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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Linking transportation agent-based model ($\mathrm{ABM}$) outputs with micro-urban social types ($\mathrm{MUSTs}$) via typology transfer for improved community relevance

The human relationship with transportation is shaped by social, economic, demographic, and urban form variables, or socio-spatial factors. The spatial dynamics of these are key to generating and interpreting outputs of transportation models that are most relevant for a community and the diverse mobility needs of its members. Here we present a typology transfer framework, grounded in socio-spatial dynamics shaping people's mobility, to take transportation-themed regional mobility model outcomes, in this case from two agent-based models (ABMs), and extrapolate them to other cities, with less time and resource intensity than new ABM development. The typology transfer process first identifies micro-urban social types (MUSTs) using socio-spatial factors, then defines city types based on spatial patterns of MUSTs to assess across which cities transfer results are likely to best hold. Lastly, a typology transfer multiplier matrix extrapolates a given variable, in our case the Mobility Energy Productivity (MEP) metric, to another city. The full process demonstration uses ABM results from Chicago (POLARIS model) and San Francisco (BEAM model), applying them to New York City. We discuss how MEP or other outputs can be appropriately estimated and used for integrated, human-centered mobility analysis. Key findings include that this MUST framework of user-defined dependent and independent variables allows tailoring ABM results and interpretations to specific community needs and data availability. Findings clarify that positive outcomes can be targeted towards user groups, based on sociospatial characteristics, using a typology approach, such as inclusive access to mobility choices, transportation affordability, and greater efficiency in resource use.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

The stability of Cl-, Br-, and I-passivated Si(100)-(2 × 1) in ambient environments for atomically-precise pattern preservation

Abstract Atomic precision advanced manufacturing (APAM) leverages the highly reactive nature of Si dangling bonds relative to H- or Cl-passivated Si to selectively adsorb precursor molecules into lithographically defined areas with sub-nanometer resolution. Due to the high reactivity of dangling bonds, this process is confined to ultra-high vacuum (UHV) environments, which currently limits its commercialization and broad-based appeal. In this work, we explore the use of halogen adatoms to preserve APAM-derived lithographic patterns outside of UHV to enable facile transfer into real-world commercial processes. Specifically, we examine the stability of H-, Cl-, Br-, and I-passivated Si(100) in inert N 2 and ambient environments. Characterization with scanning tunneling microscopy and x-ray photoelectron spectroscopy (XPS) confirmed that each of the fully passivated surfaces were resistant to oxidation in 1 atm of N 2 for up to 44 h. Varying levels of surface degradation and contamination were observed upon exposure to the laboratory ambient environment. Characterization by ex situ XPS after ambient exposures ranging from 15 min to 8 h indicated the Br– and I–passivated Si surfaces were highly resistant to degradation, while Cl–passivated Si showed signs of oxidation within minutes of ambient exposure. As a proof-of-principle demonstration of pattern preservation, a H–passivated Si sample patterned and passivated with independent Cl, Br, I, and bare Si regions was shown to maintain its integrity in all but the bare Si region post-exposure to an N 2 environment. The successful demonstration of the preservation of APAM patterns outside of UHV environments opens new possibilities for transporting atomically-precise devices outside of UHV for integrating with non-UHV processes, such as other chemistries and commercial semiconductor device processes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Liquid Piston with Spray Cooling Near-Isothermal Compressor

The goal of this project was to prototype and characterize the performance of a liquid-piston spray-cooled gas compressor. The working principle of the compressor enables optimized high-efficiency operation over a very wide range of operating conditions, unlike conventional compressors that are optimized for a narrow range of operating conditions. The compressor technology is suitable for many applications, such as gas pipeline transport, gas storage, and commercial and residential heat pumps. Both physical testing and computational fluid dynamics (CFD) modeling of the processes using the Oak Ridge National Laboratory high-performance computing center were completed. The experimental and CFD studies focused on a near-isothermal liquid-piston compressor (LPC) that uses propylene glycol to compress CO 2 . The first prototype demonstrated isothermal operation during several sequentially executed cycles of CO 2 compression and raised the temperature of the compressed CO 2 by only 2 K, compared with approximately 6 K when the gas was compressed non-isothermally. Isothermal operation was demonstrated at CO 2 flow rates of up to 2 L/min. The second prototype was designed with two compression chambers to allow continuous flow of high-pressure CO 2 . However, the design of the valve train to direct flow between the compression chambers was not sufficient to allow demonstration of CO 2 compression. Numerical simulations of the LPC in which the compression chamber was filled with propylene glycol injected from the bottom inlet were performed using large eddy simulation (LES) with the wall-adapting local eddy-viscosity subgrid-scale model coupled with the multiphase volume of fluid (VOF) model to simulate the transient interface between gas and liquid and to capture the heat and mass transfers within the compression chamber. In this effort, the effects of boundary conditions applied to the LES-VOF calculations (i.e., no wall, an adiabatic wall, and a wall with a heat flux subscribed) on the overall pressure and temperature of the CO 2 gas as well as the transient evolution of flow and heat transfer within the compression chamber were investigated and are discussed in this report. The LES calculation with no wall showed no dynamical flow patterns, and the volume-averaged temperature of CO 2 increased from 305 to 392.7 K, whereas LES calculations with a constant wall temperature or a wall heat flux had similar increases of CO 2 temperatures. The results of the LES simulation using a wall heat flux showed different stages in the compression process and revealed dynamical formation and interaction of CO 2 gas layers and circulation flow patterns within the chamber that contributed to the overall heat transfer between the solid wall, gas, and liquid surface in the compressor. Though an industrial partnership for commercializing the compressor was not secured, the technology was attractive for an industrial partner to use in two research proposals in response to US Department of Energy funding opportunity announcements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Side Chain Dynamics of Poly(norbornene)- g -Poly(propylene oxide) Bottlebrush Polymers

The segmental dynamics of the side chains of poly(norbornene)- g -poly(propylene oxide) (PNB- g -PPO) bottlebrush polymer in comparison to PPO is studied by quasi-elastic neutron scattering. Having experimental time and length scale information simultaneously allows to extract spatial information in addition to relaxation time. Tethering one end of the PPO side chain onto a stiff PNB backbone slows down the segmental relaxation, over the length and time scales investigated. The power law dependence of the relaxation time on the momentum transfer, Q , indicates a more heterogeneous relaxation pattern for the bottlebrush polymer, whereas the linear PPO has less deviations from a homogenous relaxation. Similar conclusions can be drawn from the time dependent mean square displacement, $\langle$ r 2 ( t )$\rangle$, and the non-Gaussian parameter, α 2 ( t ). Herein, the bottlebrush polymer shows a more restricted dynamics, whereas the linear PPO reaches $\langle$ r 2 ( t )$\rangle$∝ t 0.5 at the highest temperature. The deviations from Gaussian behavior are evident at the α 2 ( t ). Both samples show a decaying α 2 ( t ). Finally, the non-Gaussian parameter supports the results from the power law dependence of the relaxation times, with lower α 2 ( t ) values for PPO compared to those for PNB-g-PPO, pointing to less deviations from Gaussian behavior.

36 MATERIALS SCIENCE↗

DOE OSTI2025

A Unified Interpretation of Variability in Precipitation Isotope Ratios

Abstract Several mechanisms have been proposed to explain why the isotope ratios of precipitation vary in space and time and why they correlate with other climate variables like temperature and precipitation. Here, we argue that this behavior is best understood through the lens of radiative transfer, which treats the depletion of atmospheric vapor transport by precipitation as analogous to the attenuation of light by absorption or scattering. Building on earlier work by Siler et al., we introduce a simple model that uses the equations of radiative transfer to approximate the two-dimensional pattern of the oxygen isotope composition of precipitation ( δ p ) from monthly mean hydrologic variables. The model accurately simulates the spatial and seasonal variability in δ p within a state-of-the-art climate model and permits a simple decomposition of δ p variability into contributions from gradients in evaporation and the length scale of vapor transport. Outside the tropics, δ p is mostly controlled by gradients in evaporation, whose dependence on temperature explains the positive correlation between δ p and temperature (i.e., the temperature effect). At low latitudes, δ p is mostly controlled by gradients in the transport length scale, whose inverse relationship with precipitation explains the negative correlation between δ p and precipitation (i.e., the amount effect). This suggests that the temperature and amount effects are both mostly explained by the variability in upstream rainout, but they reflect distinct mechanisms governing rainout at different latitudes. Significance Statement The isotopic composition of precipitation has long been used to make inferences about past climates based on its observed relationship with precipitation in the tropics and with temperature at higher latitudes. These relationships—known as the “amount effect” and “temperature effect,” respectively—have been attributed to many different mechanisms, most of which are thought to operate at either high or low latitudes but not both. Here, we present a unified framework for interpreting the isotope variability that can explain the latitude dependence of the temperature and amount effects despite making no distinction between high and low latitudes. Although our results are generally consistent with certain interpretations of the amount effect, they suggest that the temperature effect is widely misunderstood.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Numerical Study of Liquid Piston Compression Using Large-Eddy Simulation and Volume-of-Fluid Approach

Efforts to increase the efficiency of residential and commercial air conditioners and heat pumps have demonstrated that the compressor accounts for most of the system’s electrical energy usage. Therefore, the efficiency of this component should be improved to reduce its energy usage. The US Department of Energy’s Oak Ridge National Laboratory developed a near-isothermal liquid piston compressor (LPC) that uses propylene glycol (PG) to compress CO2. This report presents numerical studies of the LPC in which the compression chamber fills with injected PG from the bottom inlet. Numerical simulations were performed using the large-eddy simulation (LES) with the wall-adapting local eddy-viscosity (WALE) subgrid-scale model coupled with the multiphase volume-of-fluid (VOF) model to simulate the transient interface between gas and liquid and to capture the heat and mass transfer within the compression chamber. In this effort, effects of boundary conditions applied to the LES-VOF calculations (e.g., no wall, an adiabatic wall, and a wall with a heat flux subscribed) to the overall pressure and temperature of CO2 gas as well as the transient evolution of flow and heat transfer evolution within the compression chamber are investigated and discussed. It was found that the LES calculation with no wall have shown no dynamical flow patterns and the volume-averaged temperature of CO2 increased from 305 to 392.7 K, while LES calculations with a constant wall temperature or a wall heat flux had similar increases of CO2 temperatures. Results of LES simulation using a wall heat flux showed different stages in the compression process and revealed dynamical formation and interaction of CO2 gas layers and circulation flow patterns within the chamber that contribute to the overall heat transfer between the solid wall, gas, and liquid surface in the compressor.

Nguyen, Thien D.↗

DOE OSTI2021

High flux boiling heat transfer enhancement using triangle shaped vertical walls in two-phase microchannel heat exchangers

Two-phase heat exchangers allow heat loads to be dissipated with lower fluid flow rates without inducing thermal gradients, and high surface area microchannels further reduce the thermal resistance of these heat exchangers. However, there are many practical limits that prevent widespread use of two-phase microchannel heat exchangers. Many authors have looked to address these practical limits by treating the channel floors or creating artificial nucleation sites. Few works look to enhance heat transfer in the walls of channels with hydraulic diameters below 200 μm. This work is aimed at increasing the efficacy of high aspect ratio silicon microchannels that use R134a as the working fluid. The 125 parallel channels are 40 μm wide, 200 μm deep, and 2 mm long and contain a 15 μm wide, 150 μm long restriction at each inlet. The thermal resistance and dryout characteristics of traditional, plain-walled channels are compared to channels with 2 area enhancement patterns each of which increases the area available for heat transfer by 41%. Furthermore, a resistance to dryout is seen in both area-enhanced test sections, and a 15.5% reduction in thermal resistance is shown for the area-enhanced part with a 6 μm triangular pitch. Only a 7.8% performance increase is seen in the test section with a 3 μm pitch. Both area enhancement patterns induce an average 27% increase in pressure drop for all tests. Heat transfer characteristics are solved for using a finite element analysis (FEA) model. By examining heat transfer coefficients and local heat fluxes, it is postulated that the area enhancement reduces heat transfer coefficients and the performance increase is caused by increasing the number of active nucleation sites.

42 ENGINEERING↗

DOE OSTI2022

Accelerating Hydraulic Fracture Imaging by Deep Transfer Learning

Deep transfer learning has a great success story in computer vision (CV), natural language processing, and many other fields. In this communication, we are going to push forward the deep transfer learning to the hydraulic fracture imaging problem by proposing a two-step approach: 1) train a convolutional neural network (CNN) to reconstruct target geometries by a relatively large amount of approximated field patterns generated from a simplified model and 2) fine-tune the top layers of transferred CNN by a small amount of true field patterns generated through a full model. The advantages include the rapid generation of large amount of data through the simplified model and the high reconstruction accuracy through a careful design of the deep transfer learning. Here, the CNN trained through the deep transfer learning can accurately reconstruct the lateral extent and direction of fractures with unseen conductivity and white Gaussian noise, showing a notable acceleration/accuracy enhancement over the previous CNN trained by mixing a small number of true data into the approximated data as data augmentation.

02 PETROLEUM↗

DOE OSTI2023

A cellular selection identifies elongated flavodoxins that support electron transfer to sulfite reductase

Flavodoxins (Flds) mediate the flux of electrons between oxidoreductases in diverse metabolic pathways. To investigate whether Flds can support electron transfer to a sulfite reductase (SIR) that evolved to couple with a ferredoxin, we evaluated the ability of Flds to transfer electrons from a ferredoxin-NADP reductase (FNR) to a ferredoxin-dependent SIR using growth complementation of an Escherichia coli strain with a sulfur metabolism defect. We show that Flds from cyanobacteria complement this growth defect when coexpressed with an FNR and an SIR that evolved to couple with a plant ferredoxin. When we evaluated the effect of peptide insertion on Fld-mediated electron transfer, we observed a sensitivity to insertions within regions predicted to be proximal to the cofactor and partner binding sites, while a high insertion tolerance was detected within loops distal from the cofactor and within regions of helices and sheets that are proximal to those loops. Bioinformatic analysis showed that natural Fld sequence variability predicts a large fraction of the motifs that tolerate insertion of the octapeptide SGRPGSLS. In conclusion, these results represent the first evidence that Flds can support electron transfer to assimilatory SIRs, and they suggest that the pattern of insertion tolerance is influenced by interactions with oxidoreductase partners.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Photothermal heterodyne imaging of micron-sized objects

Micron-sized dye-doped polymer beads were imaged using transmitted/reflected light microscopy and photothermal heterodyne imaging (PHI) measurements. The transmitted/reflected light images show distinct ring patterns that are attributed to diffraction effects and/or internal reflections within the beads. In the PHI experiments pump laser induced heating changes the refractive index and size of the bead, which causes changes in the diffraction pattern and internal reflections. This creates an analogous ring pattern in the PHI images. The ring pattern disappears in both the reflected light and PHI experiments when an incoherent light source is used as a probe. When the beads are imaged in an organic medium heat transfer changes the refractive index of the environment, and gives rise to a ring pattern external to the beads in the PHI images. This causes the beads to appear larger than their physical dimensions in PHI experiments. This external signal does not appear when the beads are imaged in air because the refractive index changes in air are very small.

Bhandari, Janak (ORCID:0000000188423259)↗

DOE OSTI2025

Quantitative stable isotope probing (qSIP) and cross-domain networks reveal bacterial-fungal interactions in the hyphosphere

Interactions between fungi and bacteria have the potential to substantially influence soil carbon dynamics in soil, but we have yet to fully identify these interactions and partners in their natural environment. In this study, we stacked two powerful methods, 13 C quantitative stable isotope probing (qSIP) and cross-domain co-occurrence network, to identify interacting fungi and bacteria in a California grassland soil. We used in-field whole plant 13 CO 2 labeling along with sand-filled ingrowth bags (that trap fungi and hyphae-associated bacteria) to amplify the signal of fungal-bacterial interactions, separate from the bulk soil background. We found a total of 54 bacterial ASVs and 9 fungal OTUs that were significantly 13 C-enriched. These were saprotrophic and biotrophic fungi, and motile, sometimes predatory bacteria. Among these, 70% of all 13 C-enriched bacteria identified were motile. Notably, we detected fungal-bacterial network links between a fungal OTU of the genus Alternaria and several bacterial ASVs of the genera Bacteriovorax, Mucilaginibacter, and Flavobacterium, providing empirical evidence of their direct interactions through C exchange. We observed a strong positive co-occurrence pattern between predatory bacteria of the phylum Bdellovibrionota and fungal OTUs, suggesting the transfer of C across the soil food web. To date, our ability to associate microbial co-occurrence network patterns with biological interactions is limited, but the incorporation of qSIP allowed us to more precisely detect interacting partners by narrowing in on the taxa that were actively incorporating plant-fixed, fungal-transported labeled substrates. Together, these approaches can help build a mechanistic understanding of the complex nature of fungal-bacterial interactions in soil.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Limiting the number of quantisation levels of a harmonic lens as a method for improving the quality of the generated image

The characteristics (modulation transfer functions) of harmonic lenses made by direct laser patterning of resist are experimentally studied. The influence of technological errors in the manufacture of lenses on the formation of the point spread function is modelled for harmonic lenses with 32 and 256 quantisation levels. Direct laser writing is used to produce lenses with these numbers of levels. It is experimentally shown that the modulation transfer function of the harmonic lens with 32 quantisation levels is significantly greater at high spatial frequencies (50 mm{sup −1}). (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Understanding and Leveraging the I/O Patterns of Emerging Machine Learning Analytics

The scientific community is currently experiencing unprecedented amounts of data generated by cutting-edge science facilities. Soon facilities will be producing up to 1 PB/s which will force scientist to use more autonomous techniques to learn from the data. The adoption of machine learning methods, like deep learning techniques, in large-scale workflows comes with a shift in the workflow’s computational and I/O patterns. These changes often include iterative processes and model architecture searches, in which datasets are analyzed multiple times in different formats with different model configurations in order to find accurate, reliable and efficient learning models. This shift in behavior brings changes in I/O patterns at the application level as well at the system level. These changes also bring new challenges for the HPC I/O teams, since these patterns contain more complex I/O workloads. In this paper we discuss the I/O patterns experienced by emerging analytical codes that rely on machine learning algorithms and highlight the challenges in designing efficient I/O transfers for such workflows. We comment on how to leverage the data access patterns in order to fetch in a more efficient way the required input data in the format and order given by the needs of the application and how to optimize the data path between collaborative processes. We will motivate our work and show performance gains with a study case of medical applications.

Gainaru, Ana↗

DOE OSTI2021

Heat Transfer and Pressure Drop Characteristics of Water Flow Boiling in Internally Enhanced Tubes

Flow boiling is a critical process for many thermal conversion processes such as HVAC&R, water heating, power generation and desalination applications. The major goal of this paper is to enhance the in-tube heat transfer for flow boiling of water and further improve the efficiency of the boiler. This paper presents an experimental investigation of the heat transfer and pressure drop characteristics for water under forced convection and boiling conditions inside both smooth and enhanced tubes. The results in the smooth copper tube are compared with several correlations and used as a baseline. The two-phase heat transfer and pressure drop performance of the water flow boiling in the internally enhanced tube is investigated, and the influence of internal surface pattern on the thermal characteristics is discussed. The relations of the heat transfer coefficient with mass flux, heat flux and vapor quality are analyzed according to the results.

Yang, Cheng-Min↗

DOE OSTI2025

Structure–Function Relationships in Sequence-Controlled Copolymers for Rare Earth Element Chelation

The ability to tune material function through primary sequence is a defining feature of biological macromolecules, allowing precise control over structure and target interactions in complex aqueous environments. However, translating sequence–structure–function relationships to synthetic macromolecules is challenging due to their dispersity in sequence, conformation, and composition. Here, we report systematic studies of amphiphilic polymer chelators designed to probe how composition and patterning influence binding affinity and selectivity for rare earth elements (REEs), a series of technologically relevant metals with challenging separation profiles. A library of copolymers varying hydrophobic monomer composition and patterning was synthesized via reversible addition–fragmentation chain transfer (RAFT) polymerization, spanning statistical, gradient, and block architectures. REE binding was quantified using a high-throughput colorimetric assay, and reconstruction of polymer ensembles using kinetic stochastic simulations enabled quantitative comparisons of sequence heterogeneity, linking local monomer colocalization to emergent REE binding. Further, we investigated the role of different hydrophobic comonomers in tuning metal coordination, with binding trends linked to structural features that influence binding site desolvation. Complementary dynamic light scattering (DLS) and small-angle X-ray scattering (SAXS) measurements showed that both polymer and monomer architecture modulate metal-induced conformational changes, and that multichain assembly behavior emerges beyond critical hydrophobic thresholds. Sequence control also altered REE selectivity, with nonmonotonic differences observed across compositionally identical polymers with different sequence architectures. Together, these findings establish design principles that connect polymer sequence and structure to binding performance, guiding the design of macromolecular chelators with enhanced affinity and selectivity for applications in separations, sensing, and catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Tissue-preferential recruitment of electron transfer chains for cytochrome P450-catalyzed phenolic biosynthesis

Cytochrome P450 system consists of P450 monooxygenase and redox pattern(s). While the importance of monooxygenases in plant metabolism is well documented, the metabolic roles of the related redox components have been largely overlooked. Here, we show that distinct electron transfer chains are recruited in phenylpropanoid-monolignol P450 systems to support the synthesis of different classes of phenolics in different plant tissues. While Arabidopsis cinnamate 4-hydroxylase adopts conventional NADPH-cytochrome P450 oxidoreductase (CPR) electron transfer chain for its para-hydroxylation, ferulate 5-hydroxylase employs both NADPH-CPR cytochrome b 5 (CB5) and NADH-cytochrome b 5 reductase (CBR)-CB5 chains to support benzene ring 5-hydroxylation. Moreover, the former route is primarily recruited in the stem for syringyl lignin synthesis, while the latter dominates in the syntheses of 5-hydroxylated phenolics in seeds and seed coat suberin. The availability of reductants NADPH and NADH and the catalytic properties of both reductases to CB5 contribute to the tissue-preferential recruitment of electron transfer systems. Our study unveils an additional layer of complexity and versatility of P450 system that the higher plants evolved for diversifying phenolic repertoires.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Present and Future Changes in Land‐Atmosphere Coupling of Water and Energy Over Extratropical Forest Regions

Abstract Couplings between land and the near surface atmosphere are modulated by interactions between soil conditions, vegetation dynamics, turbulent fluxes, and atmospheric properties. How the land‐atmosphere coupling responds to warming and elevated CO 2 are important for understanding the land surface carbon, energy, and water cycles. In this work, we documented this coupled land‐atmosphere network based on observations and the Energy Exascale Earth System Model (E3SM) simulations over extratropical forest ecosystems. We employed a transfer entropy approach and novel network metrics to reveal patterns and strength of the land‐atmosphere coupling under historical conditions and a future high emission scenario (SSP585). We found that, in observations, the present‐day extratropical forest coupling network has high network connectivity (72%–88% of the targeted processes are coupled). E3SM reasonably captured the extratropical forest coupling network (modeled network connectivity was 81%–96%) and predicted that the coupling strength would significantly increase by 28% (±3%) under warming and elevated CO 2 conditions. Furthermore, E3SM factorial coupled experiments suggested that warming enhanced soil nitrogen mineralization favoring plant nitrogen uptake and vegetation growth were responsible for the strengthening future land‐atmosphere coupling. This work provides new metrics to analyze and document complex couplings for coupled earth system processes and highlights the important roles soil nutrient availability and biogeochemistry have on land‐atmosphere coupling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Particle track classification using quantum associative memory

Pattern recognition algorithms are commonly employed to simplify the challenging and necessary step of track reconstruction in sub-atomic physics experiments. Aiding in the discrimination of relevant interactions, pattern recognition seeks to accelerate track reconstruction by isolating signals of interest. In high collision rate experiments, such algorithms can be particularly crucial for determining whether to retain or discard information from a given interaction even before the data is transferred to tape. As data rates, detector resolution, noise, and inefficiencies increase, pattern recognition becomes more computationally challenging, motivating the development of higher efficiency algorithms and techniques. Quantum associative memory is an approach that seeks to exploits quantum mechanical phenomena to gain advantage in learning capacity, or the number of patterns that can be stored and accurately recalled. Here, we study quantum associative memory based on quantum annealing and apply it to the particle track classification. We focus on discrimination models based on Ising formulations of quantum associative memory model (QAMM) recall and quantum content-addressable memory (QCAM) recall. We characterize classification performance of these approaches as a function detector resolution, pattern library size, and detector inefficiencies, using the D-Wave 2000Q processor as a testbed. Discrimination criteria is set using both solution-state energy and classification labels embedded in solution states. We find that energy-based QAMM classification performs well in regimes of small pattern density and low detector inefficiency. In contrast, state-based QCAM achieves reasonably high accuracy recall for large pattern density and the greatest recall accuracy robustness to a variety of detector noise sources.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗