arXiv subjects

Hemant Sharma

Publications and source records attributed to Hemant Sharma.

27 records · Page 2Linked to original sources

arXiv2022

Entanglement distribution with minimal memory requirements using time-bin photonic qudits

Generating multiple entangled qubit pairs between distributed nodes is a prerequisite for a future quantum internet. To achieve a practicable generation rate, standard protocols based on photonic qubits require multiple long-term quantum memories, which remains a significant experimental challenge. In this paper, we propose a novel protocol based on $2^m$-dimensional time-bin photonic qudits that allow for the simultaneous generation of multiple ($m$) entangled pairs between two distributed qubit registers and outline a specific implementation of the protocol based on cavity-mediated spin-photon interactions. By adopting the qudit protocol, the required qubit memory time is independent of the transmission loss between the nodes in contrast to standard qubit approaches. As such, our protocol can significantly boost the performance of near-term quantum networks.

quant-ph↗

arXiv2022

fairDMS: Rapid Model Training by Data and Model Reuse

Extracting actionable information rapidly from data produced by instruments such as the Linac Coherent Light Source (LCLS-II) and Advanced Photon Source Upgrade (APS-U) is becoming ever more challenging due to high (up to TB/s) data rates. Conventional physics-based information retrieval methods are hard-pressed to detect interesting events fast enough to enable timely focusing on a rare event or correction of an error. Machine learning~(ML) methods that learn cheap surrogate classifiers present a promising alternative, but can fail catastrophically when changes in instrument or sample result in degradation in ML performance. To overcome such difficulties, we present a new data storage and ML model training architecture designed to organize large volumes of data and models so that when model degradation is detected, prior models and/or data can be queried rapidly and a more suitable model retrieved and fine-tuned for new conditions. We show that our approach can achieve up to 100x data labelling speedup compared to the current state-of-the-art, 200x improvement in training speed, and 92x speedup in-terms of end-to-end model updating time.

cs.LG↗

arXiv2022

Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrieval

Extremely high data rates at modern synchrotron and X-ray free-electron laser light source beamlines motivate the use of machine learning methods for data reduction, feature detection, and other purposes. Regardless of the application, the basic concept is the same: data collected in early stages of an experiment, data from past similar experiments, and/or data simulated for the upcoming experiment are used to train machine learning models that, in effect, learn specific characteristics of those data; these models are then used to process subsequent data more efficiently than would general-purpose models that lack knowledge of the specific dataset or data class. Thus, a key challenge is to be able to train models with sufficient rapidity that they can be deployed and used within useful timescales. We describe here how specialized data center AI (DCAI) systems can be used for this purpose through a geographically distributed workflow. Experiments show that although there are data movement cost and service overhead to use remote DCAI systems for DNN training, the turnaround time is still less than 1/30 of using a locally deploy-able GPU.

cs.LG↗

arXiv2021

BraggNN: Fast X-ray Bragg Peak Analysis Using Deep Learning

X-ray diffraction based microscopy techniques such as High Energy Diffraction Microscopy rely on knowledge of the position of diffraction peaks with high precision. These positions are typically computed by fitting the observed intensities in area detector data to a theoretical peak shape such as pseudo-Voigt. As experiments become more complex and detector technologies evolve, the computational cost of such peak detection and shape fitting becomes the biggest hurdle to the rapid analysis required for real-time feedback during in-situ experiments. To this end, we propose BraggNN, a deep learning-based method that can determine peak positions much more rapidly than conventional pseudo-Voigt peak fitting. When applied to a test dataset, BraggNN gives errors of less than 0.29 and 0.57 pixels, relative to the conventional method, for 75% and 95% of the peaks, respectively. When applied to a real experimental dataset, a 3D reconstruction that used peak positions computed by BraggNN yields 15% better results on average as compared to a reconstruction obtained using peak positions determined using conventional 2D pseudo-Voigt fitting. Recent advances in deep learning method implementations and special-purpose model inference accelerators allow BraggNN to deliver enormous performance improvements relative to the conventional method, running, for example, more than 200 times faster than a conventional method on a consumer-class GPU card with out-of-the-box software.

eess.IV↗

arXiv2020

Big Data Staging with MPI-IO for Interactive X-ray Science

New techniques in X-ray scattering science experiments produce large data sets that can require millions of high-performance processing hours per week of computation for analysis. In such applications, data is typically moved from X-ray detectors to a large parallel file system shared by all nodes of a petascale supercomputer and then is read repeatedly as different science application tasks proceed. However, this straightforward implementation causes significant contention in the file system. We propose an alternative approach in which data is instead staged into and cached in compute node memory for extended periods, during which time various processing tasks may efficiently access it. We describe here such a big data staging framework, based on MPI-IO and the Swift parallel scripting language. We discuss a range of large-scale data management issues involved in X-ray scattering science and measure the performance benefits of the new staging framework for high-energy diffraction microscopy, an important emerging application in data-intensive X-ray scattering. We show that our framework accelerates scientific processing turnaround from three months to under 10 minutes, and that our I/O technique reduces input overheads by a factor of 5 on 8K Blue Gene/Q nodes.

cs.DC↗

arXiv2019

High-energy coherent X-ray diffraction microscopy of polycrystal grains: first steps towards a multi-scale approach

We present proof-of-concept imaging measurements of a polycrystalline material that integrate the elements of conventional high-energy X-ray diffraction microscopy with coherent diffraction imaging techniques, and that can enable in-situ strain-sensitive imaging of lattice structure in ensembles of deeply embedded crystals over five decades of length scale upon full realization. Such multi-scale imaging capabilities are critical to addressing important questions in a variety of research areas such as materials science and engineering, chemistry, and solid state physics. Towards this eventual goal, the following key aspects are demonstrated: 1) high-energy Bragg coherent diffraction imaging (HE-BCDI) of sub-micron-scale crystallites at 52 keV at current third-generation synchrotron light sources, 2) HE-BCDI performed in conjunction with far-field high-energy diffraction microscopy (ff-HEDM) on the grains of a polycrystalline sample in an smoothly integrated manner, and 3) the orientation information of an ensemble of grains obtained via ff-HEDM used to perform complementary HE-BCDI on multiple Bragg reflections of a single targeted grain. These steps lay the foundation for integration of HE-BCDI, which typically provides a spatial resolution tens of nanometers, into a broad suite of well-established HEDM methods, extending HEDM beyond the few-micrometer resolution bound and into the nanoscale, and positioning the approach to take full advantage of the orders-of-magnitude improvement of X-ray coherence expected at fourth generation light sources presently being built and commissioned worldwide.

cond-mat.mtrl-sci↗

arXiv2017

Microstructure analysis of bismuth absorbers for transition-edge sensor X-ray microcalorimeters

Transition-edge sensors (TESs) as microcalorimeters offer high resolving power, owning to their sharp response and low operating temperature. In the hard X-ray regime and above, the demand for high quantum-efficiency requires the use of absorbers. Bismuth (Bi), owing to its low heat carrier density and high X-ray stopping power, has been widely used as an absorber material for TESs. However, distinct spectral responses have been observed for Bi absorbers deposited via evaporation versus electroplating. Evaporated Bi absorbers are widely observed to have a non-Gaussian tail on the low energy side of measured spectra. In this study, we fabricated Bi absorbers via these two methods, and performed microstructure analysis using scanning electron microscopy (SEM) and X-ray diffraction microscopy. The two types of material showed the same crystallographic structure, but the grain size of the evaporated Bi was about 40 times smaller than that of the electroplated Bi. This distinction in grain size is likely to be the cause of their different spectral responses.

physics.ins-det↗

arXiv2017

Eliminating the non-Gaussian spectral response of X-ray absorbers for transition-edge sensors

Transition-edge sensors (TES) as microcalorimeters for high-energy-resolution X-ray spectroscopy are often fabricated with an absorber made of materials with high Z (for X-ray stopping power) and low heat capacity (for high resolving power). Bismuth represents one of the most compelling options. TESs with evaporated bismuth absorbers have shown spectra with undesirable and unexplained low-energy tails. We have developed TESs with electroplated bismuth absorbers over a gold layer that are not afflicted by this problem and that retain the other positive aspects of this material. To better understand these phenomena, we have studied a series of TESs with gold, gold/evaporated bismuth, and gold/electroplated bismuth absorbers, fabricated on the same die with identical thermal coupling. We show that bismuth morphology is linked to the spectral response of X-ray TES microcalorimeters.

physics.ins-det↗

arXiv2017

Influences of Granular Constraints and Surface Effects on the Heterogeneity of Elastic, Superelastic, and Plastic Responses of Polycrystalline Shape Memory Alloys

Deformation heterogeneities within microstructures of polycrystalline shape memory alloys (SMAs) during superelastic stressing are studied using both experiments and simulations. In situ X-ray diffraction, specifically the far-field high energy diffraction microscopy (ff-HEDM) technique was used to non-destructively measure the grain-averaged statistics of position, crystal orientation, elastic strain tensor and volume for hundreds of austenite grains in a superelastically loaded nickel-titanium (NiTi) SMA. This experimental data were also used to create a synthetic microstructure within a finite element model. The development of intragranular stresses were then simulated during tensile loading of the model using anisotropic elasticity. Driving forces for phase transformation and slip were calculated from these stresses. The grain-average responses of individual austenite crystals examined before and after multiple stress-induced transformation events showed that grains at the specimen interior carry more stress and plastically deform less than the surface grains as the superelastic response shakes down. Examination of the heterogeneity within individual grains showed that regions near grain boundaries exhibit larger stress variation compared to the grain interiors. This intragranular heterogeneity is more strongly driven by the constraints of neighboring grains than the initial stress state and orientation of the individual grains.

cond-mat.mtrl-sci↗