ventilation and suggest that more general forms of ventilation (e.g., non-invasive, adaptive) may also be amenable., Comment: Machine Learning for Health (ML4H) at NeurIPS 2021 - Extended Abstract. arXiv admin note: substantial text overlap with arXiv:2102.06779

Magnonics is a field of science that addresses the physical properties of spin waves and utilizes them for data processing. Scalability down to atomic dimensions, operations in the GHz-to-THz frequency range, utilization of nonlinear and nonreciprocal phenomena, and compatibility with CMOS are just a few of many advantages offered by magnons. Although magnonics is still primarily positioned in the academic domain, the scientific and technological challenges of the field are being extensively investigated, and many proof-of-concept prototypes have already been realized in laboratories. This roadmap is a product of the collective work of many authors that covers versatile spin-wave computing approaches, conceptual building blocks, and underlying physical phenomena. In particular, the roadmap discusses the computation operations with Boolean digital data, unconventional approaches like neuromorphic computing, and the progress towards magnon-based quantum computing. The article is organized as a collection of sub-sections grouped into seven large thematic sections. Each sub-section is prepared by one or a group of authors and concludes with a brief description of the current challenges and the outlook of the further development of the research directions., Comment: 74 pages, 57 figures, 500 references

We investigate the potential of machine learning models for the prediction of visual improvement after macular hole surgery from preoperative data (retinal images and clinical features). Collecting our own data for the task, we end up with only 121 total samples, putting our work in the very limited data regime. We explore a variety of deep learning methods for limited data to train deep computer vision models, finding that all tested deep vision models are outperformed by a simple regression model on the clinical features. We believe this is compelling evidence of the extreme difficulty of using deep learning on very limited data., Comment: Machine Learning for Health (ML4H) - Extended Abstract

Cavity magnonics deals with the interaction of magnons - elementary excitations in magnetic materials - and confined electromagnetic fields. We introduce the basic physics and review the experimental and theoretical progress of this young field that is gearing up for integration in future quantum technologies. Much of its appeal is derived from the strong magnon-photon coupling and the easily-reached nonlinear regime in microwave cavities. The interaction of magnons with light as detected by Brillouin light scattering is enhanced in magnetic optical resonators, which can be employed to manipulate magnon distributions. The cavity photon-mediated coupling of a magnon mode to a superconducting qubit enables measurements in the single magnon limit., Comment: review article, 54 pages

In this work, we expand and test the capabilities of our recently developed super-resolution (SR) model to generate high-resolution (HR) realizations of the full phase-space matter distribution, including both displacement and velocity, from computationally cheap low-resolution (LR) cosmological N-body simulations. The SR model enhances the simulation resolution by generating 512 times more tracer particles, extending into the deeply non-linear regime where complex structure formation processes take place. We validate the SR model by deploying the model in 10 test simulations of box size 100 Mpc/h, and examine the matter power spectra, bispectra and 2D power spectra in redshift space. We find the generated SR field matches the true HR result at percent level down to scales of k ~ 10 h/Mpc. We also identify and inspect dark matter halos and their substructures. Our SR model generate visually authentic small-scale structures, that cannot be resolved by the LR input, and are in good statistical agreement with the real HR results. The SR model performs satisfactorily on the halo occupation distribution, halo correlations in both real and redshift space, and the pairwise velocity distribution, matching the HR results with comparable scatter, thus demonstrating its potential in making mock halo catalogs. The SR technique can be a powerful and promising tool for modelling small-scale galaxy formation physics in large cosmological volumes., Comment: 13 pages, 11 figures, published version

We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation: a controller must let air in and out of a sedated patient's lungs according to a trajectory of airway pressures specified by a clinician. Hand-tuned PID controllers and similar variants have comprised the industry standard for decades, yet can behave poorly by over- or under-shooting their target or oscillating rapidly. We consider a data-driven machine learning approach: First, we train a simulator based on data we collect from an artificial lung. Then, we train deep neural network controllers on these simulators.We show that our controllers are able to track target pressure waveforms significantly better than PID controllers. We further show that a learned controller generalizes across lungs with varying characteristics much more readily than PID controllers do.