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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quantum graph learning and algorithms applied in quantum computer sciences and image classification

Graph and network theory play a fundamental role in quantum computer sciences, including quantum information and computation. Random graphs and complex network theory are pivotal in predicting novel quantum phenomena, where entangled links are represented by edges. Quantum algorithms have been developed to enhance solutions for various network problems, giving rise to quantum graph computing and quantum graph learning (QGL). Here, in this review, we explore graph theory and graph learning methods as powerful tools for quantum computers to generate efficient solutions to problems beyond the reach of classical systems. We delve into the development of quantum complex network theory and its applications in quantum computation, materials discovery, and research. We also discuss quantum machine learning (QML) methodologies for effective image classification using qubits, quantum gates, and quantum circuits. Additionally, the paper addresses the challenges of QGL and algorithms, emphasizing the steps needed to develop flexible QGL solvers. This review presents a comprehensive overview of the fields of QGL and QML, highlights recent advancements, and identifies opportunities for future research.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1982

A means for utilizing ancillary information in multispectral classification

Attention is given to a method which makes it possible to incorporate information from ancillary data sources into the results of an existing classification of remotely sensed data. The method is based upon probabilistic label relaxation procedures which are used for imbedding spatial context data in image-labeling problems. The source of ancillary information is utilized in the form of a set of probabilities. These are introduced into a modified relaxation method called supervised relaxation labeling which, on application, develops a labeling for remotely sensed data that strikes a balance in consistency between spectral, spatial, and ancillary data sources of information. A description is given of results of a forestry classification in which accuracy is improved from 68% to 81% by incorporating topographic elevation in the outlined manner.

Richards, J. A.↗
NASA NTRS1982

Modeling misregistration and related effects on multispectral classification

Any noise in measurements (due to the scene, sensor, or the analog to digital process) causes a finite fraction of measurements to fall outside of the classification limits. For field boundaries, where the misregistration effects are felt, the misregistration causes the border in a given (set of) band(s) to be closer than expected to a given pixel, so that the mixed materials in the pixels cause additional pixels to fall outside of the class limits. Considerations of the transient distance involved in the difference in brightness between adjacent fields, when scaled to "per pixel", allow the estimation of the width of the border zones. The entire problem is then scaled to field sizes to allow estimation of the global effects. This approach allows the estimation of the accuracy of multispectral classification which might be expected for field interiors, the useful number of quantization bits, and one set of criteria for an unbiased classifier.

Billingsley, F. C.↗
NASA NTRS2000

Fusion of AIRSAR and TM Data for Parameter Classification and Estimation in Dense and Hilly Forests

The expanded remotely sensed data space consisting of coincident radar backscatter and optical reflectance data provides for a more complete description of the Earth surface. This is especially useful where many parameters are needed to describe a certain scene, such as in the presence of dense and complex-structured vegetation or where there is considerable underlying topography. The goal of this paper is to use a combination of radar and optical data to develop a methodology for parameter classification for dense and hilly forests, and further, class-specific parameter estimation. The area to be used in this study is the H. J. Andrews Forest in Oregon, one of the Long-Term Ecological Research (LTER) sites in the US. This area consists of various dense old-growth conifer stands, and contains significant topographic relief. The Andrews forest has been the subject of many ecological studies over several decades, resulting in an abundance of ground measurements. Recently, biomass and leaf-area index (LAI) values for approximately 30 reference stands have also become available which span a large range of those parameters. The remote sensing data types to be used are the C-, L-, and P-band polarimetric radar data from the JPL airborne SAR (AIRSAR), the C-band single-polarization data from the JPL topographic SAR (TOPSAR), and the Thematic Mapper (TM) data from Landsat, all acquired in late April 1998. The total number of useful independent data channels from the AIRSAR is 15 (three frequencies, each with three unique polarizations and amplitude and phase of the like-polarized correlation), from the TOPSAR is 2 (amplitude and phase of the interferometric correlation), and from the TM is 6 (the thermal band is not used). The range pixel spacing of the AIRSAR is 3.3m for C- and L-bands and 6.6m for P-band. The TOPSAR pixel spacing is 10m, and the TM pixel size is 30m. To achieve parameter classification, first a number of parameters are defined which are of interest to ecologists for forest process modeling. These parameters include total biomass, leaf biomass, LAI, and tree height. The remote sensing data from radar and TM are used to formulate a multivariate analysis problem given the ground measurements of the parameters. Each class of each parameter is defined by a probability density function (pdf), the spread of which defines the range of that class. High classification accuracy results from situations in which little overlap occurs between pdfs. Classification results provide the basis for the future work of class-specific parameter estimation using radar and optical data. This work was performed in part by the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, and in part by the NASA Ames Research Center, Moffett Field, CA, both under contract from the National Aeronautics and Space Administration.

Moghaddam, Mahta↗
DOE OSTI2025

SympGNNs: Symplectic Graph Neural Networks for identifying high-dimensional Hamiltonian systems and node classification

Existing neural network models to learn Hamiltonian systems, such as SympNets, although accurate in low-dimensions, struggle to learn the correct dynamics for high-dimensional many-body systems. Herein, we introduce Symplectic Graph Neural Networks (SympGNNs) that can effectively handle system identification in high-dimensional Hamiltonian systems, as well as node classification. SympGNNs combine symplectic maps with permutation equivariance, a property of graph neural networks. Specifically, we propose two variants of SympGNNs: (i) G-SympGNN and (ii) LA-SympGNN, arising from different parameterizations of the kinetic and potential energy. We demonstrate the capabilities of SympGNN on two physical examples: a 40-particle coupled Harmonic oscillator, and a 2000-particle molecular dynamics simulation in a two-dimensional Lennard-Jones potential. Furthermore, we demonstrate the performance of SympGNN in the node classification task, achieving accuracy comparable to the state-of-the-art. Finally, we also empirically show that SympGNN can overcome the oversmoothing and heterophily problems, two key challenges in the field of graph neural networks.

Deep learning↗
NASA NTRS1998

Fuzzy Classification of Ocean Color Satellite Data for Bio-optical Algorithm Constituent Retrievals

The ocean has been traditionally viewed as a 2 class system. Morel and Prieur (1977) classified ocean water according to the dominant absorbent particle suspended in the water column. Case 1 is described as having a high concentration of phytoplankton (and detritus) relative to other particles. Conversely, case 2 is described as having inorganic particles such as suspended sediments in high concentrations. Little work has gone into the problem of mixing bio-optical models for these different water types. An approach is put forth here to blend bio-optical algorithms based on a fuzzy classification scheme. This scheme involves two procedures. First, a clustering procedure identifies classes and builds class statistics from in-situ optical measurements. Next, a classification procedure assigns satellite pixels partial memberships to these classes based on their ocean color reflectance signature. These membership assignments can be used as the basis for a weighting retrievals from class-specific bio-optical algorithms. This technique is demonstrated with in-situ optical measurements and an image from the SeaWiFS ocean color satellite.

Campbell, Janet W.↗
DOE OSTI2022

A Classification of G -invariant Shallow Neural Networks

When trying to fit a deep neural network (DNN) to a G-invariant target function with G a group, it only makes sense to constrain the DNN to be G-invariant as well. However, there can be many different ways to do this, thus raising the problem of “G-invariant neural architecture design”: What is the optimal Ginvariant architecture for a given problem? Before we can consider the optimization problem itself, we must understand the search space, the architectures in it, and how they relate to one another. In this paper, we take a first step towards this goal; we prove a theorem that gives a classification of all G-invariant single-hidden-layer or “shallow” neural network (G-SNN) architectures with ReLU activation for any finite orthogonal group G, and we prove a second theorem that characterizes the inclusion maps or “network morphisms” between the architectures that can be leveraged during neural architecture search (NAS). The proof is based on a correspondence of every G-SNN to a signed permutation representation of G acting on the hidden neurons; the classification is equivalently given in terms of the first cohomology classes of G, thus admitting a topological interpretation. The G-SNN architectures corresponding to nontrivial cohomology classes have, to our knowledge, never been explicitly identified in the literature previously. Using a code implementation, we enumerate the G-SNN architectures for some example groups G and visualize their structure. Lastly, we prove that architectures corresponding to inequivalent cohomology classes coincide in function space only when their weight matrices are zero, and we discuss the implications of this for NAS.

Agrawal, Devanshu↗
NASA NTRS1975

The problem of regime summaries of the data from radar observations

Peculiarities of the radar information about clouds are examined in comparison with visual data. An objective radar classification is presented and the relation of it to the meteorological classification is shown. The advisability of storage and summarization of the primary radar data for regime purposes is substantiated.

Divinskaya, B. S.↗
NASA NTRS1983

International Geoscience and Remote Sensing Symposium, Universitaet Muenchen, Munich, West Germany, June 1-4, 1982, Proceedings

The present conference on geoscience and remote sensing considers the multispectral remote sensing of saline seeps, the augmentation of LANDSAT MSS data with topographic data, thematic mapping, the sampling problem in radiation budget studies, aerial conductivity measurements over geothermal areas, a comparison of multifrequency band radars for crop classification, the improved estimation of vegetation-covered soil by combined active/passive microwave remote sensing, and atmospheric water vapor profiling by ground-based radiometry. Also discussed are SAR imaging from an inclined geosynchronous orbit, the classification of agricultural crops in radar images, the Ocean Color Experiment on the second orbital flight test of the Space Shuttle, the dielectric properties of wet materials, remote sensing systems for the mm-wave region, and the simulation of spaceborne stereo radar imagery.

Seiber, A. J.↗
NASA NTRS1989

Identification of asteroid dynamical families

Problems involved in the identification of asteroid dynamical families are discussed, and some methodological guidelines are presented. Asteroid family classifications are reviewed, and differences in the existing classifications are examined with special attention given to the effects of observational selection on the classification of family membership. The paper also discusses various theories of secular perturbations, including the classical linear theory, the theory of Williams (1969), and the higher order/degree theory of Yuasa (1973).

Valsecchi, G. B.↗
NASA NTRS1990

Dumb-bell galaxies in southern clusters: Catalog and preliminary statistical results

The dominant galaxy of a rich cluster is often an object whose formation and evolution is closely connected to the dynamics of the cluster itself. Hoessel (1980) and Schneider et al. (1983) estimate that 50 percent of the dominant galaxies are either of the dumb-bell type or have companions at projected distances less than 20 kpc, which is far in excess of the number expected from chance projection (see also Rood and Leir 1979). Presently there is no complete sample of these objects, with the exception of the listing of dumb-bell galaxies in BM type I and I-II clusters in the Abell statistical sample of Rood and Leir (1979). Recent dynamical studies of dumb-bell galaxies in clusters (Valentijn and Casertano, 1988) still suffer from inhomogeneity of the sample. The fact that it is a mixture of optically and radio selected objects may have introduced an unknown biases, for instance if the probability of radio emission is enhanced by the presence of close companions (Stocke, 1978, Heckman et al. 1985, Vettolani and Gregorini 1988) a bias could be present in their velocity distribution. However, this situation is bound to improve: a new sample of Abell clusters in the Southern Hemisphere has been constructed (Abell et al., 1988 hereafter ACO), which has several advantages over the original northern catalog. The plate material (IIIaJ plates) is of better quality and reaches fainter magnitudes. This makes it possible to classify the cluster types with a higher degree of accuracy, as well as to fainter magnitudes. The authors therefore decided to reconsider the whole problem constructing a new sample of dumb-bell galaxies homogeneously selected from the ACO survey. Details of the classification criteria are given.

Vettolani, G.↗
NASA NTRS1992

Neural network-based retrieval from software reuse repositories

A significant hurdle confronts the software reuser attempting to select candidate components from a software repository - discriminating between those components without resorting to inspection of the implementation(s). We outline an approach to this problem based upon neural networks which avoids requiring the repository administrators to define a conceptual closeness graph for the classification vocabulary.

Eichmann, David A.↗
DOE OSTI2022

Solving Large‐Scale Linear Systems of Equations by a Quantum Hybrid Algorithm

Abstract Today's intermediate‐scale quantum computers, although imperfect, already perform computational tasks that are manifestly beyond the capabilities of modern classical supercomputers. However, so far, quantum‐enabled large‐scale solutions have been realized only for limited set of problems. Here a hybrid algorithm based on phase estimation and classical optimization of the circuit width and depth is employed for solving a specific class of large linear systems of equations ubiquitous to many areas of science and engineering. A classification of linear systems based on the entanglement properties of the associated phase‐estimation unitary operation is introduced, enabling a highly efficient search for solutions that is facilitated by a straightforward matrix‐to‐circuit map. A 2 17 ‐dimensional problem is implemented on several IBM quantum computer superconducting quantum processors, a record‐breaking result for a linear system solved by a quantum computer. Demonstrated realisation sets a clear benchmark in the quest for the future quantum speedup in the linear systems of equations solution.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

SQMS Quantum R&D in Machine Learning, Optimization and Sensing beyond Fundamental Physics Applications

This newly formed team at SQMS under the Ecosystem Thrust is looking to develop capabilities impacting societal advances outside the core domain of HEP and condensed matter physics. We explicitly leverage the experimental and algorithmic innovations developed across all groups as well as connect to broad-scope external projects of the diverse team of PIs. As the inaugural set of projects, we are studying numerically quantum machine learning models inspired by efficiently trainable echo-state and orthogonal neural networks and developing designs for related experiments to be performed on quantum processors based on SQMS SRF cQED technology and Rigetti s transmon arrays. Investigated models exploit ideas and lessons learned from multiple prior work by SQMS team members in a variety of internal and external activities [R1]. Target initial applications include noisy signal processing, potentially captured by quantum sensors or noisy QPUs, as well as simulation and classification of healthcare data. For instance, image reconstruction of the brain s electrical properties by solving the inverse Maxwell equation problem with uncertainty [R2] through a hybrid quantum-classical physics-informed architecture for time-dependent processes [R3]. The group is also investigating the application and development of novel quantum sensors based on magnetic levitation of a superconducting sphere coupled to a superconducting qubit. This coupling enables high-precision measurements of the position of the sphere, which can be used for sensitive detection of forces, enabling practical applications such as gravimetry for geophysics analysis, or accelerometry for GPS-denied navigation [R4] [R1] Rieffel, Eleanor G., Ata Akbari Asanjan, M. Sohaib Alam, Namit Anand, David E. Bernal Neira, Sophie Block, Lucas T. Brady et al. "Assessing and advancing the potential of quantum computing: A NASA case study." Future Generation Computer Systems (2024). [R2] Yu, X., Serrall s, J.E., Giannakopoulos, I.I., Liu, Z., Daniel, L., Lattanzi, R. and Zhang, Z., 2023. Pifon-ept: Mr-based electrical property tomography using physics-informed fourier networks. IEEE Journal on Multiscale and Multiphysics Computational Techniques. [R3] Wudarski, Filip, Daniel OConnor, Shaun Geaney, Ata Akbari Asanjan, Max Wilson, Elena Strbac, P. Aaron Lott, and Davide Venturelli. "Hybrid quantum-classical reservoir computing for simulating chaotic systems." arXiv preprint arXiv:2311.14105 (2023). [R4] Higgins, Gerard, Saarik Kalia, and Zhen Liu. "Maglev for dark matter: Dark-photon and axion dark matter sensing with levitated superconductors." Physical Review D 109.5 (2024): 055024.

Venturelli, Davide↗
NASA NTRS1974

Remote sensing in Iowa agriculture

The author has identified the following significant results. Analysis of 1972 single-date coverage indicated that a complete crop classification was not attainable at the test sites. Good multi-date coverage during 1973 indicates that many of the problems encountered in 1972 will be minimized. In addition, the compilation of springtime imagery covering the entire state of Iowa has added a new dimension to interpretation of Iowa's natural resources. ERTS-1 has provided data necessary to achieve the broad synoptic view not attainable through other means. This should provide soils and crop researchers and land use planners a base map of Iowa. Granted and due to the resolution of ERTS-1, not all details are observable for many land use planning needs, but this gives a general and current view of Iowa.

Mahlstede, J. P.↗
NASA NTRS1975

Water resources, summary

The application of remote sensing products to the development and understanding of water resources problems is considered. Geology and hydrogeology, analysis of watersheds, snow and ice, prediction of runoff from snowmelt, hydrologic land use classifications, soil moisture, evapotranspiration, flood hazards, and water quality surveys are among the topics discussed. Suggestions for further use of remotely sensed data are given along with increased user requirements.

Simons, D. B.↗
NASA NTRS1976

Applications of feature selection

The use of satellite-acquired (LANDSAT) multispectral scanner (MSS) data to conduct an inventory of some crop of economic interest such as wheat over a large geographical area is considered in relation to the development of accurate and efficient algorithms for data classification. The dimension of the measurement space and the computational load for a classification algorithm is increased by the use of multitemporal measurements. Feature selection/combination techniques used to reduce the dimensionality of the problem are described.

Guseman, L. F., Jr.↗