Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “characterization”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Focused‐beam X‐ray fluorescence and diffraction microtomographies for mineralogical and chemical characterization of unsectioned extraterrestrial samples

Abstract This study describes the application of new synchrotron X‐ray fluorescence (XRF) and diffraction (XRD) microtomographies for the 3‐D visualization of chemical and mineralogical variations in unsectioned extraterrestrial samples. These improved methods have been applied to three compositionally diverse chondritic meteorite samples that were between 300 and 400 μm in diameter, including samples prepared from fragments of the CR2 chondrite LaPaz Icefield (LAP) 02342, H5 chondrite MacAlpine Hills (MAC) 88203, and the CM2 chondrite Murchison. The synchrotron‐based XRF and XRD tomographies used are focused‐beam techniques that measure the intensities of fluorescent and diffracted X‐rays in a sample simultaneously during irradiation by a high‐energy microfocused incident X‐ray beam. Measured sinograms of the emitted and diffracted intensities were then tomographically reconstructed to generate 2‐D slices of XRF and XRD intensity through the sample, with reconstructed pixel resolution of 1–2 μm, defined by the resolution of the focused incident X‐ray beam. For sample LAP 02342, primary mineral phases that were visualized in reconstructed slices using these techniques included isolated grains of α‐Fe, orthopyroxene, and olivine. For our sample of MAC 88203, XRF/XRD tomography allowed visualization of forsteritic olivine as a primary mineral phase, a vitrified fusion crust at the sample surface, identification of localized Cr‐rich spinels at spatial resolutions of several micrometers, and imaging of a plagioclase‐rich glassy matrix. In the sample of Murchison, major identifiable phases include clinoenstatite‐ and olivine‐rich chondrules, variable serpentine matrix minerals and small Cr‐rich spinels. Most notable in the tomographic analysis of Murchison is the ability to quantitatively distinguish and visualize the complex mixture of serpentine‐group minerals and associated tochilinite–cronstedtite intergrowths. These methods provide new opportunities for spatially resolved characterization of sample texture, mineralogy, crystal structure, and chemical state in unsectioned samples. This provides researchers an ability to characterize such samples internally with minimal disruption of sample micro‐structures and chemistry, possibly without the need for sample extraction from some types of sampling and capture media.

Geochemistry & Geophysics↗
DOE OSTI2022

Combining GWAS and population genomic analyses to characterize coevolution in a legume‐rhizobia symbiosis

Abstract The mutualism between legumes and rhizobia is clearly the product of past coevolution. However, the nature of ongoing evolution between these partners is less clear. To characterize the nature of recent coevolution between legumes and rhizobia, we used population genomic analysis to characterize selection on functionally annotated symbiosis genes as well as on symbiosis gene candidates identified through a two‐species association analysis. For the association analysis, we inoculated each of 202 accessions of the legume host Medicago truncatula with a community of 88 Sinorhizobia (Ensifer) meliloti strains. Multistrain inoculation, which better reflects the ecological reality of rhizobial selection in nature than single‐strain inoculation, allows strains to compete for nodulation opportunities and host resources and for hosts to preferentially form nodules and provide resources to some strains. We found extensive host by symbiont, that is, genotype‐by‐genotype, effects on rhizobial fitness and some annotated rhizobial genes bear signatures of recent positive selection. However, neither genes responsible for this variation nor annotated host symbiosis genes are enriched for signatures of either positive or balancing selection. This result suggests that stabilizing selection dominates selection acting on symbiotic traits and that variation in these traits is under mutation‐selection balance. Consistent with the lack of positive selection acting on host genes, we found that among‐host variation in growth was similar whether plants were grown with rhizobia or N‐fertilizer, suggesting that the symbiosis may not be a major driver of variation in plant growth in multistrain contexts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Characterization and Scaling of Forced Convective Swirl in Sinusoidal Wavy-Plate-Fin Cores of Compact Heat Exchangers

The characterization and scaling of the thermal-hydraulic performance in wavy plate-fin compact heat exchanger cores, based on the understanding of the physical phenomena and heat transfer enhancement mechanism is delineated. Experimental data are presented for forced convection in air (Pr = 0.71) with flow rates in the range 50 ≤ Re ≤ 4000. A variety of wavy-fin cores that span viable applications, with geometrical attributes described by the cross section aspect ratio α (ratio of fin spacing to height), fin corrugation aspect ratio γ (ratio of 2× corrugation amplitude to wave pitch), and fin spacing ratio ζ (ratio of fin spacing to wave pitch), are considered. To characterize and correlate the vortex-flow mixing in interfin spaces, a Swirl number Sw is introduced from the balance of viscous, inertial and centrifugal forces. It is shown that the laminar, transitional and turbulent flow regimes can be explicitly identified by this Swirl number. Based on the experimental results and extended analysis, new correlations for Fanning friction factor f and Colburn factor j are developed with Sw, α, γ, and ζ as scaling parameters. Requisite expressions are devised by a superposition of both enhancement components due to the corrugated surface area enlargement and induced swirl flow field, and they are combined to cover the laminar, transitional and turbulent regimes by the method of asymptotic matching. The resulting generalized correlations are further shown to predict all available experimental data for f and j factors to within ±20% and ±15%, respectively.

42 ENGINEERING↗
DOE OSTI2026

Characterization of Flashback and Flame-Holding in a Jet-in-Crossflow Mixing Configuration With Methane–Hydrogen Fuel Blends

An approach that combines experimental and numerical analyses has been implemented to characterize the fundamentals of flashback events and flame-holding phenomena during high-hydrogen combustion in a jet-in-crossflow (JICF) configuration. Such flame dynamics are visualized experimentally using nanosecond (ns)-based hydroxyl planar laser-induced fluorescence (OH-PLIF) and chemiluminescence diagnostics techniques. The JICF burner has an optically accessible premixing tube allowing the optical diagnostics. The testing was conducted for varied premixer velocities (V) and equivalence ratio (ϕ) for 90–100% (H 2 , by mole) H 2 /CH 4 reactant mixtures at atmospheric temperature and pressure conditions. Two distinct flashback events were identified – conventional rich flashback and lean flashback, recorded while increasing ϕ and decreasing ϕ, respectively. The cause of lean flashback was attributed to lower momentum flux ratio which bends the jet sharply, closer toward the injection plane. The mean OH-PLIF images characterized the flame-holding behavior, where the flame was found to be stabilized on the leeward side only or on both windward and leeward sides as a lifted flame near the fuel port. A large eddy simulation (LES) with detailed chemistry combustion modeling approach was implemented along with an OH* submechanism, and it showed qualitative agreement with the integrated line-of-sight chemiluminescence results as well as the planar OH-PLIF measurements.

chemiluminescence diagnostic↗
DOE OSTI2022

Characterization of Properties of Laser Powder Bed Fusion 3D-Printed Inconel 718 for Centrifugal Turbomachinery Applications

Here, this paper presents the results of a comprehensive effort to characterize the properties of Inconel 718 produced by a form of laser powder bed fusion (LPBF) additive manufacturing (AM) or 3D-printing, subsequently subjected to hot isostatic pressing (HIP) and heat treatment according to standards F3055-14a and AMS 5663, respectively. Material property data, while broadly available for traditional Inconel 718 presentations (e.g. forgings or castings), is currently lacking for the 3D-printed material. It is expected that while limited in size, the experimental data sets presented provide sufficient information to glean the capability of LPBF Inconel 718. These include: 1) Chemical composition, electron backscatter diffraction (EBSD), and x-ray energy dispersive spectroscopy (XEDS) characterization of 3D-printed material structure; 2) Tensile properties — 0.2% yield stress, ultimate stress, modulus of elasticity, and elongation to failure — based on 108 samples, as functions of temperature and sample print orientation; 3) Creep rupture data including the Larson-Miller parameter, based on 21 samples; and 4) High cycle fatigue data based on 21 samples as a function of temperature. Results are compared to available standards and/or data for forged, cast, and other AM Inconel 718. A key observation of this study, based on the EBSD results, is that while the material appears to approach full recrystallization following heat treatment, there is a detectable fraction of the material that does not fully recrystallize, resulting in a material with mechanical properties (e.g. yield stress, creep rupture) measurably lower than those of forgings, but higher than those of castings.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Use of Mini-CT Specimens for Fracture Toughness Characterization of Irradiated Highly Embrittled Weld

In the 1990’s, the Heavy Section Steel Irradiation Program at the ORNL performed investigation of the shape of the fracture toughness master curve for reactor pressure vessel steel highly embrittled because of irradiation exposure. A radiation-sensitive reactor pressure vessel (RPV) weld with intentionally enhanced copper content, designated KS-01, has been characterized in terms of static initiation (KJc) and Charpy impact toughness in the unirradiated and irradiated conditions. The objective of this original project was to investigate the ability of highly embrittled material to maintain the shape of the unirradiated transition fracture toughness curve, as well as to examine the ability of the Charpy 41-J shift to predict the fracture toughness shift at such high level of embrittlement. Irradiation of this weld was performed at the University of Michigan Ford Reactor in the new HSSI irradiation-anneal-reirradiation (IAR) facility. Broken specimens from that project have been saved in ORNL storage. To verify applicability of Mini-CT specimens for fracture toughness characterization of RPV materials as part of the DOE Light Water Reactor Sustainability Program, Mini-CT specimens were machined from broken Charpy specimens and tested according to ASTM E1921 standard. As result of this study, the fracture toughness of this weld derived by testing Mini-CT specimens in the unirradiated and irradiated conditions is compared to previously reported fracture toughness derived by large number of conventional specimens.

Sokolov, Mikhail A.↗
DOE OSTI2023

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗
DOE OSTI2025

Hybrid chemical characterization of latent images in EUV resist with 12 nm half-pitch features

With the advancement of high numerical aperture extreme ultraviolet (EUV) lithography, the new platform will enable chipmakers to achieve critical dimensions of 8 nm. However, resist materials face significant challenges in delivering increased sensitivity while managing rising stochastic variations. We aim to develop comprehensive techniques to characterize the chemical profile of latent images, stored in EUV resists after exposure and postexposure baking, which is essential for understanding the origin of stochastic effects. Infrared photo-induced force microscopy (IR PiFM) is a bimodal atomic force microscopy technique combined with an infrared light source, allowing for simultaneous sub-5 nm topographic and chemical characterization within a localized environment. Critical-dimension resonant soft X-ray scatterometry (CD-RSoXS) provides statistical data that reveal structural and chemical information for comparative analysis. For the first time, IR PiFM has been used to chemically map the latent images of EUV resists (after exposure and postexposure baking) at a record high resolution of 12 nm half-pitch, enabling nondestructive analysis of patterns prior to development. Furthermore, CD-RSoXS offers direct experimental observation and comparison of exposed, postexposure baked, and developed patterns, which align with the IR PiFM results. We demonstrate that the IR PiFM technique offers valuable insights into both high spatial resolution and local chemical information simultaneously. In addition, CD-RSoXS provides statistical results that support our main findings. This hybrid metrology approach leverages a multifaceted dataset by combining the most reliable information from each source, which is essential for a comprehensive understanding of the stochastic effects in EUV lithography processes.

O’Reilly, Padraic↗
DOE OSTI2026

Binary pseudo-random array standard for extreme ultraviolet lithography tool characterization

Extreme ultraviolet (EUV) imaging tools play a crucial role in EUV lithography. Achieving high accuracy in EUV metrology is essential for advanced semiconductor manufacturing. A thorough characterization of the instrumentation in use is required. Binary pseudo-random arrays (BPRAs) are an established standard for calibrating and characterizing optical instruments in the frequency domain. Here, we expand the BPRA standard to applications in EUV imaging. To extend the technology to the EUV spectral range, a high-resolution BPRA target with the smallest feature size of 40 nm is developed. The EUV BPRA target establishes an in situ and portable calibration and alignment standard for EUV imaging. The target is patterned by means of electron-beam lithography, using a nickel absorber with a thickness of 39 nm. The substrate is a 4″ silicon wafer with a molybdenum/silicon multilayer coating. To demonstrate the efficacy of the target and develop the instrument calibration protocol, the target is imaged on the Sharp Hyper-NA Actinic Reticle Review Project EUV mask microscope. Power spectral density (PSD) data are presented. The characteristics of the imaging system are imprinted on the PSD. The modulation transfer function is extracted from the PSD data. A partially coherent imaging model is used as a reference to the experimental results.

BPRA↗
DOE OSTI2020

Binary pseudorandom array test standard optimized for characterization of large field-of-view optical interferometers

Recently, a technique for calibrating the modulation transfer function (MTF) of a broad variety of metrology instrumentation has been demonstrated. This technique is based on test samples structured as one-dimensional binary pseudo-random (BPR) sequences and two-dimensional BPR arrays (BPRAs). The inherent power spectral density of BPR gratings (sequences) and arrays has a deterministic white-noise-like character that allows direct determination of the MTF with uniform sensitivity over the entire spatial frequency range and field-of-view of an instrument. As such, the BPR samples satisfy the characteristics of a test standard: functionality, ease of specification and fabrication, reproducibility, and low sensitivity to manufacturing error. Here we discuss our recent developments directed to the optimization of the sample design, fabrication, application, and data processing procedures, suitable for thorough characterization of large aperture optical interferometers. Compared with the previous coded-aperture based design, the improved, 'highly randomized' BPRA pattern of the new test standard provides better accuracy and reliability of instrument MTF and aberration characterization, and enables operation optimization of large aperture optical interferometers. We describe the pattern generation algorithm and tests to verify the compliance to desired BPRA topography. The data acquisition and analysis procedures for different applications of the technique are also discussed.

Yashchuk, Valeriy V.↗
DOE OSTI2024

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

The high spectral resolution afforded by Hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn't fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data.

• Artificial intelligence (AI) / machine learning ↗
DOE OSTI2023

Characterization of a novel aromatic substrate-processing microcompartment in Actinobacteria

ABSTRACT We have discovered a new cluster of genes that is found exclusively in the Actinobacteria phylum. This locus includes genes for the 2-aminophenol meta -cleavage pathway and the shell proteins of a bacterial microcompartment (BMC) and has been named aromatics (ARO) for its putative role in the breakdown of aromatic compounds. In this study, we provide details about the distribution and composition of the ARO BMC locus and conduct phylogenetic, structural, and functional analyses of the first two enzymes in the catabolic pathway: a unique 2-aminophenol dioxygenase, which is exclusively found alongside BMC shell genes in Actinobacteria, and a semialdehyde dehydrogenase, which works downstream of the dioxygenase. Genomic analysis reveals variations in the complexity of the ARO loci across different orders. Some loci are simple, containing shell proteins and enzymes for the initial steps of the catabolic pathway, while others are extensive, encompassing all the necessary genes for the complete breakdown of 2-aminophenol into pyruvate and acetyl-CoA. Furthermore, our analysis uncovers two subtypes of ARO BMC that likely degrade either 2-aminophenol or catechol, depending on the presence of a pathway-specific gene within the ARO locus. The precise precursor of 2-aminophenol, which serves as the initial substrate and/or inducer for the ARO pathway, remains unknown, as our model organism Micromonospora rosaria cannot utilize 2-aminophenol as its sole energy source. However, using enzymatic assays, we demonstrate the dioxygenase’s ability to cleave both 2-aminophenol and catechol in vitro , in collaboration with the aldehyde dehydrogenase, to facilitate the rapid conversion of these unstable and toxic intermediates. IMPORTANCE Bacterial microcompartments (BMCs) are proteinaceous organelles that are widespread among bacteria and provide a competitive advantage in specific environmental niches. Studies have shown that the genetic information necessary to form functional BMCs is encoded in loci that contain genes encoding shell proteins and the enzymatic core. This allows the bioinformatic discovery of BMCs with novel functions and expands our understanding of the metabolic diversity of BMCs. ARO loci, found only in Actinobacteria, contain genes encoding for phylogenetically remote shell proteins and homologs of the meta -cleavage degradation pathway enzymes that were shown to convert central aromatic intermediates into pyruvate and acetyl-CoA in gamma Proteobacteria. By analyzing the gene composition of ARO BMC loci and characterizing two core enzymes phylogenetically, structurally, and functionally, we provide an initial functional characterization of the ARO BMC, the most unusual BMC identified to date, distinctive among the repertoire of studied BMCs.

2-AP 1,6-dioxygenase↗
DOE OSTI2025

Genetic and biochemical characterization of a radical SAM enzyme required for post-translational glutamine methylation of methyl-coenzyme M reductase

ABSTRACT Methyl-coenzyme M reductase (MCR), the key catalyst in the anoxic production and consumption of methane, contains an unusual 2-methylglutamine residue within its active site. In vitro data show that a B12-dependent radical SAM (rSAM) enzyme, designated MgmA, is responsible for this post-translational modification (PTM). Here, we show that two different MgmA homologs are able to methylate MCR in vivo when expressed in Methanosarcina acetivorans , an organism that does not normally possess this PTM. M. acetivorans strains expressing MgmA showed small, but significant, reductions in growth rates and yields on methylotrophic substrates. Structural characterization of the Ni(II) form of Gln-methylated M. acetivorans MCR revealed no significant differences in the protein fold between the modified and unmodified enzyme; however, the purified enzyme contained the heterodisulfide reaction product, as opposed to the free cofactors found in eight prior M. acetivorans MCR structures, suggesting that substrate/product binding is altered in the modified enzyme. Structural characterization of MgmA revealed a fold similar to other B12-dependent rSAMs, with a wide active site cleft capable of binding an McrA peptide in an extended, linear conformation. IMPORTANCE Methane plays a key role in the global carbon cycle and is an important driver of climate change. Because MCR is responsible for nearly all biological methane production and most anoxic methane consumption, it plays a major role in setting the atmospheric levels of this important greenhouse gas. Thus, a detailed understanding of this enzyme is critical for the development of methane mitigation strategies.

Rodriguez Carrero, Roy J. (ORCID:0000000184475641)↗
DOE OSTI2022

Characterization of a 2016-2017 Human Seasonal H3 Influenza A Virus Spillover Now Endemic to U.S. Swine

In 2017, the Iowa State University Veterinary Diagnostic Laboratory detected a reverse-zoonotic transmission of a human seasonal H3 influenza A virus into swine (IAV-S) in Oklahoma. Pairwise comparison between the recently characterized human seasonal H3 IAV-S (H3.2010.2) hemagglutinin (HA) sequences detected in swine and the most similar 2016–2017 human seasonal H3 revealed 99.9% nucleotide identity. To elucidate the origin of H3.2010.2 IAV-S, 45 HA and 27 neuraminidase (NA) sequences from 2017 to 2020 as well as 11 whole-genome sequences (WGS) were genetically characterized. Time to most recent common human ancestor was estimated between August and September 2016. The N2 NA was of human origin in all but one strain from diagnostic submissions with NA sequences, and the internal gene segments from WGS consisted of matrix genes originating from the 2009 pandemic H1N1 and another 5 internal genes of triple reassortant swine origin (TTTTPT). Pigs experimentally infected with H3.2010.2 demonstrated efficient nasal shedding and replication in the lungs, mild pneumonia, and minimal microscopic lung lesions and transmitted the virus to indirect contact swine. Antigenically, H3.2010.2 viruses were closer to a human seasonal vaccine strain, A/Hong Kong/4801/2014, than to the H3.2010.1 human seasonal H3 viruses detected in swine in 2012. This was the second sustained transmission of a human seasonal IAV into swine from the 2010 decade after H3.2010.1. Monitoring the spillover and detection of novel IAV from humans to swine may help vaccine antigen selection and could impact pandemic preparedness.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Fast neutron background characterization of the future Ricochet experiment at the ILL research nuclear reactor

Abstract The future Ricochet experiment aims at searching for new physics in the electroweak sector by providing a high precision measurement of the Coherent Elastic Neutrino-Nucleus Scattering (CENNS) process down to the sub-100 eV nuclear recoil energy range. The experiment will deploy a kg-scale low-energy-threshold detector array combining Ge and Zn target crystals 8.8 m away from the 58 MW research nuclear reactor core of the Institut Laue Langevin (ILL) in Grenoble, France. Currently, the Ricochet Collaboration is characterizing the backgrounds at its future experimental site in order to optimize the experiment’s shielding design. The most threatening background component, which cannot be actively rejected by particle identification, consists of keV-scale neutron-induced nuclear recoils. These initial fast neutrons are generated by the reactor core and surrounding experiments (reactogenics), and by the cosmic rays producing primary neutrons and muon-induced neutrons in the surrounding materials. In this paper, we present the Ricochet neutron background characterization using $$^3$$ 3 He proportional counters which exhibit a high sensitivity to thermal, epithermal and fast neutrons. We compare these measurements to the Ricochet Geant4 simulations to validate our reactogenic and cosmogenic neutron background estimations. Eventually, we present our estimated neutron background for the future Ricochet experiment and the resulting CENNS detection significance. Our results show that depending on the effectiveness of the muon veto, we expect a total nuclear recoil background rate between 44 ± 3 and 9 ± 2 events/day/kg in the CENNS region of interest, i.e. between 50 eV and 1 keV. We therefore found that the Ricochet experiment should reach a statistical significance of 4.6 to 13.6 $$\sigma $$ σ for the detection of CENNS after one reactor cycle, when only the limiting neutron background is considered.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Spatial and Temporal Characterization of Polarization Entanglement

We describe the full temporal and spatial characterization of polarization-entangled photons produced by Spontaneous Parametric Down Conversions using an intensified high-speed optical camera, Tpx3Cam. This novel technique allows for precise determination of Bell inequality parameters and for new characterization methods for the spatial distribution of entangled quantum information. We also discuss a technique to synchronize multiple cameras separated by vast distances, which will be required for a distributed quantum network.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Automatic Qubit Characterization and Gate Optimization with QubiC

As the size and complexity of a quantum computer increases, quantum bit (qubit) characterization and gate optimization become complex and time-consuming tasks. Current calibration techniques require complicated and verbose measurements to tune up qubits and gates, which cannot easily expand to the large-scale quantum systems. We develop a concise and automatic calibration protocol to characterize qubits and optimize gates using QubiC, which is an open source FPGA (field-programmable gate array) based control and measurement system for superconducting quantum information processors. We propose multi-dimensional loss-based optimization of single-qubit gates and full XY-plane measurement method for the two-qubit CNOT gate calibration. We demonstrate the QubiC automatic calibration protocols are capable of delivering high-fidelity gates on the state-of-the-art transmon-type processor operating at the Advanced Quantum Testbed at Lawrence Berkeley National Laboratory. Finally, the single-qubit and two-qubit Clifford gate infidelities measured by randomized benchmarking are of 4.9(1.1) × 10 -4 and 1.4(3) × 10 -2 , respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Bayesian Approach for Characterizing and Mitigating Gate and Measurement Errors

Various noise models have been developed in quantum computing study to describe the propagation and effect of the noise that is caused by imperfect implementation of hardware. Identifying parameters such as gate and readout error rates is critical to these models. We use a Bayesian inference approach to identify posterior distributions of these parameters such that they can be characterized more elaborately. By characterizing the device errors in this way, we can further improve the accuracy of quantum error mitigation. Experiments conducted on IBM’s quantum computing devices suggest that our approach provides better error mitigation performance than existing techniques used by the vendor. Also, our approach outperforms the standard Bayesian inference method in some scenarios.

97 MATHEMATICS AND COMPUTING↗