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Transitioning from quantum computation on physical qubits to quantum computation on encoded, logical qubits can improve the error rate of operations, and will be essential for realizing valuable quantum computational advantages. Using a neutral atom quantum processor with 256 qubits, each an individual Ytterbium atom, we demonstrate the entanglement of 24 logical qubits using the distance-two [[4,2,2]] code, simultaneously detecting errors and correcting for lost qubits. We also implement the Bernstein-Vazirani algorithm with up to 28 logical qubits encoded in the [[4,1,2]] code, showing better-than-physical error rates. We demonstrate fault-tolerant quantum computation in our approach, guided by the proposal of Gottesman (2016), by performing repeated loss correction for both structured and random circuits encoded in the [[4,2,2]] code. Finally, since distance-two codes can correct qubit loss, but not other errors, we show repeated loss and error correction using the distance-three [[9,1,3]] Bacon-Shor code. These results begin to clear a path for achieving scientific quantum advantage with a programmable neutral atom quantum processor., Comment: 17 pages, 16 figures

Quantum information processing with neutral atoms relies on Rydberg excitation for entanglement generation. While the use of heavy divalent or open-shell elements, such as strontium or ytterbium, has benefits due to their optically active core and a variety of possible qubit encodings, their Rydberg structure is generally complex. For some isotopes in particular, hyperfine interactions are relevant even for highly excited electronic states. We employ multi-channel quantum defect theory to infer the Rydberg structure of isotopes with non-zero nuclear spin and perform non-perturbative Rydberg-pair interaction calculations. We find that due to the high level density and sensitivities to external fields, experimental parameters must be precisely controlled. Specifically in ${}^{87}$Sr, we study an intrinsic F\"orster resonance, unique to divalent atoms with hyperfine-split thresholds, which simultaneously provides line stability with respect to external field fluctuations and enhanced long-range interactions. Additionally, we provide parameters for pair states that can be effectively described by single-channel Rydberg series. The explored pair states provide exciting opportunities for applications in the blockade regime as well as for more exotic long-range interactions such as largely flat, distance-independent potentials., Comment: 12 pages, 7 figures

We propose a weakly-supervised framework for the semantic segmentation of circular-scan synthetic-aperture-sonar (CSAS) imagery. The first part of our framework is trained in a supervised manner, on image-level labels, to uncover a set of semi-sparse, spatially-discriminative regions in each image. The classification uncertainty of each region is then evaluated. Those areas with the lowest uncertainties are then chosen to be weakly labeled segmentation seeds, at the pixel level, for the second part of the framework. Each of the seed extents are progressively resized according to an unsupervised, information-theoretic loss with structured-prediction regularizers. This reshaping process uses multi-scale, adaptively-weighted features to delineate class-specific transitions in local image content. Content-addressable memories are inserted at various parts of our framework so that it can leverage features from previously seen images to improve segmentation performance for related images. We evaluate our weakly-supervised framework using real-world CSAS imagery that contains over ten seafloor classes and ten target classes. We show that our framework performs comparably to nine fully-supervised deep networks. Our framework also outperforms eleven of the best weakly-supervised deep networks. We achieve state-of-the-art performance when pre-training on natural imagery. The average absolute performance gap to the next-best weakly-supervised network is well over ten percent for both natural imagery and sonar imagery. This gap is found to be statistically significant., Comment: Submitted to the IEEE Journal of Oceanic Engineering

We demonstrate that it is possible to construct operators that stabilize the constraint-satisfying subspaces of computational problems in their Ising representations. We provide an explicit recipe to construct unitaries and associated measurements given a set of constraints. The stabilizer measurements allow the detection of constraint violations, and provide a route to recovery back into the constrained subspace. We call this technique ''quantum subspace correction". As an example, we explicitly investigate the stabilizers using the simplest local constraint subspace: Independent Set. We find an algorithm that is guaranteed to produce a perfect uniform or weighted distribution over all constraint-satisfying states when paired with a stopping condition: a quantum analogue of partial rejection sampling. The stopping condition can be modified for sub-graph approximations. We show that it can prepare exact Gibbs distributions on $d-$regular graphs below a critical hardness $\lambda_d^*$ in sub-linear time. Finally, we look at a potential use of quantum subspace correction for fault-tolerant depth-reduction. In particular we investigate how the technique detects and recovers errors induced by Trotterization in preparing maximum independent set using an adiabatic state preparation algorithm., Comment: 12 + 4 pages, 6 figures

We demonstrate an electrically controlled metal-VO2 metasurface for the mid-wave infrared that simultaneously functions as a tunable optical switch, an optical limiter with a tunable limiting threshold, and a nonlinear optical isolator with a tunable operating range. The tunability is achieved via Joule heating through the metal comprising the metasurface, resulting in an integrated optoelectronic device. As an optical switch, the device has an experimental transmission ratio of ~100 when varying the bias current. Operating as an optical limiter, we demonstrated tunability of the limiting threshold from 20 mW to 180 mW of incident laser power. Similar degrees of tunability are also achieved for nonlinear optical isolation, which enables asymmetric (nonreciprocal) transmission., Comment: Main text + supplementary

Successful deployment of geological carbon storage (GCS) requires an extensive use of reservoir simulators for screening, ranking and optimization of storage sites. However, the time scales of GCS are such that no sufficient long-term data is available yet to validate the simulators against. As a consequence, there is currently no solid basis for assessing the quality with which the dynamics of large-scale GCS operations can be forecasted. To meet this knowledge gap, we have conducted a major GCS validation benchmark study. To achieve reasonable time scales, a laboratory-size geological storage formation was constructed (the "FluidFlower"), forming the basis for both the experimental and computational work. A validation experiment consisting of repeated GCS operations was conducted in the FluidFlower, providing what we define as the true physical dynamics for this system. Nine different research groups from around the world provided forecasts, both individually and collaboratively, based on a detailed physical and petrophysical characterization of the FluidFlower sands. The major contribution of this paper is a report and discussion of the results of the validation benchmark study, complemented by a description of the benchmarking process and the participating computational models. The forecasts from the participating groups are compared to each other and to the experimental data by means of various indicative qualitative and quantitative measures. By this, we provide a detailed assessment of the capabilities of reservoir simulators and their users to capture both the injection and post-injection dynamics of the GCS operations.

Both the magnitude and spectrum of the blackbody-radiation distribution change with temperature. Here, we designed the temperature-dependent spectral emissivity of a coating to counteract all the changes in the blackbody-radiation distribution over a certain temperature range, enabled by the nonhysteretic insulator-to-metal phase transition of SmNiO3. At each wavelength within the long-wave infrared atmospheric-transparency window, the thermal radiance of our coating remains nearly constant over a temperature range of at least 20 {\deg}C. Our approach can conceal thermal gradients and transient temperature changes from infrared imaging systems, including those that discriminate by wavelength, such as multispectral and hyperspectral cameras., Comment: Main text + supplementary

We introduce an optical tweezer platform for assembling and individually manipulating a two-dimensional register of nuclear spin qubits. Each nuclear spin qubit is encoded in the ground $^{1}S_{0}$ manifold of $^{87}$Sr and is individually manipulated by site-selective addressing beams. We observe that spin relaxation is negligible after 5 seconds, indicating that $T_1\gg5$ s. Furthermore, utilizing simultaneous manipulation of subsets of qubits, we demonstrate significant phase coherence over the entire register, estimating $T_2^\star = \left(21\pm7\right)$ s and measuring $T_2^\text{echo}=\left(42\pm6\right)$ s., Comment: 10 pages, 4 figures

We propose a framework for saliency-based, multi-target detection and segmentation of circular-scan, synthetic-aperture-sonar (CSAS) imagery. Our framework relies on a multi-branch, convolutional encoder-decoder network (MB-CEDN). The encoder portion of the MB-CEDN extracts visual contrast features from CSAS images. These features are fed into dual decoders that perform pixel-level segmentation to mask targets. Each decoder provides different perspectives as to what constitutes a salient target. These opinions are aggregated and cascaded into a deep-parsing network to refine the segmentation. We evaluate our framework using real-world CSAS imagery consisting of five broad target classes. We compare against existing approaches from the computer-vision literature. We show that our framework outperforms supervised, deep-saliency networks designed for natural imagery. It greatly outperforms unsupervised saliency approaches developed for natural imagery. This illustrates that natural-image-based models may need to be altered to be effective for this imaging-sonar modality., Comment: Submitted to IEEE Journal of Oceanic Engineering

All spectrometers rely on some mechanism to achieve spectral selectivity; common examples include gratings, prisms, and interferometers with moving mirrors. We experimentally demonstrated and validated a spectroscopic technique -- here dubbed Planck spectroscopy -- that measures the spectral emissivity of a surface using only a temperature-controlled stage and a detector, without any wavelength-selective optical components. Planck spectroscopy involves the measurement of temperature-dependent thermally emitted power, where the spectral selectivity is realized via the temperature- and wavelength dependence of Planck's law. We experimentally demonstrated and validated Planck spectroscopy in the mid infrared, for wavelengths from 3 to 13 um -- limited primarily by the bandwidth of our detector -- with resolution of approximately 1 um. The minimalistic setup of Planck spectroscopy can be implemented using infrared cameras to achieve low-cost infrared hyperspectral imaging and imaging ellipsometry., Comment: Main text + supplementary

Despite recent advancements in deep learning methods for protein structure prediction and representation, little focus has been directed at the simultaneous inclusion and prediction of protein backbone and sidechain structure information. We present SidechainNet, a new dataset that directly extends the ProteinNet dataset. SidechainNet includes angle and atomic coordinate information capable of describing all heavy atoms of each protein structure. In this paper, we provide background information on the availability of protein structure data and the significance of ProteinNet. Thereafter, we argue for the potentially beneficial inclusion of sidechain information through SidechainNet, describe the process by which we organize SidechainNet, and provide a software package (https://github.com/jonathanking/sidechainnet) for data manipulation and training with machine learning models., Comment: 8 pages, 2 figures, 1 table, Accepted for the Machine Learning for Structural Biology Workshop at the 34th Conference on Neural Information Processing Systems (MLSB NeurIPS 2020)

Optical limiters are nonlinear devices that feature decreasing transmittance with increasing incident optical intensity, and thus can protect sensitive components from high-intensity illumination. The ideal optical limiter reflects rather than absorbs light in its active ("limiting") state, minimizing risk of damage to the limiter itself. Previous efforts to realize reflective limiters were based on embedding nonlinear layers into relatively thick multilayer photonic structures, resulting in substantial fabrication complexity, reduced speed and, in some instances, limited working bandwidth. We overcome these tradeoffs by using the insulator-to-metal transition in vanadium dioxide (VO2) to achieve intensity-dependent modulation of resonant transmission through aperture antennas. Due to the dramatic change of optical properties across the insulator-to-metal transition, low-quality-factor resonators were sufficient to achieve high on-off ratios in device transmittance. As a result, our ultra-thin reflective limiter (thickness ~1/100 of the free-space wavelength) is broadband in terms of operating wavelength (> 2 um at 10 um) and angle of incidence (up to ~50$\deg$ away from the normal)., Comment: Main text + supplementary

Modern web applications can now offer desktop-like experiences from within the browser, thanks to technologies such as WebSockets, which enable low-latency duplex communication between the browser and the server. While these advances are great for the user experience, they represent a new responsibility for web developers who now need to manage and verify the correctness of more complex and potentially stateful interactions in their application. In this paper, we present a technique for developing interactive web applications that are statically guaranteed to communicate following a given protocol. First, the global interaction protocol is described in the Scribble protocol language -- based on multiparty session types. Scribble protocols are checked for well-formedness, and then each role is projected to a Finite State Machine representing the structure of communication from the perspective of the role. We use source code generation and a novel type-level encoding of FSMs using multi-parameter type classes to leverage the type system of the target language and guarantee only programs that communicate following the protocol will type check. Our work targets PureScript -- a functional language that compiles to JavaScript -- which crucially has an expressive enough type system to provide static linearity guarantees. We demonstrate the effectiveness of our approach through a web-based Battleship game where communication is performed through WebSocket connections., Comment: In Proceedings PLACES 2019, arXiv:1904.00396

Under standard operating conditions, the emissivity of structural alloys used for various components of nuclear reactors may evolve, affecting the heat transfer of the systems. In this study, mid-infrared emissivities of several reactor structural alloys were measured before and after exposure to environments relevant to next-generation reactors. We evaluated nickel-based alloys Haynes 230 and Inconel 617 exposed to helium gas at 1000 $^{\circ}$C, nickel-based Hastelloy N and iron-based 316 stainless steel exposed to molten salts at 750-850 $^{\circ}$C, 316 stainless steel exposed to liquid sodium at 650 $^{\circ}$C, and 316 stainless steel and Haynes 230 exposed to supercritical CO2 at 650 $^{\circ}$C. Emissivity was measured via emissive and reflective techniques using a Fourier transform infrared (FTIR) spectrometer. Large increases in emissivity are observed for alloys exposed to oxidizing environments, while only minor differences were observed in other exposure conditions., Comment: Main text and supplementary info (merged in one file)

Report of the first workshop to identify approaches and techniques in the domain of quantum sensing that can be utilized by future High Energy Physics applications to further the scientific goals of High Energy Physics., Comment: 38 pages, report of the first workshop on Quantum Sensing for High Energy Physics, held at Argonne National Laboratory, December 12-14, 2017