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Quantum error mitigation is a promising route to achieving quantum utility, and potentially quantum advantage in the near-term. Many state-of-the-art error mitigation schemes use knowledge of the errors in the quantum processor, which opens the question to what extent inaccuracy in the error model impacts the performance of error mitigation. In this work, we develop a methodology to efficiently compute upper bounds on the impact of error-model inaccuracy in error mitigation. Our protocols require no additional experiments, and instead rely on comparisons between the error model and the error-learning data from which the model is generated. We demonstrate the efficacy of our methodology by deploying it on an IBM Quantum superconducting qubit quantum processor, and through numerical simulation of standard error models. We show that our estimated upper bounds are typically close to the worst observed performance of error mitigation on random circuits. Our methodology can also be understood as an operationally meaningful metric to assess the quality of error models, and we further extend our methodology to allow for comparison between error models. Finally, contrary to what one might expect we show that observable error in noisy layered circuits of sufficient depth is not always maximized by a Clifford circuit, which may be of independent interest., Comment: 22 pages including references and appendices, 9 figures

The estimation of low energies of many-body systems is a cornerstone of computational quantum sciences. Variational quantum algorithms can be used to prepare ground states on pre-fault-tolerant quantum processors, but their lack of convergence guarantees and impractical number of cost function estimations prevent systematic scaling of experiments to large systems. Alternatives to variational approaches are needed for large-scale experiments on pre-fault-tolerant devices. Here, we use a superconducting quantum processor to compute eigenenergies of quantum many-body systems on two-dimensional lattices of up to 56 sites, using the Krylov quantum diagonalization algorithm, an analog of the well-known classical diagonalization technique. We construct subspaces of the many-body Hilbert space using Trotterized unitary evolutions executed on the quantum processor, and classically diagonalize many-body interacting Hamiltonians within those subspaces. These experiments show that quantum diagonalization algorithms are poised to complement their classical counterpart at the foundation of computational methods for quantum systems., Comment: 25 pages, 13 figures

Prostate cancer is one of the most prevalent malignancies in the world. While deep learning has potential to further improve computer-aided prostate cancer detection on MRI, its efficacy hinges on the exhaustive curation of manually annotated images. We propose a novel methodology of semisupervised learning (SSL) guided by automatically extracted clinical information, specifically the lesion locations in radiology reports, allowing for use of unannotated images to reduce the annotation burden. By leveraging lesion locations, we refined pseudo labels, which were then used to train our location-based SSL model. We show that our SSL method can improve prostate lesion detection by utilizing unannotated images, with more substantial impacts being observed when larger proportions of unannotated images are used., Comment: 4 page paper accepted to IEEE International Symposium on Biomedical Imaging (ISBI 2024)

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.

Tracking and following objects of interest is critical to several robotics use cases, ranging from industrial automation to logistics and warehousing, to healthcare and security. In this paper, we present a robotic system to detect, track, and follow any object in real-time. Our approach, dubbed ``follow anything'' (FAn), is an open-vocabulary and multimodal model -- it is not restricted to concepts seen at training time and can be applied to novel classes at inference time using text, images, or click queries. Leveraging rich visual descriptors from large-scale pre-trained models (foundation models), FAn can detect and segment objects by matching multimodal queries (text, images, clicks) against an input image sequence. These detected and segmented objects are tracked across image frames, all while accounting for occlusion and object re-emergence. We demonstrate FAn on a real-world robotic system (a micro aerial vehicle) and report its ability to seamlessly follow the objects of interest in a real-time control loop. FAn can be deployed on a laptop with a lightweight (6-8 GB) graphics card, achieving a throughput of 6-20 frames per second. To enable rapid adoption, deployment, and extensibility, we open-source all our code on our project webpage at https://github.com/alaamaalouf/FollowAnything . We also encourage the reader to watch our 5-minutes explainer video in this https://www.youtube.com/watch?v=6Mgt3EPytrw ., Comment: Project webpage: https://github.com/alaamaalouf/FollowAnything Explainer video: https://www.youtube.com/watch?v=6Mgt3EPytrw

Blood vessel segmentation in medical imaging is one of the essential steps for vascular disease diagnosis and interventional planning in a broad spectrum of clinical scenarios in image-based medicine and interventional medicine. Unfortunately, manual annotation of the vessel masks is challenging and resource-intensive due to subtle branches and complex structures. To overcome this issue, this paper presents a self-supervised vessel segmentation method, dubbed the contrastive diffusion adversarial representation learning (C-DARL) model. Our model is composed of a diffusion module and a generation module that learns the distribution of multi-domain blood vessel data by generating synthetic vessel images from diffusion latent. Moreover, we employ contrastive learning through a mask-based contrastive loss so that the model can learn more realistic vessel representations. To validate the efficacy, C-DARL is trained using various vessel datasets, including coronary angiograms, abdominal digital subtraction angiograms, and retinal imaging. Experimental results confirm that our model achieves performance improvement over baseline methods with noise robustness, suggesting the effectiveness of C-DARL for vessel segmentation.

This study focuses on the ethical considerations that a consumer perceives with augmented reality (AR) in the context of smartphone applications. Through a systematic review, this research can provide an understanding and ability for developers, product managers, digital marketers and associated business professionals to effectively implement and deploy mobile AR related applications and campaigns, with consideration to the perceptions of the ethical considerations that consumers have of this growing technology. The rise in digital transformation and new technologies paved this research agenda. Trends in the data revealed two overarching factors of 'Benefits' and 'Ethical Considerations'. Within these two factors, several consumer perceived themes were identified with regards to AR applications and their association categorised either positive, negative or neutral. 'Benefits' revealed 3 consistent themes of personalisation, interactivity and information acquisition. 'Ethical Considerations' revealed consistent patterns of educational awareness, privacy, transparency and security. From identifying the consumer perceptions, business professionals can strategically address and or challenge the inherent limitations and their associations during AR application development, product adoption strategies or marketing purposes., Comment: 46 pages, 1 figure

To run large-scale algorithms on a quantum computer, error-correcting codes must be able to perform a fundamental set of operations, called logic gates, while isolating the encoded information from noise~\cite{Harper2019,Ryan-Anderson2021,Egan2021fault, Chen2022calibrated, Sundaresan2022matching, ryananderson2022implementing, Postler2022demonstration, GoogleAI2023}. We can complete a universal set of logic gates by producing special resources called magic states~\cite{Bravyi2005universal,Maier2013magic, Chamberland2022building}. It is therefore important to produce high-fidelity magic states to conduct algorithms while introducing a minimal amount of noise to the computation. Here, we propose and implement a scheme to prepare a magic state on a superconducting qubit array using error correction. We find that our scheme produces better magic states than those we can prepare using the individual qubits of the device. This demonstrates a fundamental principle of fault-tolerant quantum computing~\cite{Shor96}, namely, that we can use error correction to improve the quality of logic gates with noisy qubits. Additionally, we show we can increase the yield of magic states using adaptive circuits, where circuit elements are changed depending on the outcome of mid-circuit measurements. This demonstrates an essential capability we will need for many error-correction subroutines. Our prototype will be invaluable in the future as it can reduce the number of physical qubits needed to produce high-fidelity magic states in large-scale quantum-computing architectures., Comment: 19 pages, 13 figures, 3 tables, comments welcome; v2 - Updated draft including new appendices following peer review. Includes a section on injecting the encoded magic state into larger codes (explicitly studying the surface code, the heavy-hex code and the color code) and a numerical section interrogating the fault-tolerant properties of the circuit

UV-cured collagen-based hydrogels hold promise in skeletal muscle regeneration due to their soft elastic properties and porous architecture. However, the complex triple helix conformation of collagen and environmental conditions, i.e. molecular oxygen, pose risks to reaction controllability, wet-state integrity and reproducibility. To address this challenge, a photo-click hydrogel platform is presented through an oxygen-insensitive thiol-ene reaction between 2-iminothiolane (2IT)-functionalized type I collagen and multi-arm, non-homopolymerizable norbornene-terminated polyethylene glycol (PEG). UV-induced network formation is demonstrated by oscillatory time sweeps on the reacting thiol-ene mixture, so that significantly increased storage moduli are measured and adjusted depending on the photo-initiator concentration. Variations in PEG functionality (4-arm and 8-arm) and PEG content generate hydrogels with skeletal muscle native stiffness (Ec= 1.3-11.5 kPa), diffusion-controlled swelling behavior and erosion-driven degradability. In vitro, no cytotoxic effect is detected on C2C12 murine myoblasts, while myogenic differentiation is successfully accomplished on hydrogel-seeded cells in low serum culture medium. In vivo, 7-day subcutaneous implantation of selected thiol-ene hydrogel in rats reveal higher cell infiltration, blood vessel formation, and denser tissue interface compared to a clinical gold standard collagen matrix (Mucograft, Geistlich). These results therefore support the applicability and further development of this hydrogel platform for skeletal muscle regeneration., Comment: 10 figures, 1 table; accepted in Macromolecular Materials and Engineering

Initially, the acoustic field forced the droplets into an oblate spheroid shape, though the counteracting force of the cooling stream caused them to circularize. Droplet geometry was thus the net result of streaming forces and surface tension at the acoustic boundary layer/air-liquid interface. Nucleation was determined to be neither homogeneous nor heterogeneous but secondary, and thus dependent on the cooling rate and not on the degree of supercooling. It was likely initiated by aerosolized ice particles from the air or from droplets that had already nucleated and broken up. The latter secondary ice production process resulted in multi-drop systems with statistically identical nucleation times. Notably, this meant that the presence of interfacial rupture at an adjacent droplet could influence the crystallization behaviour of another. After the formation of an initial ice shell around the individual droplets, dendritic protrusions grew from the droplet surface, likely seeded by the same ice particles that caused nucleation, but at a quasi-liquid layer. When freezing was complete, it was determined that the frozen core had undergone a volumetric expansion of 30.75%, compared to 9% for pure, sessile water expansion. This significantly greater expansion may have resulted from entrained air bubbles at the inner solid-liquid interface and oscillations at the moving phase boundary caused by changes in local acoustic forces. Soon after melting began, acoustic streaming, the buoyancy of the remaining ice, and convective currents caused by an inner thermal gradient and thermocapillary effects along the air-liquid interface, all contributed to the droplet spinning about the horizontal axis.

We investigate an error mitigated tomographic approach to the quantum circuit cutting problem in the presence of gate and measurement noise. We explore two tomography specific error mitigation techniques; readout error mitigated conditional fragment tomography, which uses knowledge of readout errors on all cut and conditional qubit measurements in the tomography reconstruction procedure; and dominant eigenvalue truncation (DEVT), which aims to improve the performance of circuit cutting by performing truncation of the individual conditional tomography fragments used in the reconstruction. We find that the performance of both readout error mitigated tomography and DEVT tomography are comparable for circuit cutting in the presence of symmetric measurement errors. For gate errors our numerical results show that probability estimates for the original circuit obtained using DEVT outperforms general circuit cutting for measurement, depolarization and weakly biased Pauli noise models, but does not improve performance for amplitude damping and coherent errors, and can greatly decrease performance for highly biased Pauli noise. In cases where DEVT was effective, it as also found to improve performance of partial tomographic reconstruction using at least 50% of the full tomographic data with a conditional least-squares tomographic fitter, while linear inversion tomography with or without DEVT mitigation was found to perform poorly with with partial data., Comment: 15+2 pages, double column

We develop general purpose algorithms for computing and utilizing both the Dyson series and Magnus expansion, with the goal of facilitating numerical perturbative studies of quantum dynamics. To enable broad applications to models with multiple parameters, we phrase our algorithms in terms of multivariable sensitivity analysis, for either the solution or the time-averaged generator of the evolution over a fixed time-interval. These tools simultaneously compute a collection of terms up to arbitrary order, and are general in the sense that the model can depend on the parameters in an arbitrary time-dependent way. We implement the algorithms in the open source software package \qiskitdynamics{}, utilizing the JAX array library to enable just-in-time compilation, automatic differentiation, and GPU execution of all computations. Using a model of a single transmon, we demonstrate how to use these tools to approximate fidelity in a region of model parameter space, as well as construct perturbative robust control objectives. We also derive and implement Dyson and Magnus-based variations of the recently introduced Dysolve algorithm [Shillito et al., Physical Review Research, 3(3):033266] for simulating linear matrix differential equations. We show how the pre-computation step can be phrased as a multivariable expansion computation problem with fewer terms than in the original method. When simulating a two-transmon entangling gate on a GPU, we find the Dyson and Magnus-based solvers provide a speedup over traditional ODE solvers, ranging from roughly $2\times$ to $4\times$ for a solution and $10\times$ to $60\times$ for a gradient, depending on solution accuracy., Comment: Accepted version: Speedup results updated after rerunning simulations with updated packages, modified figures, and expanded discussions

In this work, we propose to explicitly use the landmarks of prostate to guide the MR-TRUS image registration. We first train a deep neural network to automatically localize a set of meaningful landmarks, and then directly generate the affine registration matrix from the location of these landmarks. For landmark localization, instead of directly training a network to predict the landmark coordinates, we propose to regress a full-resolution distance map of the landmark, which is demonstrated effective in avoiding statistical bias to unsatisfactory performance and thus improving performance. We then use the predicted landmarks to generate the affine transformation matrix, which outperforms the clinicians' manual rigid registration by a significant margin in terms of TRE., Comment: Submitted to SPIE Medical Imaging 2023

Advancements in fast electron detectors have enabled the statistically significant sampling of crystal structures on the nanometre scale by means of Scanning Electron Nanobeam Diffraction (SEND). Characterisation of structural similarity across this length scale is key to bridging the gap between local atomic structure (using atomic resolution techniques such as High Resolution Scanning Transmission Electron Microscopy (HR-STEM)) and the macro-scale (using bulk techniques such as powder X-ray and neutron diffraction). The use of SEND technique allows for structural investigation of a broad range of samples, due to the techniques ability to operate with low electron dosage and its tolerance for sample thickness, relative to HR-STEM. This, coupled with the capacity for data collection over a wide areas and the automation of this collection, allows for statistically representative sampling of the microstructure. Also due to these factors, SEND generates large datasets and as a result automated/ semi-automated data processing workflows are required to aid in maximal extraction of useful information. As such, this paper outlines a versatile, data-driven approach for producing domain maps, as well as a statistical approach for assessing their applicability. The production of such domain maps for a dataset can help highlight nuance in the microstructure, as well as improve the manageability of that dataset for further investigation. The workflow outlined utilises a Variational AutoEncoder to identify and learn the sources of variance in the diffraction signal and this, in combination with clustering techniques, is used to produce domain maps for a set of varied example cases. This approach: is agnostic to domain crystallinity; requires no prior knowledge of crystal structure; and does not require the, potentially prohibitive, simulation of a library of appropriate diffraction patterns.

Mid-circuit measurements are a key component in many quantum information computing protocols, including quantum error correction, fault-tolerant logical operations, and measurement based quantum computing. As such, techniques to quickly and efficiently characterize or benchmark their performance are of great interest. Beyond the measured qubit, it is also relevant to determine what, if any, impact mid-circuit measurement has on adjacent, unmeasured, spectator qubits. Here, we present a mid-circuit measurement benchmarking suite developed from the ubiquitous paradigm of randomized benchmarking. We show how our benchmarking suite can be used to both detect as well as quantify errors on both measured and spectator qubits, including measurement-induced errors on spectator qubits and entangling errors between measured and spectator qubits. We demonstrate the scalability of our suite by simultaneously characterizing mid-circuit measurement on multiple qubits from an IBM Quantum Falcon device, and support our experimental results with numerical simulations. Further, using a mid-circuit measurement tomography protocol we establish the nature of the errors identified by our benchmarking suite., Comment: 28 pages, 10 figures