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We present Benchpress, a benchmarking suite for evaluating the performance and range of functionality of multiple quantum computing software development kits. This suite consists of a collection of over $1000$ tests measuring key performance metrics for a wide variety of operations on quantum circuits comprised of up to $930$ qubits and $\mathcal{O}(10^{6})$ two-qubit gates, as well as an execution framework for running the tests over multiple quantum software packages in a unified manner. We give a detailed overview of the benchmark suite, its methodology, and generate representative results over seven different quantum software packages. The flexibility of the Benchpress framework allows for benchmarking that not only keeps pace with quantum hardware improvements but can preemptively gauge the quantum circuit processing costs of future device architectures. Being open-source, Benchpress ensures the transparency and verification of performance claims., Comment: 11 pages, 4 figures, 2 tables

How and where the first generation of inner solar system planetesimals formed remains poorly understood. Potential formation regions are the silicate condensation line and water-snowline of the solar protoplanetary disk. Whether the chemical compositions of these planetesimals align with accretion at the silicate condensation line (water-free and reduced) or water-snowline (water-bearing and oxidized) is, however, unknown. Here we use Fe/Ni and Fe/Co ratios of magmatic iron meteorites to quantify the oxidation states of the earliest planetesimals associated with non-carbonaceous (NC) and carbonaceous (CC) reservoirs, representing the inner and outer solar system, respectively. Our results show that the earliest NC planetesimals contained substantial amounts of oxidized Fe in their mantles (3-19 wt% FeO). In turn, we argue that this required the accretion of water-bearing materials into these NC planetesimals. The presence of substantial quantities of moderately and highly volatile elements in their parent cores is also inconsistent with their accretion at the silicate condensation line and favors instead their formation at or beyond the water-snowline. Similar oxidation states in the early-formed parent bodies of NC iron meteorites and those of NC achondrites and chondrites with diverse accretion ages suggests that the formation of oxidized planetesimals from water-bearing materials was widespread in the early history of the inner solar system., Comment: 28 pages, 3 main figures, 6 Extended data figures, 4 Extended data tables

Entangled graph states can be used for quantum sensing and computing applications. Error correction in measurement-based quantum computing schemes will require the construction of cluster states in at least 3 dimensions. Here we generate 1-, 2-, 3-, and 4-dimensional optical frequency-mode cluster states by sending broadband 2-mode vacuum-squeezed light through an electro-optical modulator (EOM) driven with multiple frequencies. We create the squeezed light using 4-wave mixing in Rb atomic vapor and mix the sideband frequencies (qumodes) using an EOM, as proposed by Zhu et al. (1), producing a pattern of entanglement correlations that constitute continuous-variable graph states containing up to several hundred qumodes. We verify the entanglement structure by using homodyne measurements to construct the covariance matrices and evaluate the nullifiers. This technique enables scaling of optical cluster states to multiple dimensions without increasing loss.

Fast radio bursts (FRBs) are mysterious astrophysical transients whose origin and mechanism remain unclear. Compact object mergers may be a promising channel to produce some FRBs. Neutron star-black hole (NSBH) mergers could produce FRBs through mechanisms involving neutron star tidal disruption or magnetospheric disturbances. This could present an opportunity for multi-messenger gravitational-wave observations, providing new insight into the nature of FRBs and nuclear matter. However, some of the gravitational-wave signals may be marginal detections with signal-to-noise ratios < 8 or have large sky location and distance uncertainties, making it less straightforward to confidently associate an FRB with the gravitational-wave signal. One must therefore take care to avoid a false positive association. We demonstrate how to do this with simulated data. We calculate the posterior odds -- a measurement of our relative belief for a common versus unrelated origin of a coincident NSBH and FRB. We find that a coincident FRB+NSBH from a common source can yield a statistically significant posterior odds in a network with at least two observatories, but only if we require a coincidence in time and and sky location, rather than time alone. However, we find that for our model, we require a network signal-to-noise ratio greater than 10 to be confident in the common-source detection, when using a threshold of ln odds > 8. We suggest that a coincident NSBH+FRB detection could help distinguish between FRB engines by discriminating between disrupting and non-disrupting models., Comment: 15 pages, 7 figures, 2 tables

Radio frequency (rf) receivers using Rydberg atoms offer appealing features over classical sensors, such as their size, frequency tuning range, and lack of field absorption. In this work, we extend the application space by demonstrating a Rydberg atom rf polarimeter. Using rf heterodyne with three independent and orthogonal local oscillators, we are able to extract the polarization ellipse of the field in three dimensions, in addition to the total amplitude of the field. We use the relative phases and amplitudes of the three generated heterodyne signals to measure the field amplitudes and phases along three cardinal axes giving the full three-dimensional polarization. We demonstrate this polarization measurement for incoming fields at different angles around the sensor. Lastly, we investigate the reception of data symbols encoded in the horizontal and vertical signal field polarizations and the phase between them. Our measurements yield an amplitude noise of 57 $\mu$V/m/$\sqrt{\text{Hz}}$ for horizontal polarization, 66 $\mu$V/m/$\sqrt{\text{Hz}}$ for vertical polarization, and a standard deviation of 0.094 rad in the phase between the field components., Comment: 10 pages, 6 figures

Structural superlubricity is an intriguing physical phenomenon, whereby sliding at a structurally incommensurate, atomically flat interface yields vanishingly small friction forces. Despite its recent experimental validation, critical questions remain regarding the physical limitations of the concept. In particular, it is not known whether the ultra-low friction state would persist at high sliding speeds relevant for practical, small-scale mechanical systems. Here, we perform sliding experiments via atomic force microscopy on gold nanoislands on graphite at increasing speeds, extracting interfacial friction forces under ambient conditions. A heterodyne detection methodology enables the extraction of extremely weak friction signals buried deep in the noise, revealing that the structurally superlubric regime extends over 100,000 nm/s with minimal changes in friction force, spanning three orders of magnitude in sliding speed. Our results contribute significantly to the pursuit of functional, superlubric mechanical devices.

Quantum sensors represent a new generation of sensors with improved precision, accuracy, stability, and robustness to environmental effects compared to their classical predecessors. After decades of laboratory development, several types of quantum sensors are now commercially available or are part-way through the commercialization process. This article provides a brief description of the operation of a selection of quantum sensors that employ the principles of atom-light interactions and discusses progress toward packaging those sensors into products. This article covers quantum inertial and gravitational sensors, including gyroscopes, accelerometers, gravimeters, and gravity gradiometers that employ atom interferometry, nuclear magnetic resonance gyroscopes, atomic and spin-defect magnetometers, and Rydberg electric field sensors., Comment: 96 pages

Dose-response prediction in cancer is an active application field in machine learning. Using large libraries of \textit{in-vitro} drug sensitivity screens, the goal is to develop accurate predictive models that can be used to guide experimental design or inform treatment decisions. Building on previous work that makes use of permutation invariant multi-output Gaussian Processes in the context of dose-response prediction for drug combinations, we develop a variational approximation to these models. The variational approximation enables a more scalable model that provides uncertainty quantification and naturally handles missing data. Furthermore, we propose using a deep generative model to encode the chemical space in a continuous manner, enabling prediction for new drugs and new combinations. We demonstrate the performance of our model in a simple setting using a high-throughput dataset and show that the model is able to efficiently borrow information across outputs.

Effective clustering of biomedical data is crucial in precision medicine, enabling accurate stratifiction of patients or samples. However, the growth in availability of high-dimensional categorical data, including `omics data, necessitates computationally efficient clustering algorithms. We present VICatMix, a variational Bayesian finite mixture model designed for the clustering of categorical data. The use of variational inference (VI) in its training allows the model to outperform competitors in term of efficiency, while maintaining high accuracy. VICatMix furthermore performs variable selection, enhancing its performance on high-dimensional, noisy data. The proposed model incorporates summarisation and model averaging to mitigate poor local optima in VI, allowing for improved estimation of the true number of clusters simultaneously with feature saliency. We demonstrate the performance of VICatMix with both simulated and real-world data, including applications to datasets from The Cancer Genome Atlas (TCGA), showing its use in cancer subtyping and driver gene discovery. We demonstrate VICatMix's utility in integrative cluster analysis with different `omics datasets, enabling the discovery of novel subtypes. \textbf{Availability:} VICatMix is freely available as an R package, incorporating C++ for faster computation, at \url{https://github.com/j-ackierao/VICatMix}.

In this work, we present empirical results regarding the feasibility of using offline large language models (LLMs) in the context of electronic design automation (EDA). The goal is to investigate and evaluate a contemporary language model's (Llama-2-7B) ability to function as a microelectronic Q & A expert as well as its reasoning, and generation capabilities in solving microelectronic-related problems. Llama-2-7B was tested across a variety of adaptation methods, including introducing a novel low-rank knowledge distillation (LoRA-KD) scheme. Our experiments produce both qualitative and quantitative results., Comment: 4 pages, 2 figures, 2 tables, The First IEEE International Workshop on LLM-Aided Design (LAD'24)

Stellar models indicate that the core compactness of a star, which is a common proxy for its explodability in a supernova, does not increase monotonically with the star's mass. Rather, the core compactness dips sharply over a range of carbon-oxygen core masses; this range may be somewhat sensitive to the star's metallicity and evolutionary history. Stars in this compactness dip are expected to experience supernovae leaving behind neutron stars, whereas stars on either side of this range are expected to form black holes. This results in a hypothetical mass range in which black holes should seldom form. Quantitatively, when applied to binary stripped stars, these models predict a dearth of binary black holes with component masses $\approx 10 M_\odot - 15 M_\odot$. The population of gravitational-wave signals indicates potential evidence for a dip in the distribution of chirp masses of merging binary black holes near $\approx 10 M_\odot - 12 M_\odot$. This feature could be linked to the hypothetical component mass gap described above, but this interpretation depends on what assumptions are made of the binaries' mass ratios. Here, we directly probe the distribution of binary black hole component masses to look for evidence of a gap. We find no evidence for this feature using data from the third gravitational-wave transient catalogue (GWTC-3). If this gap does exist in nature, we find that it is unlikely to be resolvable by the end of the current (fourth) LIGO-Virgo-KAGRA (LVK) observing run., Comment: 16 pages, 11 figures

In this paper, we introduce Dreamweaver, which belongs to a new class of auto-regressive decision-making models known as large reasoning models (LRMs). Dreamweaver is designed to improve 3D floorplanning in electronic design automation (EDA) via an architecture that melds advancements in sequence-to-sequence reinforcement learning algorithms. A significant advantage of our approach is its ability to effectively reason over large discrete action spaces, which is essential for handling the numerous potential positions for various functional blocks in floorplanning. Additionally, Dreamweaver demonstrates strong performance even when trained on entirely random trajectories, showcasing its capacity to leverage sub-optimal or non-expert trajectories to enhance its results. This innovative approach contributes to streamlining the integrated circuit (IC) design flow and reducing the high computational costs typically associated with floorplanning. We evaluate its performance against a current state-of-the-art method, highlighting notable improvements., Comment: Under review

As the catalogue of gravitational-wave transients grows, several entries appear "exceptional" within the population. Tipping the scales with a total mass of $\approx 150 M_\odot$, GW190521 likely contained black holes in the pair-instability mass gap. The event GW190814, meanwhile, is unusual for its extreme mass ratio and the mass of its secondary component. A growing model-building industry has emerged to provide explanations for such exceptional events, and Bayesian model selection is frequently used to determine the most informative model. However, Bayesian methods can only take us so far. They provide no answer to the question: does our model provide an adequate explanation for the data? If none of the models we are testing provide an adequate explanation, then it is not enough to simply rank our existing models - we need new ones. In this paper, we introduce a method to answer this question with a frequentist $p$-value. We apply the method to different models that have been suggested to explain GW190521: hierarchical mergers in active galactic nuclei and globular clusters. We show that some (but not all) of these models provide adequate explanations for exceptionally massive events like GW190521., Comment: 8 pages, 3 figures, 1 table

We describe Qiskit, a software development kit for quantum information science. We discuss the key design decisions that have shaped its development, and examine the software architecture and its core components. We demonstrate an end-to-end workflow for solving a problem in condensed matter physics on a quantum computer that serves to highlight some of Qiskit's capabilities, for example the representation and optimization of circuits at various abstraction levels, its scalability and retargetability to new gates, and the use of quantum-classical computations via dynamic circuits. Lastly, we discuss some of the ecosystem of tools and plugins that extend Qiskit for various tasks, and the future ahead.

General relativity (GR) has proven to be a highly successful theory of gravity since its inception. The theory has thrivingly passed numerous experimental tests, predominantly in weak gravity, low relative speeds, and linear regimes, but also in the strong-field and very low-speed regimes with binary pulsars. Observable gravitational waves (GWs) originate from regions of spacetime where gravity is extremely strong, making them a unique tool for testing GR, in previously inaccessible regions of large curvature, relativistic speeds, and strong gravity. Since their first detection, GWs have been extensively used to test GR, but no deviations have been found so far. Given GR's tremendous success in explaining current astronomical observations and laboratory experiments, accepting any deviation from it requires a very high level of statistical confidence and consistency of the deviation across GW sources. In this paper, we compile a comprehensive list of potential causes that can lead to a false identification of a GR violation in standard tests of GR on data from current and future ground-based GW detectors. These causes include detector noise, signal overlaps, gaps in the data, detector calibration, source model inaccuracy, missing physics in the source and in the underlying environment model, source misidentification, and mismodeling of the astrophysical population. We also provide a rough estimate of when each of these causes will become important for tests of GR for different detector sensitivities. We argue that each of these causes should be thoroughly investigated, quantified, and ruled out before claiming a GR violation in GW observations., Comment: Review article; 1 figure; 1 table; comments welcome

Gravitational-wave memory is a non-linear effect predicted by general relativity that remains undetected. We apply a Bayesian analysis framework to search for gravitational-wave memory using binary black hole mergers in LIGO-Virgo-KAGRA's third gravitational-wave transient catalogue. We obtain a Bayes factor of $\ln \text{BF}=0.01$, in favour of the no-memory hypothesis, which implies that we are unable to measure memory with currently available data. This is consistent with previous work, suggesting that a catalogue of $\mathcal{O}(2000)$ binary black hole mergers is needed to detect memory. We look for new physics by allowing the memory amplitude to deviate from the prediction of general relativity by a multiplicative factor $A$. We obtain an upper limit of $A<23$ ($95\%$ credibility)., Comment: 9 pages, 5 figures. Accepted for publication

We establish an explicit global spectral decomposition of shifted convolution sums and the second moment of automorphic L-functions for Maass forms with explicit integral transforms as well as explicit inversion formulae over every local field., Comment: 41 pages

Rowlinson et al. 2023 recently claimed the detection of a coherent radio flash 76.6 minutes after a short gamma-ray burst. They proposed that the radio emission may be associated with a long-lived neutron star engine. We show through theoretical and observational arguments that the coherent radio emission, if real and indeed associated with GRB 201006A and at the estimated redshift, is unlikely to be due to the collapse of the neutron star, ruling out a blitzar-like mechanism. Instead, we show if a long-lived engine was created, it must have been stable with the radio emission likely linked to the intrinsic magnetar activity. However, we find that the optical upper limits require fine-tuning to be consistent with a magnetar-driven kilonova: we show that neutron-star engines that do satisfy the optical constraints would have produced a bright kilonova afterglow that should already be observable by the VLA or MeerKAT (for ambient densities typical for short GRBs). Given the optical limits and the current lack of a kilonova afterglow, we instead posit that no neutron star survived the merger, and the coherent radio emission was produced far from a black hole central engine via mechanisms such as synchrotron maser or magnetic reconnection in the jet -- a scenario consistent with all observations. We encourage future radio follow-up to probe the engine of this exciting event and continued prompt radio follow-up of short GRBs., Comment: Submitted. 10 pages, 4 figures

We prove Sarnak's spherical density conjecture for the principal congruence subgroup of SL(n, Z) of arbitrary level. Applications include a complete version of Sarnak's optimal lifting conjecture for principal congruence subgroups of SL(n, Z), as well as a transfer of the density theorem to certain co-compact situations. The main ingredients are new lower bounds for Whittaker functions and strong estimates for the cardinality of ramified Kloosterman sets., Comment: 48 pages

BOLD, the Barcode of Life Data System, supports the acquisition, storage, validation, analysis, and publication of DNA barcodes, activities requiring the integration of molecular, morphological, and distributional data. Its pivotal role in curating the reference library of DNA barcodes, coupled with its data management and analysis capabilities, make it a central resource for biodiversity science. It enables rapid, accurate identification of specimens and also reveals patterns of genetic diversity and evolutionary relationships among taxa. Launched in 2005, BOLD has become an increasingly powerful tool for advancing understanding of planetary biodiversity. It currently hosts 17 million specimen records and 14 million barcodes that provide coverage for more than a million species from every continent and ocean. The platform has the long-term goal of providing a consistent, accurate system for identifying all species of eukaryotes. BOLD's integrated analytical tools, full data lifecycle support, and secure collaboration framework distinguish it from other biodiversity platforms. BOLD v4 brought enhanced data management and analysis capabilities as well as novel functionality for data dissemination and publication. Its next version will include features to strengthen its utility to the research community, governments, industry, and society-at-large.

It has become increasingly useful to answer questions in gravitational-wave astronomy using transdimensional models where the number of free parameters can be varied depending on the complexity required to fit the data. Given the growing interest in transdimensional inference, we introduce a new package for the Bayesian inference Library (Bilby) called tBilby. The tBilby package allows users to set up transdimensional inference calculations using the existing Bilby architecture with off-the-shelf nested samplers and/or Markov Chain Monte Carlo algorithms. Transdimensional models are particularly helpful when we seek to test theoretically uncertain predictions described by phenomenological models. For example, bursts of gravitational waves can be modelled using a superposition of N wavelets where N is itself a free parameter. Short pulses are modelled with small values of N whereas longer, more complicated signals are represented with a large number of wavelets stitched together. Other transdimensional models have found use describing instrumental noise and the population properties of gravitational-wave sources. We provide a few demonstrations of tBilby, including fitting the gravitational-wave signal GW150914 with a superposition of N sine-Gaussian wavelets. We outline our plans to further develop the tbilby code suite for a broader range of transdimensional problems., Comment: 12 pages, 7 figures

While advances continue to be made in model-based clustering, challenges persist in modeling various data types such as panel data. Multivariate panel data present difficulties for clustering algorithms because they are often plagued by missing data and dropouts, presenting issues for estimation algorithms. This research presents a family of hidden Markov models that compensate for the issues that arise in panel data. A modified expectation-maximization algorithm capable of handling missing not at random data and dropout is presented and used to perform model estimation.

This work presents a computer program that performs symmetry-unrestricted 3D nuclear time-dependent density function theory (DFT) calculations. The program features the augmented Lagrangian constraint in the static calculation. This allows for the calculation of the potential energy surface. In addition, the code includes the full energy density functionals derived from the Skyrme interaction, meaning that the time-even and time-odd tensor parts are included for the time-dependent calculations. The results of the hit3d code are carefully compared with the Sky3D and Ev8 programs. The testing cases include unconstrained DFT calculations for doubly magic nuclei, the constrained DFT + BCS calculations for medium-heavy nucleus 110Mo, and the dynamic applications for harmonic vibration and nuclear reactions., Comment: 18 pages, 3 figures, 6 tables

Solar energy is rapidly becoming a robust renewable energy source to conventional finite resources such as fossil fuels. It is harvested using interconnected photovoltaic panels, typically built with crystalline silicon cells, i.e. semiconducting materials that convert effectively the solar radiation into electricity. However, crystalline silicon is fragile and vulnerable to cracking over time or in predictive maintenance tasks, which can lead to electric isolation of parts of the solar cell and even failure, thus affecting the panel performance and reducing electricity generation. This work aims to developing a system for detecting cell cracks in solar panels to anticipate and alaert of a potential failure of the photovoltaic system by using computer vision techniques. Three scenarios are defined where these techniques will bring value. In scenario A, images are taken manually and the system detecting failures in the solar cells is not subject to any computationa constraints. In scenario B, an Edge device is placed near the solar farm, able to make inferences. Finally, in scenario C, a small microcontroller is placed in a drone flying over the solar farm and making inferences about the solar cells' states. Three different architectures are found the most suitable solutions, one for each scenario, namely the InceptionV3 model, an EfficientNetB0 model shrunk into full integer quantization, and a customized CNN architechture built with VGG16 blocks., Comment: 9 pages, 8 figures, 2 tables

The ringdown portion of a binary black hole merger consists of a sum of modes, each containing an infinite number of tones that are exponentially damped sinusoids. In principle, these can be measured as gravitational-waves with observatories like LIGO/Virgo/KAGRA, however in practice it is unclear how many tones can be meaningfully resolved. We investigate the consistency and resolvability of the overtones of the quadrupolar $\ell = m = 2$ mode by starting at late times when the gravitational waveform is expected to be well-approximated by the $\ell m n = 220$ tone alone. We present a Bayesian inference framework to measure the tones in numerical relativity data. We measure tones at different start times, checking for consistency: we classify a tone as stably recovered if and only if the 95\% credible intervals for amplitude and phase at time $t$ overlap with the credible intervals at all subsequent times. We test a set of tones including the first four overtones of the fundamental mode and the 320 tone and find that the 220 and 221 tones can be measured consistently with the inclusion of additional overtones. The 222 tone measurements can be stabilised when we include the 223 tone, but only in a narrow time window, after which it is too weak to measure. The 223 tone recovery appears to be unstable, and does not become stable with the introduction of the 224 tone. We find that $N=3$ tones can be stably recovered simultaneously. However, when analysing $N \geq 4$ tones, the amplitude of one tone is consistent with zero. Thus, within our framework, one can identify only $N=3$ tones with non-zero amplitude that are simultaneously stable., Comment: 14 pages, 8 figures, 2 tables. Published in PRD

This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems, addressing the lack of depth and precision in existing literature. It proposes a two-pronged approach: a thorough analysis of current algorithms and a novel, hierarchical taxonomy for precise categorization. The taxonomy is based on a tri-level hierarchy, starting with the methodology category and narrowing down to specific techniques. Such a framework allows for a structured and comprehensive classification of algorithms, assisting researchers in understanding the interrelationships among diverse algorithms and techniques. Covering a wide range of algorithms, this taxonomy first categorizes algorithms into four main analysis types: User and Item Similarity-Based Methods, Hybrid and Combined Approaches, Deep Learning and Algorithmic Methods, and Mathematical Modeling Methods, with further subdivisions into sub-categories and techniques. The paper incorporates both empirical and experimental evaluations to differentiate between the techniques. The empirical evaluation ranks the techniques based on four criteria. The experimental assessments rank the algorithms that belong to the same category, sub-category, technique, and sub-technique. Also, the paper illuminates the future prospects of big data techniques in recommendation systems, underscoring potential advancements and opportunities for further research in this field