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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.

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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Compliant heat exchangers, heat pipes and methods for making same

A method for fabricating heat exchangers using additive manufacturing technologies. Additive manufacturing enables the manufacture of heat exchangers with complex geometries and/or with internal and external integral surface features. Additive manufacture also facilitates the manufacture of heat exchangers with regional variations, such as changes in size, shape and surface features. In one embodiment, the present invention provides a heat exchanger with a helicoidal shape that provides axial elastic compliance. In one embodiment, the internal channel of the heat exchanger varies along its length. The internal channel may have a cross-sectional area that increases progressively from one end to the other. In one embodiment, the external shape of the tubular structure may be non-circular to optimize heat transfer with an external heat transfer fluid. In one embodiment, the present invention provides a heat pipe with an internal wicking structure formed as an integral part of the additive manufacturing process.

Lara-Curzio, Edgar↗
DOE OSTI2018

Enhancement of Structural Health Monitoring Framework by Optimizing Vibro-Acoustic Modulation Technique to Localize Alkali-Silica Reaction Degradation in Medium-Sized Concrete Samples

Assessment and management of aging concrete structures in nuclear power plants require a more systematic approach than simple reliance on existing code margins of safety. Health monitoring of concrete structures is performed to understand the current condition of a structure based on heterogeneous measurements and then produce high confidence actionable information regarding structural integrity. This information can then be used to support operational and maintenance decisions. The objectives of this ongoing research project focus on health monitoring and data analytics of concrete slabs containing reactive aggregates and thus subjected to degradation due to alkali-silica reaction (ASR). A controlled concrete slab with four pockets of reactive aggregates (pure silica, wells, placitas, and spratt) was cast at Vanderbilt University and cured in representative conditions to accelerate degradation due to ASR. A set of four concrete samples were also cast and cured at the University of Alabama for ASR testing. Of these four samples, two slabs contained reactive aggregates while the other two had the non-reactive aggregate counterparts mixed throughout the samples. Vibro-acoustic methods were used on these slabs to locate large pockets of ASR within the reactive samples and determine the effects of rebar using the non-reactive slabs. Vibro-acoustic modulation (VAM) is a vibration-based NDE method that utilizes signatures of nonlinear dynamic interactions on contact surfaces of crack or delamination damage to detect and localize the damage. VAM analysis was conducted on both Vanderbilt and Alabama’s samples using multiple variables for damage detection and localization. The results from the data analysis of the NDE vibro-acoustic techniques on concrete slabs cured at Vanderbilt University and the University of Alabama are discussed in detail in this report. The major outcome of the analysis suggests that the VAM method can be used to detect damage within the concrete samples by comparing the results found in different sections of the samples. Results for damage localization are dependent on multiple variables used in the vibro-acoustic modulation experiments. Digital image correlation (DIC) is a three-dimensional, full-field, optical NDE technique to measure contour, deformation, vibration, and strain. The application of the digital image correlation technique to study ASR-related degradation on a large concrete sample at the University of Tennessee Knoxville is discussed and observations are presented in this report.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Impact of electrode porosity architecture on electrochemical performances of 1 mm-thick LiFePO4 binder-free Li-ion electrodes fabricated by Spark Plasma Sintering

Thick electrodes with high active material loadings have been intensively studied over the last couple of decades in pursuit of achieving high energy density systems. To optimize and enhance the electrochemical performance of such electrodes, one has to control the pore morphology by, for example, varying the pore size and shape, and the level of porosity. In the present work, the fabrication of thick binder-free LiFePO4 (LFP) electrodes with two different pore sizes (12 and 20 mu m) and porosities (21 vol% and 44 vol%) using Spark Plasma Sintering (SPS) and templating approach is reported. The well-controlled porous architecture inside the thick electrodes is realized by fine-tuning experimental parameters. The impact of porosity architecture on electrochemical performance is quantified and correlated with the 3D tortuosity values determined from both micro-computed tomography and electrochemical impedance-based experimental methods. Based on the micro-computed tomography data analysis, estimated tortuosity values along X, Y, and Z axes reveal an anisotropic effect perpendicularly to the SPS compression axis (Z-direction). This is particularly profoundly observed in the samples with larger pores (20 mu m). The correlation between morphological properties and the rate capability performance is established indicating that the capacity loss happens mainly due to the Li-ion diffusion limitations.

Cathode material↗
DOE OSTI2025

From Structure to Function: Zn/Mn-Modified Maghemite as an Advanced Nanoplatform for Magnetic Hyperthermia and Radionuclide Therapy

The development of nanoplatforms capable of efficient heat generation and stable radionuclide delivery is essential for effective bimodal cancer therapy. Here, in this study, binary (Fe–M) and ternary (Fe–M–M′) metal oxide nanoparticles were synthesized via a polyol method optimized to produce flower-like γ-Fe 2 O 3 (maghemite) structures, with M and M′ representing Zn and/or Mn. Comprehensive structural and magnetic characterization was conducted to explain the relationship between composition, defect structure, and hyperthermic performance. The analyses revealed that cation substitution induced an Fe-site vacancy, primarily at octahedral positions, leading to local structural distortions, as confirmed by powder X-ray diffraction and pair distribution function analysis. The optimized composition, with Zn/Mn/Fe = 0.040:0.182:1, exhibited the highest concentration of vacancies and structural disorder. These vacancies altered the bonding environment, enhancing magnetic interactions at tetrahedral sites while weakening those at the octahedral positions. The resulting multicore nanoflowers (20–63 nm; core size 13–18 nm) displayed strong heating performance, with intrinsic loss power ranging from 0.34 to 5.77 nHm 2 kg –1 . The optimized sample achieved a temperature increase of 30 °C within 2 min and a specific absorption rate of 369 W g –1 . This composition was further coated with citrate (CA) and successfully radiolabeled with 177 Lu, achieving a radiolabeling yield of 92.7% and excellent stability, thus forming a robust nanoplatform for combined magnetic hyperthermia and radionuclide therapy. Biological evaluation of the optimized S5 composition revealed selective cytotoxicity toward HeLa and LS174 cells, while toxicity was significantly lower to A549, A375, and normal MRC-5 cells. Citrate coating of S5 nanoparticles (S5@CA) drastically reduced their cytotoxicity across all tested cell lines (IC 50 > 200 μg mL –1 ), confirming their enhanced biocompatibility for therapeutic applications. In HeLa cells subjected to magnetic hyperthermia, the viability decreased to approximately 84% after 30 min and 61% after 60 min of treatment, demonstrating the sustained hyperthermic effect at a controlled working temperature of 48 °C. These results underscore the effectiveness of cation substitution and vacancy engineering in tailoring the functional properties of maghemite-based nanomaterials for advanced multimodal cancer therapies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Characterization of an aerosolized nanoparticle beam beyond the diffraction limit through strong field ionization

Abstract The study of nanomaterials is an active area of research for technological applications as well as fundamental science. A common method for studying properties of isolated nanoparticles is by an in-vacuum particle beam produced via an aerodynamic lens. Despite being common practice, characterization of such beams has proven difficult as light scattering detection techniques fail for particles with sizes beyond the diffraction limit. Here we present a new technique for characterizing such nanoparticle beams using strong field ionization. By focusing an ultrafast, mJ-level laser into the particle beam, a nanoparticle within the laser focus is ionized and easily detected by its ejected electrons. This method grants direct access to the nanoparticle density at the location of the focus, and by scanning the focus through the transverse and longitudinal profiles of the particle beam we attain the 3-dimensional particle density distribution for a cylindrically symmetric beam. Further, we show that strong field ionization is effective in detecting spherical nanoparticles as small as 10 nm in diameter. Additionally, this technique is an effective tool in optimizing the particle beam for specific applications. As an example we show that the particle beam density and width can be manipulated by restricting the gas flow into the aerodynamic lens.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS1994

Assessment of impact damage of composite rocket motor cases

This contract reviewed the available literature on mechanisms of low velocity impact damage in filament wound rocket motor cases, MDE methods to quantify damage, critical coupon level test methods, manufacturing and material process variables and empirical and analytical modeling off impact damage. The critical design properties for rocket motor cases are biaxial hoop and axial tensile strength. Low velocity impact damage is insidious because it can create serious nonvisible damage at very low impact velocities. In thick rocket motor cases the prevalent low velocity impact damage is fiber fracture and matrix cracking adjacent to the front face. In contrast, low velocity loading of thin wall cylinders induces flexure, depending on span length and the flexure induces delamination and tensile cracking on the back face wall opposed to impact occurs due to flexural stresses imposed by impact loading. Important NDE methods for rocket motor cases are non-contacting methods that allow inspection from one side. Among these are vibrothermography, and pulse-echo methods based on acoustic-ultrasonic methods. High resolution techniques such as x-ray computed tomography appear to have merit for accurate geometrical characterization of local damage to support development of analytical models of micromechanics. The challenge of coupon level testing is to reproduce the biaxial stress state that the full scale article experiences, and to determine how to scale the composite structure to model full sized behavior. Biaxial tensile testing has been performed by uniaxially tensile loading internally pressurized cylinders. This is experimentally difficult due to gripping problems and pressure containment. Much prior work focused on uniaxial tensile testing of model filament wound cylinders. Interpretation of the results of some studies is complicated by the fact that the fabrication process did not duplicate full scale manufacturing. It is difficult to scale results from testing subscale cylinders since there are significant differences in out time of the resins relative to full scale cylinder fabrication, differences in hoop fiber tensioning and unsatisfactory coupon configurations. It appears that development of a new test method for subscale cylinders is merited. Damage tolerance may be improved by material optimization that uses fiber treatments and matrix modifications to control the fiber matrix interface bonding. It is difficult to develop process optimization in subscale cylinders without also modeling the longer out times resins experience in full scale testing. A major breakthrough in characterizing the effect of impact damage on residual strength, and understanding how to scale results of subscale evaluations, will be a sound micromechanical model that described progressive failure of the composite. Such models will utilize a three dimensional stress analysis due to the complex nature of low velocity impact stresses in thick composites. When these models are coupled with non-contact NDE methods that geometrically characterize the damage and acoustic methods that characterize the effective local elastic properties, accurate assessment of residual strength from impact damage may be possible. Directions for further development are suggested.

Paris, Henry G.↗
NASA NTRS2016

Sylph: Life Detection Probe for a Europa Plume

Jupiter’s Galilean satellite Europa likely contains a vast, global, subsurface salt-water ocean. A singular telescope observation in 2012 indicated possible plume activity, in some ways similar to the continuous plume activity seen at Enceladus. If a Europan plume does expel ocean water into space, its vapor, ice grains, and dust grains could be analyzed by a spacecraft equipped with high-resolution mass spectrometers. Such plume measurements would be sufficient to measure the contextual habitability profile of the ocean, and to detect multiple signatures of life, should it exist in the ocean. Instrumented with MASPEX (Mass Spectrometer for Planetary Exploration) and SUDA (Surface Dust Analyzer), NASA’s planned Europa Mission now in development could theoretically conduct this investigation. However, the mission’s closest flyby altitude would be 25 km; due to Europa’s large gravity, frozen ocean spray containing non-volatile molecular species critical to biosignature detection can be sampled only within 5 km of the surface. The Europa Mission would not fly this low. Sylph is a concept for a complementary, free-flyer smallsat probe, about the same size as an office water-cooler bottle, that could be deployed by the Europa Mission as it conducts a plume fly-through. Sylph would execute a single ~2 km altitude plume pass below the main spacecraft. With a mass of 40 kg and a 24-hr life, the probe would autonomously navigate its low-altitude pass, and carry a dualchannel Mini-SUDA impact ionization mass spectrometer that would measure the composition of large grains, complementing the main spacecraft’s measurements of small grains and gas. Sylph is a novel smallsat concept: purpose-built configuration optimized for both the harsh Jovian environment and for Europan planetary-protection requirements, and built with advanced manufacturing methods from both deep space and smallsat components. For $80M (~4% marginal cost), the Europa Mission could buy a Sylph “insurance policy,” hedging the possibility that it does discover ocean plumes upon arrival in 2028. At less than 85 kg for probe and accommodations, the mission could carry two or three of the probes.

Sherwood, Brent↗
DOE OSTI2024

Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units

Atomic structure prediction and associated property calculations are the bedrock of chemical physics. Since high-fidelity ab initio modeling techniques for computing the structure and properties can be prohibitively expensive, this motivates the development of machine-learning (ML) models that make these predictions more efficiently. Training graph neural networks over large atomistic databases introduces unique computational challenges such as the need to process millions of small graphs with variable size and support communication patterns that are distinct from learning over large graphs such as social networks. We demonstrate a novel hardware-software co-design approach to scale up the training of atomistic graph neural networks (GNN) for structure and property prediction. First, to eliminate redundant computation and memory associated with alternative padding techniques and to improve throughput via minimizing communication, we formulate the effective coalescing of the batches of variable-size atomistic graphs as the bin packing problem and introduce a hardware-agnostic algorithm to pack these batches. In addition, we propose hardware-specific optimizations including a planner and vectorization for the gather-scatter operations targeted for Graphcore’s Intelligence Processing Unit (IPU), as well as model-specific optimizations such as merged communication collectives and optimized softplus. Putting these all together, we demonstrate the effectiveness of the proposed co-design approach by providing an implementation of a well-established atomistic GNN on the Graphcore IPUs. We evaluate the training performance on multiple atomistic graph databases with varying degrees of graph counts, sizes and sparsity. Here, we demonstrate that such a co-design approach can reduce the training time of atomistic GNNs and can improve the performance by up to 1.5× compared to the baseline implementation of the model on the IPUs. Additionally, we compare our IPU implementation with a Nvidia GPU-based implementation and show that our atomistic GNN implementation on the IPUs can run 1.8× faster on average compared to the execution time on the GPUs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Patterning approach for detecting defect in device manufacturing

Compact handheld devices which were a dream in the past are now a reality; this has been enabled by miniaturization of circuit architectures including power devices. Scaling down of the design feature sizes does come with a price with an increase in systematic defects during chip manufacturing. There are generally two methods of inline defect detection adopted to monitor the semiconductor device fabrication—optical inspection and electron beam inspection. The optical inspection uses ultra-violet and deep ultra-violet (UV/DUV) light to find patterning defects on the wafer. While the electron-beam inspection uses electron charge and discharge measurement to find electrical connection defects, both are a costly procedure in terms of resources and time. The physical limit of feature resolution of the optical source is now making the defect inspection job difficult in miniaturized application specific integrated circuit (ASIC). This study is designed to test the patterning optimization approach on both inspection platforms. Using hotspot analysis weak locations are identified in the full chip design, and then they are verified in the inline wafer inspection. The criterion for hot-spot determination is also discussed in this paper.

36 MATERIALS SCIENCE↗
NASA NTRS2007

NASA Solar Sail Propulsion Technology Development

NASA's In-Space Propulsion Technology Program has developed the first generation of solar sail propulsion systems sufficient to accomplish inner solar system science and exploration missions. These first generation solar sails, when operational, will range in size from 40 meters to well over 100 meters in diameter and have an areal density of less than 13 grams per square meter. A rigorous, multi-year technology development effort culminated in 2005 with the testing of two different 20-m solar sail systems under thermal vacuum conditions. The first system, developed by ATK Space Systems of Goleta, California, uses rigid booms to deploy and stabilize the sail. In the second approach, L'Garde, Inc. of Tustin, California uses inflatable booms that rigidize in the coldness of space to accomplish sail deployment. This effort provided a number of significant insights into the optimal design and expected performance of solar sails as well as an understanding of the methods and costs of building and using them. In a separate effort, solar sail orbital analysis tools for mission design were developed and tested. Laboratory simulations of the effects of long-term space radiation exposure were also conducted on two candidate solar sail materials. Detailed radiation and charging environments were defined for mission trajectories outside the protection of the earth's magnetosphere, in the solar wind environment. These were used in other analytical tools to prove the adequacy of sail design features for accommodating the harsh space environment. Preceding and in conjunction with these technology efforts, NASA sponsored several mission application studies for solar sails. Potential missions include those that would be flown in the near term to study the sun and be used in space weather prediction to one that would use an evolved sail capability to support humanity's first mission into nearby interstellar space. This paper will describe the status of solar sail propulsion within NASA, nearterm solar sail mission applications, and near-term plans for further development.

Johnson, Les↗
NASA NTRS2002

Multidimensional indexing structure for use with linear optimization queries

Linear optimization queries, which usually arise in various decision support and resource planning applications, are queries that retrieve top N data records (where N is an integer greater than zero) which satisfy a specific optimization criterion. The optimization criterion is to either maximize or minimize a linear equation. The coefficients of the linear equation are given at query time. Methods and apparatus are disclosed for constructing, maintaining and utilizing a multidimensional indexing structure of database records to improve the execution speed of linear optimization queries. Database records with numerical attributes are organized into a number of layers and each layer represents a geometric structure called convex hull. Such linear optimization queries are processed by searching from the outer-most layer of this multi-layer indexing structure inwards. At least one record per layer will satisfy the query criterion and the number of layers needed to be searched depends on the spatial distribution of records, the query-issued linear coefficients, and N, the number of records to be returned. When N is small compared to the total size of the database, answering the query typically requires searching only a small fraction of all relevant records, resulting in a tremendous speedup as compared to linearly scanning the entire dataset.

Bergman, Lawrence David↗
NASA NTRS2013

NASA Advanced Concepts Office, Earth-To-Orbit Team Design Process and Tools

The Earth to Orbit (ETO) Team of the Advanced Concepts Office (ACO) at NASA Marshal Space Flight Center (MSFC) is considered the preeminent group to go to for prephase A and phase A concept definition. The ACO team has been at the forefront of a multitude of launch vehicle studies determining the future direction of the Agency as a whole due, in part, to their rapid turnaround time in analyzing concepts and their ability to cover broad trade spaces of vehicles in that limited timeframe. Each completed vehicle concept includes a full mass breakdown of each vehicle to tertiary subsystem components, along with a vehicle trajectory analysis to determine optimized payload delivery to specified orbital parameters, flight environments, and delta v capability. Additionally, a structural analysis of the vehicle based on material properties and geometries is performed as well as an analysis to determine the flight loads based on the trajectory outputs. As mentioned, the ACO Earth to Orbit Team prides themselves on their rapid turnaround time and often need to fulfill customer requests within limited schedule or little advanced notice. Due to working in this fast paced environment, the ETO team has developed some finely honed skills and methods to maximize the delivery capability to meet their customer needs. This paper will describe the interfaces between the 3 primary disciplines used in the design process; weights and sizing, trajectory, and structural analysis, as well as the approach each discipline employs to streamline their particular piece of the design process.

Waters, Eric D.↗
NASA NTRS2019

The Updated GEO Population for ORDEM 3.1

The limited availability of data for satellite fragmentations and debris in the geosynchronous orbit (GEO) region creates challenges to building accurate models for the orbital debris environment at such altitudes. Updated methods to properly incorporate and extrapolate measurement data have become a cornerstone of the GEO component in the newest version of the NASA Orbital Debris Engineering Model (ORDEM), ORDEM 3.1. For the GEO region, the Space Surveillance Network (SSN) catalog provides coverage down to a limit of approximately 1 m. A more statistically complete representation of the GEO population for smaller objects, which can pose a high risk to operational spacecraft, is thus dependent on dedicated observations by instruments optimized to observe debris smaller than the SSN cataloging threshold. For ORDEM 3.1, optical data from the Michigan Orbital DEbris Survey Telescope (MODEST) provided the input for building the GEO population down to approximately 30 cm (converting absolute magnitude to size). For smaller sizes, the size distribution of debris in the MODEST dataset was extrapolated down to 10 cm, and orbital parameters were estimated based on the orbits of the larger objects. When compared to previous versions of the model, significant improvements were made to the process of building the GEO population in ORDEM 3.1, both in the assessment of fragmentation debris in the data and assignment of orbital elements within the model. A so-called “debris ring filter,” based on a range of angles between an orbit’s angular momentum vector and that of the stable Laplace plane, was applied to the data to reduce biases from non- GEO objects, such as objects in a GEO-transfer orbit. In addition, a new approach was implemented to assign noncircular mean motions and eccentricities to the fragmentation debris observed by MODEST because the short observation window (5 min) in GEO limits orbit resolution to a circular orbit assumption for assigning orbital parameters. For ORDEM 3.1, non-circular orbital elements were assigned using relationships that were identified between mean motion and the angle between the orbit plane and the stable Laplace plane, as well as between mean motion and eccentricity, based on breakup clouds modeled by the NASA Standard Breakup Model. This approach has yielded a high-fidelity GEO model that has been validated with data from more recent MODEST observation campaigns.

Manis, A.↗
DOE OSTI2024

Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms

This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative models generate synthetic data resembling real data, enabling deep learning model training without extensive labeled datasets. They prove valuable in creating virtual libraries of molecules for material science and facilitating drug discovery by generating molecules with specific properties. While generative adversarial networks (GANs) are explored for these purposes, mode collapse restricts their efficacy, limiting novel structure variability. To address this, we introduce a masked language model (LM) inspired by natural language processing. Although LMs alone can have inherent limitations, we propose a hybrid architecture combining LMs and GANs to efficiently generate new molecules, demonstrating superior performance over standalone masked LMs, particularly for smaller population sizes. This hybrid LM-GAN architecture enhances efficiency in optimizing properties and generating novel samples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Automated Computational Thermochemistry for Butane Oxidation: A Prelude to Predictive Automated Combustion Kinetics

arge-scale implementation of high level computational theoretical chemical kinetics offers the prospect for dramatically improving the fidelity of combustion chemical modeling. As a first step toward this goal, we developed a procedure for automatically generating the thermochemical data for combustion of an arbitrary fuel. The procedure begins by producing a list of combustion relevant species from a specification of the fuel and combustion conditions of interest. Then, for each element in the list of species, the procedure determines an internal coordinate z-matrix description of its structure, the optimal torsional configuration via Monte Carlo sampling, key rovibrational properties for that optimal geometry (including anharmonic corrections from torsional mappings and/or vibrational perturbation theory), and high level estimates of the electronic and zero-point energies via arbitrarily defined composite methods. This dataset is then converted first to partition functions, then to thermodynamic properties, and finally to NASA polynomial representations of the data. The end product is an automatically generated database of electronic structure results and thermochemical data including representations in a format appropriate for combustion simulations. The utility and functioning of this predictive automated computational thermochemistry (PACT) software package is illustrated through application to the automated generation of thermochemical data for the combustion of n-butane. Butane is chosen for this demonstration as its species list is of reasonably manageable size for debugging level computations, while still presenting most of the key challenges that need to be surmounted in the consideration of larger fuels. Furthermore, its low temperature chemistry is representative of that oc-curring with larger alkanes.

Keceli, Murat↗
DOE OSTI2023

Laser Scribing for Electrode Patterning of Perovskite Spectrometer-Grade CsPbBr 3 Gamma-ray Detectors

Making semiconductor radiation detectors that work at room temperature relies heavily on the deposition and pixelation of electrodes. Electrode patterning of perovskite solar cells widely implements laser scribing techniques, which is a convenient, scalable, and inexpensive technique. However, this method has not found its application in radiation detector patterning yet, and the question whether laser scribing can achieve high-quality patterns with minimum damage to a detector crystal and low interpixel cross-talk remains largely unanswered. To prove that laser scribing is a practical method for electrode patterning on perovskite CsPbBr 3 detectors, we use the material to create a variety of patterns. A very low lateral leakage current (60 nA at 10 V) and high mobility-lifetime product (9.7(3) x 10 -4 cm 2 /V) were observed between the pixel and the guard ring in tests of single-pixel devices with a separation of 200 or 100 μm between the central electrode and the guard ring. The 122 and 136 keV photopeaks in 57 Co gamma-ray spectra were very well resolved with an energy resolution of up to 6.1% at 122 keV. Finally, a further reduction in gap size to 50 mu m is conceivable, but more process optimization is needed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Quantum adiabatic machine learning by zooming into a region of the energy surface

Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification. Here, we propose QAML-Z, an algorithm that iteratively zooms in on a region of the energy surface by mapping the problem to a continuous space and sequentially applying quantum annealing to an augmented set of weak classifiers. Results on a programmable quantum annealer show that QAML-Z matches classical deep neural network performance at small training set sizes and reduces the performance margin between QAML and classical deep neural networks by almost 50% at large training set sizes, as measured by area under the receiver operating characteristic curve. The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗