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Abstract We show that the quantum approximate optimization algorithm (QAOA) for higher-order, random coefficient, heavy-hex compatible spin glass Ising models has strong parameter concentration across problem sizes from 16 up to 127 qubits for p = 1 up to p = 5, which allows for computationally efficient parameter transfer of QAOA angles. Matrix product state (MPS) simulation is used to compute noise-free QAOA performance. Hardware-compatible short-depth QAOA circuits are executed on ensembles of 100 higher-order Ising models on noisy IBM quantum superconducting processors with 16, 27, and 127 qubits using QAOA angles learned from a single 16-qubit instance using the JuliQAOA tool. We show that the best quantum processors find lower energy solutions up to p = 2 or p = 3, and find mean energies that are about a factor of two off from the noise-free distribution. We show that p = 1 QAOA energy landscapes remain very similar as the problem size increases using NISQ hardware gridsearches with up to a 414 qubit processor.

Abstract We present a direct comparison between QAOA (Quantum Alternating Operator Ansatz), and QA (Quantum Annealing) on 127 qubit problem instances. QAOA with p = 1, 2 rounds is executed on the 127 qubit heavy-hex graph gate-model quantum computer ibm_washington, using on-device grid-searches for angle finding, and QA is executed on two Pegasus-chip D-Wave quantum annealers. The problems are random Ising models whose connectivity matches heavy-hex graphs and the Pegasus graph connectivity, and optionally include hardware-compatible cubic terms (Z Z Z terms). The QAOA circuits are heavily optimized and of extremely short depth, with a CNOT depth of 6 per round, which allows whole chip usage of the heavy-hex lattice. QAOA and QA are both compared against simulated annealing and the optimal solutions are computed exactly using CPLEX. The noiseless mean QAOA expectation values for p = 1, 2 are computed using classical light-cone based simulations. We find QA outperforms QAOA on the evaluated devices.

Quantum information cannot be perfectly cloned, but approximate copies of quantum information can be generated. Quantum telecloning combines approximate quantum cloning, more typically referred to as quantum cloning, and quantum teleportation. Quantum telecloning allows approximate copies of quantum information to be constructed by separate parties, using the classical results of a Bell measurement made on a prepared quantum telecloning state. Quantum telecloning can be implemented as a circuit on quantum computers using a classical coprocessor to compute classical feedforward instructions using if statements based on the results of a midcircuit Bell measurement in real time. We present universal symmetric optimal $1 \rightarrow M$ telecloning circuits and experimentally demonstrate these quantum telecloning circuits for $M=2$ up to $M=10$, natively executed with real-time classical control systems on IBM Quantum superconducting processors, known as dynamic circuits. We perform the cloning procedure on many different message states across the Bloch sphere, on seven IBM Quantum processors, optionally using the error suppression technique X–X sequence digital dynamical decoupling. Two circuit optimizations are utilized: one that removes ancilla qubits for $M=2, 3$, and one that reduces the total number of gates in the circuit but still uses ancilla qubits. Parallel single-qubit tomography with maximum likelihood estimation density matrix reconstruction is used in order to compute the mixed-state density matrices of the clone qubits, and clone quality is measured using quantum fidelity. These results present one of the largest and most comprehensive noisy intermediate-scale quantum computer experimental analyses on (single qubit) quantum telecloning to date. The clone fidelity sharply decreases to 0.5 for $M > 5$, but for $M=2$, we are able to achieve a mean clone fidelity of up to 0.79 using dynamical decoupling.

Abstract The morphometric assessment of the corneal subbasal nerve plexus (SNP) by confocal microscopy holds great potential as a sensitive biomarker for various ocular and systemic conditions and diseases. Automated wide-field montages (or large-area mosaic images) of the SNP provide an opportunity to overcome the limited field of view of the available imaging systems without the need for manual, subjective image selection for morphometric characterization. However, current wide-field montaging solutions usually calculate the mosaic image after the examination session, without a reliable means for the clinician to predict or estimate the resulting mosaic image quality during the examination. This contribution describes a novel approach for a real-time creation and visualization of a mosaic image of the SNP that facilitates an informed evaluation of the quality of the acquired image data immediately at the time of recording. In cases of insufficient data quality, the examination can be aborted and repeated immediately, while the patient is still at the microscope. Online mosaicking also offers the chance to identify an overlap of the imaged tissue region with previous SNP mosaic images, which can be particularly advantageous for follow-up examinations.

Quantum volume (QV) has become the de-facto standard benchmark to quantify the capability of noisy intermediate-scale quantum (NISQ) devices. While QV values are often reported by NISQ providers for their systems, we perform our own series of QV calculations on 24 NISQ devices currently offered by IBM Q, IonQ, Rigetti, Oxford Quantum Circuits, and Quantinuum (formerly Honeywell). Our approach characterizes the performances that an advanced user of these NISQ devices can expect to achieve with a reasonable amount of optimization but without white-box access to the device. In particular, we compile QV circuits to standard gate sets of the vendor using compiler optimization routines where available, and we perform experiments across different qubit subsets. We find that running QV tests requires very significant compilation cycles, QV values achieved in our tests typically lag behind officially reported results and also depend significantly on the classical compilation effort invested.

We present a divide-and-conquer approach to deterministically prepare Dicke states $|D^{n}_{k}\rangle$ (i.e., equal-weight superpositions of all $n$-qubit states with Hamming weight $k$) on quantum computers. In an experimental evaluation for up to $n=6$ qubits on IBM Quantum Sydney and Montreal devices, we achieve significantly higher state fidelity compared to previous results. The fidelity gains are achieved through several techniques: our circuits first “divide” the Hamming weight between blocks of $n/2$ qubits, and then “conquer” those blocks with improved versions of Dicke state unitaries (Bärtschi et al. FCT’2019). Due to the sparse connectivity on IBM’s heavy-hex-architectures, these circuits are implemented for linear nearest neighbor topologies. Further gains in (estimating) the state fidelity are due to our use of measurement error mitigation and hardware progress.

Measurement(s) Prominent corneal nerve fibers • Dendritic Cell Technology Type(s) confocal microscopy Factor Type(s) disease status • comorbidity Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16691452

Abstract Small fiber neuropathy (SFN) has been suggested as a trigger of restless legs syndrome (RLS). An increased prevalence of peripheral neuropathy has been demonstrated in Parkinson’s disease (PD). We aimed to investigate, in a cross-sectional manner, whether SFN is overrepresented in PD patients with concurrent RLS relative to PD patients without RLS, using in vivo corneal confocal microscopy (IVCCM) and quantitative sensory testing (QST) as part of small fiber assessment. Study participants comprised of age- and sex-matched PD patients with (n = 21) and without RLS (n = 21), and controls (n = 13). Diagnosis of RLS was consolidated with the sensory suggested immobilization test. Assessments included nerve conduction studies (NCS), Utah Early Neuropathy Scale (UENS), QST, and IVCCM, with automated determination of corneal nerve fiber length (CNFL) and branch density (CNBD) from wide-area mosaics of the subbasal nerve plexus. Plasma neurofilament light (p-NfL) was determined as a measure of axonal degeneration. No significant differences were found between groups when comparing CNFL (p = 0.81), CNBD (p = 0.92), NCS (p = 0.82), and QST (minimum p = 0.54). UENS scores, however, differed significantly (p = 0.001), with post-hoc pairwise testing revealing higher scores in both PD groups relative to controls (p = 0.018 and p = 0.001). Analysis of all PD patients (n = 42) revealed a correlation between the duration of l-dopa therapy and CNBD (ρ = −0.36, p = 0.022), and p-NfL correlated with UENS (ρ = 0.35, p = 0.026) and NCS (ρ = −0.51, p = 0.001). Small and large fiber neuropathy do not appear to be associated with RLS in PD. Whether peripheral small and/or large fiber pathology associates with central neurodegeneration in PD merits further longitudinal studies.

Paclitaxel and trastuzumab have been associated with adverse effects including chemotherapy-induced peripheral neuropathy (CIPN) or ocular complications. In vivo confocal laser scanning microscopy (CLSM) of the cornea could be suitable for assessing side effects since the cornea is susceptible to, i.e., neurotoxic stimuli. The study represents a one-year follow-up of a breast cancer patient including large-area in vivo CLSM of the subbasal nerve plexus (SNP), nerve function testing, and questionnaires during paclitaxel and trastuzumab therapy. Six monitoring sessions (one baseline, four during, and one after therapy) over 58 weeks were carried out. Large-area mosaics of the SNP were generated, and identical regions within all sessions were assigned. While corneal nerve morphology did not cause alterations, the number of dendritic cells (DCs) showed dynamic changes with a local burst at 11 weeks after baseline. Simultaneously, paclitaxel treatment was terminated due to side effects, which, together with DCs, returned to normal levels as the therapy progressed. Longitudinal in vivo CLSM of the SNP could complement routine examinations and be helpful to generate a comprehensive clinical picture. The applied techniques, with corneal structures acting as biomarkers could represent a diagnostic tool for the objective assessment of the severity of adverse events and the outcome.

Magnetic resonance tomography typically applies the Fourier transform to k-space signals repeatedly acquired from a frequency encoded spatial region of interest, therefore requiring a stationary object during scanning. Any movement of the object results in phase errors in the recorded signal, leading to deformed images, phantoms, and artifacts, since the encoded information does not originate from the intended region of the object. However, if the type and magnitude of movement is known instantaneously, the scanner or the reconstruction algorithm could be adjusted to compensate for the movement, directly allowing high quality imaging with non-stationary objects. This would be an enormous boon to studies that tie cell metabolomics to spontaneous organism behaviour, eliminating the stress otherwise necessitated by restraining measures such as anesthesia or clamping. In the present theoretical study, we use a phantom of the animal model C. elegans to examine the feasibility to automatically predict its movement and position, and to evaluate the impact of movement prediction, within a sufficiently long time horizon, on image reconstruction. For this purpose, we use automated image processing to annotate body parts in freely moving C. elegans, and predict their path of movement. We further introduce an MRI simulation platform based on bright field videos of the moving worm, combined with a stack of high resolution transmission electron microscope (TEM) slice images as virtual high resolution phantoms. A phantom provides an indication of the spatial distribution of signal-generating nuclei on a particular imaging slice. We show that adjustment of the scanning to the predicted movements strongly reduces distortions in the resulting image, opening the door for implementation in a high-resolution NMR scanner.

Abstract The capability of corneal confocal microscopy (CCM) to acquire high-resolution in vivo images of the densely innervated human cornea has gained considerable interest in using this non-invasive technique as an objective diagnostic tool for staging peripheral neuropathies. Morphological alterations of the corneal subbasal nerve plexus (SNP) assessed by CCM have been shown to correlate well with the progression of neuropathic diseases and even predict future-incident neuropathy. Since the field of view of single CCM images is insufficient for reliable characterisation of nerve morphology, several image mosaicking techniques have been developed to facilitate the assessment of the SNP in large-area visualisations. Due to the limited depth of field of confocal microscopy, these approaches are highly sensitive to small deviations of the focus plane from the SNP layer. Our contribution proposes a new automated solution, combining guided eye movements for rapid expansion of the acquired SNP area and axial focus plane oscillations to guarantee complete imaging of the SNP. We present results of a feasibility study using the proposed setup to evaluate different oscillation settings. By comparing different image selection approaches, we show that automatic tissue classification algorithms are essential to create high-quality mosaic images from the acquired 3D datasets.

Abstract The present article describes a concept for the creation and application of energy forecasting models in a distributed environment. Additionally, a benchmark comparing the time required for the training and application of data-driven forecasting models on a single computer and a computing cluster is presented. This comparison is based on a simulated dataset and both R and Apache Spark are used. Furthermore, the obtained results show certain points in which the utilization of distributed computing based on Spark may be advantageous.

Abstract Background and Purpose:  Vasospasm of the cerebral vessels remains a major source for morbidity and mortality after aneurysmal subarachnoid hemorrhage (SAH). The purpose of this study was to evaluate the frequency of infarction after transluminal balloon angioplasty (TBA) in patients with severe SAH-related vasospasm. Patients and Methods:  20 consecutive patients with angiographically confirmed vasospasm (median Hunt & Hess grade II, median Fisher grade IV) were included in this study. Treatment consisted of TBA in severe vasospasm of the middle cerebral artery (MCA), whereas vasospasm of the anterior cerebral artery (ACA) was not considered for TBA and served as a control group. A total of 30 MCA segments revealing extensive vasospasm were treated with TBA. In 33 ACA segments severe vasospasm was present. For both vessel territories cerebral infarction on follow-up CT was assessed as a marker of adverse outcome. Results:  In the MCA territory, infarction occurred after TBA in two of 30 vessels (7%). In the ACA territory 14 of 33 ACA territories with confirmed severe vasospasm developed infarction (42%). This was a significantly lower rate of infarction in the TBA group (p Conclusion:  In a population of patients with a high risk for vasospasm-related infarction after SAH the frequency of infarction in vessel territories after TBA amounts to 7% and is considerably lower than in vessels with vasospasm not undergoing TBA. [ABSTRACT FROM AUTHOR]

Conventional blood oxygenation level-dependent (BOLD) based functional magnetic resonance imaging (fMRI) is accompanied by substantial acoustic gradient noise. This noise can influence the performance as well as neuronal activations. Conventional fMRI typically has a pulsed noise component, which is a particularly efficient auditory stimulus. We investigated whether the elimination of this pulsed noise component in a recent modification of continuous-sound fMRI modifies neuronal activations in a cognitively demanding non-auditory working memory task. Sixteen normal subjects performed a letter variant n-back task. Brain activity and psychomotor performance was examined during fMRI with continuous-sound fMRI and conventional fMRI. We found greater BOLD responses in bilateral medial frontal gyrus, left middle frontal gyrus, left middle temporal gyrus, left hippocampus, right superior frontal gyrus, right precuneus and right cingulate gyrus with continuous-sound compared to conventional fMRI. Conversely, BOLD responses were greater in bilateral cingulate gyrus, left middle and superior frontal gyrus and right lingual gyrus with conventional compared to continuous-sound fMRI. There were no differences in psychomotor performance between both scanning protocols. Although behavioral performance was not affected, acoustic gradient noise interferes with neuronal activations in non-auditory cognitive tasks and represents a putative systematic confound [ABSTRACT FROM AUTHOR]

During breast cancer therapy, paclitaxel and trastuzumab are both associated with adverse effects such as chemotherapy-induced peripheral neuropathy and other systemic side effects including ocular complications. Corneal nerves are considered part of the peripheral nervous system and can be imaged non-invasively by confocal laser scanning microscopy (CLSM) on the cellular level. Thus, in vivo CLSM imaging of structures of the corneal subbasal nerve plexus (SNP) such as sensory nerves or dendritic cells (DCs) can be a powerful tool for the assessment of corneal complications during cancer treatment. During the present study, the SNP of a breast cancer patient was analyzed over time by using large-scale in vivo CLSM in the course of paclitaxel and trastuzumab therapy. The same corneal regions could be re-identified over time. While the subbasal nerve morphology did not alter significantly, a change in dendritic cell density and an additional local burst within the first 11 weeks of therapy was detected, indicating treatment-mediated corneal inflammatory processes. Ocular structures such as nerves and dendritic cells could represent useful biomarkers for the assessment of ocular adverse effects during cancer therapy and their management, leading to a better visual prognosis.

State-of-the-art light-sheet and confocal microscopes allow recording of entire embryos in 3D and over time (3D+t) for many hours. Fluorescently labeled structures can be segmented and tracked automatically in these terabyte-scale 3D+t images, resulting in thousands of cell migration trajectories that provide detailed insights to large-scale tissue reorganization at the cellular level. Here we present EmbryoMiner, a new interactive open-source framework suitable for in-depth analyses and comparisons of entire embryos, including an extensive set of trajectory features. Starting at the whole-embryo level, the framework can be used to iteratively focus on a region of interest within the embryo, to investigate and test specific trajectory-based hypotheses and to extract quantitative features from the isolated trajectories. Thus, the new framework provides a valuable new way to quantitatively compare corresponding anatomical regions in different embryos that were manually selected based on biological prior knowledge. As a proof of concept, we analyzed 3D+t light-sheet microscopy images of zebrafish embryos, showcasing potential user applications that can be performed using the new framework.

Automated analysis of multi-dimensional microscopy images has become an integral part of modern research in life science. Most available algorithms that provide sufficient segmentation quality, however, are infeasible for a large amount of data due to their high complexity. In this contribution we present a fast parallelized segmentation method that is especially suited for the extraction of stained nuclei from microscopy images, e.g., of developing zebrafish embryos. The idea is to transform the input image based on gradient and normal directions in the proximity of detected seed points such that it can be handled by straightforward global thresholding like Otsu's method. We evaluate the quality of the obtained segmentation results on a set of real and simulated benchmark images in 2D and 3D and show the algorithm's superior performance compared to other state-of-the-art algorithms. We achieve an up to ten-fold decrease in processing times, allowing us to process large data sets while still providing reasonable segmentation results.