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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Variational quantum and neural quantum states algorithms for the linear complementarity problem

Variational quantum algorithms (VQAs) are promising hybrid quantum-classical methods designed to leverage the computational advantages of quantum computing while mitigating the limitations of current noisy intermediate-scale quantum (NISQ) hardware. Although VQAs have been demonstrated as proofs of concept, their practical utility in solving real-world problems—and whether quantum-inspired classical algorithms can match their performance—remains an open question. We present a novel application of the variational quantum linear solver (VQLS) and its classical neural quantum states-based counterpart, the variational neural linear solver (VNLS), as key components within a minimum map Newton solver for a complementarity-based rigid-body contact model. We demonstrate using the VNLS that our solver accurately simulates the dynamics of rigid spherical bodies during collision events. These results suggest that quantum and quantum-inspired linear algebra algorithms can serve as viable alternatives to standard linear algebra solvers for modelling certain physical systems.

neural quantum states↗
DOE OSTI2022

Topological alloy engineering and locally linearized gap dependence on concentration

Alloy engineering is a well-established approach to tune various materials’ properties, but its application to topological alloys remains rudimentary. Of special interest is the band gap, the most defining property of topological materials; however, the concentration dependence of energy gaps in topological alloys remains unknown. Here we systematically investigate the band gap evolution of a topological alloy as a function of alloy concentration, using KZnSb 1-x Bi x as a prototype, based on first-principles calculations. In contrast to the well-established smooth bowing curve for a trivial gap in semiconductor alloys, we found that the topological gap evolves generally with a complex fragmented pattern due to topological phase transitions, and most strikingly a linear dependence on concentration locally in each distinct phase. Such gap linearization is fundamentally rooted in the linear dependence on alloy concentration of spin-orbit coupling (SOC) that predominantly determines a topological gap. Furthermore, we demonstrate topological alloy engineering as a general approach to tune the topological order by modulating the band edge composition and degeneracy through the alloying-induced interplay of SOC and atomic orbital on-site energy, while the linear gap dependence on alloy concentration remains independent of the degree of topological order.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Controlled skyrmion ratchet in linear protrusion defects

Using atomistic simulations, we investigate the dynamical behavior of a single skyrmion interacting with an asymmetric linear protrusion array under external ac driving. The linear protrusion array is placed in the xy plane and is composed of magnetic walls near the edge of the nanotrack that have angled linear protrusions into the nanotrack. The structure forms a hard axis for –x direction motion and an easy axis for +x direction motion. When the ac drive is applied along the x direction, the skyrmion moves along the hard direction of the substrate asymmetry in three phases: a pinned phase with localized skyrmion orbits, a constant velocity phase where the orbits become delocalized, and a reentrant pinned phase with larger localized orbits. We measure the dependence of the skyrmion velocity on the frequency and amplitude of the ac drive. All three phases appear for all frequency values simulated here, and in the constant velocity phase the skyrmion velocity depends only on the frequency and not on the amplitude of the ac drive. When ac driving is applied in the y direction, the skyrmion moves along the easy direction of the substrate asymmetry and exhibits the same three phases as for x-direction driving along with a fourth phase which, at high driving frequencies, consists of a series of constant velocity phases, each with different average skyrmion velocities. For low frequencies, the constant velocity phase is lost and the skyrmion speed increases linearly with increasing ac drive amplitude due to a Magnus boost effect. Finally, our findings suggest new ways to create reliable data transport for spintronic devices using skyrmions as information carriers, where the skyrmion direction and speed can be controlled by varying only the ac drive amplitude and frequency.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Temperature scaling behavior of the linear magnetoresistance observed in high-temperature superconductors

An analytical model invoking variations in the charge-carrier density is used to generate magnetoresistance curves that are almost indistinguishable from those produced by sophisticated numerical models. This demonstrates that, though disorder is pivotal in causing linear magnetoresistance, the form of the magnetoresistance thus generated is insensitive to details of the disorder. Additionally, taken in conjunction with the temperature (T) dependence of the zero-field resistivity, realistic levels of disorder are shown to be sufficient to explain the linear magnetoresistance and field-T resistance scaling observed in high-temperature pnictide and cuprate superconductors. Hence, though the T-linear zero-field resistance is a definite signature of the “strange metal” state of high-temperature superconductors, their linear magnetoresistance and its scaling is unlikely to be so.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Studying the Peak Force of Magnetic Linear Motion Devices Using Dimensional Neutrality

The merits of different sizing equations for linear motion magnetic force devices are assessed. A peak force scaling analysis for a linear magnetic coupling with respect to the volumetric force density, mass force density, magnetic shear stress and energy density is compared. By utilizing a 3-D analytic based model of a Halbach cylinder magnetic coupling it is shown that the maximizing of volumetric and mass force density is not an appropriate sizing metric for a linear magnetic actuator. Only energy density and magnetic shear stress are dimensionally neutral sizing metrics for linear actuators.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Linear Solvers for Collector Systems of Generalized Large-scale Inverter-Based Resources

Collector systems for inverter-based resources (IBRs) are typically represented by equivalent circuits for electromagnetic transient (EMT) simulations. Recent studies have revealed that modeling a detailed collector system is essential to accurately represent the behavior of IBRs, especially when dealing with partial tripping during external disturbances. However, there are several challenges in simulating a detailed EMT model of a collector system due to the time required to simulate such systems. Thus, this paper investigates the modeling of a detailed collector system, taking into account its configuration and components as defined in IEEE standard 2800. The configurations include the collector systems of generalized large-scale IBR plants. The components include the main IBR transformer, collector bus, and feeders with lines and/or cables. The EMT model of the collector system is represented by differential algebraic equations (DAEs) that are discretized to form linear equations that are solved using linear solvers. In this paper, linear solvers are proposed based on the Schur complement method, which are utilized for simulation of the EMT model of collector systems of generalized large-scale IBRs to accelerate simulation speed while maintaining the accuracy of the results. The proposed solvers are verified by comparing the performance to that of linear solvers provided in MATLAB.

Choi, Jongchan↗
DOE OSTI2021

Learning Optimal Power Flow Solutions using Linearized Models in Power Distribution Systems

Solving nonlinear optimal power flow (OPF) problem is computationally expensive, and poses scalability challenges for power distribution networks. An alternative to solving the original nonlinear OPF is the linear approximated OPF models. Although, these linear approximated OPF models are fast, the resulting solutions may result in significant optimality gap. Lately, the application of machine learning (ML) methods in successfully solving the nonlinear OPF has been reported. These methods learn and estimate the nonlinear control policies using a purely data-driven approach. In this paper, we propose an approach to complements the ML based approach to solving OPF using solutions from known linearized OPF model. Specifically, we use supervised learning to map the solutions of linear OPF to nonlinear control variables. Unlike, the traditional ML based methods for OPF that approximate the full distribution feeder model using function approximation, our approach uses a two-node approximation of radial networks. The proposed approach is validated using IEEE 123 bus test system for OPF solutions obtained using the nonlinear OPF models.

optimal power flow, power distribution systems, su↗
DOE OSTI2024

Estimation and Visualization of Isosurface Uncertainty from Linear and High-Order Interpolation Methods

Isosurface visualization is fundamental for exploring and analyzing 3D volumetric data. Marching cubes (MC) algorithms with linear interpolation are commonly used for isosurface extraction and visualization. Although linear interpolation is easy to implement, it has limitations when the underlying data is complex and high-order, which is the case for most real-world data. Linear interpolation can output vertices at the wrong location. Its inability to deal with sharp features and features smaller than grid cells can lead to an incorrect isosurface with holes and broken pieces. Despite these limitations, isosurface visualizations typically do not include insight into the spatial location and the magnitude of these errors. We utilize high-order interpolation methods with MC algorithms and interactive visualization to highlight these uncertainties. Our visualization tool helps identify the regions of high interpolation errors. It also allows users to query local areas for details and compare the differences between isosurfaces from different interpolation methods. In addition, we employ high-order methods to identify and reconstruct possible features that linear methods cannot detect. We showcase how our visualization tool helps explore and understand the extracted isosurface errors through synthetic and real-world data.

Ouermi, Timbwaoga↗
DOE OSTI2024

Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

As an optical processor, a diffractive deep neural network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees of freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are nonnegative, acting on diffraction-limited optical intensity patterns at the input field of view. Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Investigation of Microwave Transducers for Linearity Dependence and Applications in Quantum Networking

Quantum devices have the potential to revolutionize applications in computing, communications, and sensing; however, current state-of-art resources must operate at extremely low temperatures, making the routing of microwave control and readout signals challenging to scale. Interest in microwave photonic solutions to this problem has grown in recent years, in which control signals are delivered to the cold stage via optical fiber, where they are converted to electrical signals through photodetection. Overall link performance depends strongly on the characteristics of the photodiode, yet detailed measurements of many detector properties remain lacking at cold temperatures. In this work, we examine and compare the performance of a modified uni-traveling carrier photodiode (MUTC-PD) at both room (300 K) and liquid nitrogen (80 K) temperatures, focusing in particular on responsivity, bandwidth, and linearity. In line with previous work, we find a sharp reduction in responsivity at 1550 nm as temperature decreases, while RF bandwidth remains steady. Interestingly, our linearity tests reveal that the RF output saturates more quickly at 80 K, suggesting reduced linearity with lower temperature, the cause of which is still under investigation. Our results should help contribute to the understanding and future design of highly linear cryogenic quantum links.

Bolton, Summer↗
DOE OSTI2023

Splitting phonons: Building a platform for linear mechanical quantum computing

Linear optical quantum computing provides a desirable approach to quantum computing, with only a short list of required computational elements. The similarity between photons and phonons points to the interesting potential for linear mechanical quantum computing using phonons in place of photons. Although single-phonon sources and detectors have been demonstrated, a phononic beam splitter element remains an outstanding requirement. Here we demonstrate such an element, using two superconducting qubits to fully characterize a beam splitter with single phonons. We further use the beam splitter to demonstrate two-phonon interference, a requirement for two-qubit gates in linear computing. In conclusion, this advances a new solid-state system for implementing linear quantum computing, further providing straightforward conversion between itinerant phonons and superconducting qubits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A linear collider vision for the future of particle physics

In this paper we review the physics opportunities at linear e + e - colliders with a special focus on high centre-of-mass energies and beam polarisation, take a fresh look at the various accelerator technologies available or under development and, for the first time, discuss how a facility first equipped with a technology that is mature today could be upgraded with technologies of tomorrow to reach much higher energies and/or luminosities. In addition, we discuss detectors, alternative collider modes, as well as opportunities for beyond-collider experiments and R&D facilities as part of a linear collider facility (LCF). The material of this paper supports all plans for e + e - linear colliders and the additional opportunities they offer, independently of technology choice or proposed site, as well as R&D for advanced accelerator technologies. This joint perspective on the physics goals, early technologies and upgrade strategies has been developed by the LCVision team based on an initial discussion at LCWS2024 in Tokyo and a follow-up at the LCVision Community Event at CERN in January 2025. It heavily builds on decades of achievements of the global linear collider community, in particular in the context of CLIC and ILC.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Reproduced Computational Results Report for “Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing”

The article titled “Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing” by Anzt et al. presents a modern, linear operator centric, C++ library for sparse linear algebra. Experimental results in the article demonstrate that Ginkgo is a flexible and user-friendly framework capable of achieving high-performance on state-of-the-art GPU architectures. In this report, the Ginkgo library is installed and a subset of the experimental results are reproduced. Specifically, the experiment that shows the achieved memory bandwidth of the Ginkgo Krylov linear solvers on NVIDIA A100 and AMD MI100 GPUs is redone and the results are compared to what presented in the published article. Upon completion of the comparison, the published results are deemed reproducible.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

hypredrive: high-level interface for solving linear systems with hypre

This software introduces a high-level interface designed to simplify solving linear systems using hypre, a renowned library for such computational challenges. It is crafted to be accessible and user-friendly, making the powerful capabilities of hypre available to a broader audience without requiring in-depth technical knowledge. The interface is characterized by its use of YAML for input, a format celebrated for its structured yet straightforward readability. This choice ensures that users can easily configure the software to meet their specific needs. Additionally, the software boasts an intuitive API that encapsulates hypre's functionalities, making it easier for users to interact with the process of solving linear systems. It is particularly beneficial for prototyping, offering a quick and efficient means to test various solver and preconditioner configurations. Furthermore, the software allows for the creation of an offline testing framework in which predefined linear systems are read from files and benchmarked with user-defined solution strategies. This makes it an invaluable tool for developers and researchers exploring and validating their computational models. Overall, the software serves as a bridge, bringing the advanced computational capabilities of hypre closer to users who may need more specialized technical expertise, thereby facilitating innovation and exploration in the field of numerical linear algebra.

Paludetto Magri, Victor↗
DOE OSTI2021

Shotgun Immunoproteomic Approach for the Discovery of Linear B-Cell Epitopes in Biothreat Agents Francisella tularensis and Burkholderia pseudomallei

Peptide-based subunit vaccines are coming to the forefront of current vaccine approaches, with safety and cost-effective production among their top advantages. Peptide vaccine formulations consist of multiple synthetic linear epitopes that together trigger desired immune responses that can result in robust immune memory. The advantages of linear compared to conformational epitopes are their simple structure, ease of synthesis, and ability to stimulate immune responses by means that do not require complex 3D conformation. Prediction of linear epitopes through use of computational tools is fast and cost-effective, but typically of low accuracy, necessitating extensive experimentation to verify results. On the other hand, identification of linear epitopes through experimental screening has been an inefficient process that requires thorough characterization of previously identified full-length protein antigens, or laborious techniques involving genetic manipulation of organisms. In this study, we apply a newly developed generalizable screening method that enables efficient identification of B-cell epitopes in the proteomes of pathogenic bacteria. As a test case, we used this method to identify epitopes in the proteome of Francisella tularensis (Ft), a Select Agent with a well-characterized immunoproteome. Our screen identified many peptides that map to known antigens, including verified and predicted outer membrane proteins and extracellular proteins, validating the utility of this approach. We then used the method to identify seroreactive peptides in the less characterized immunoproteome of Select Agent Burkholderia pseudomallei (Bp). This screen revealed known Bp antigens as well as proteins that have not been previously identified as antigens. Although B-cell epitope prediction tools Bepipred 2.0 and iBCE-EL classified many of our seroreactive peptides as epitopes, they did not score them significantly higher than the non-reactive tryptic peptides in our study, nor did they assign higher scores to seroreactive peptides from known Ft or Bp antigens, highlighting the need for experimental data instead of relying on computational epitope predictions alone. The present workflow is easily adaptable to detecting peptide targets relevant to the immune systems of other mammalian species, including humans (depending upon the availability of convalescent sera from patients), and could aid in accelerating the discovery of B-cell epitopes and development of vaccines to counter emerging biological threats.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Characterizing the Non-linear Interactions Between Tide, Storm Surge, and River Flow in the Delaware Bay Estuary, United States

Low-lying coastal areas in the mid-Atlantic region are prone to compound flooding resulting from the co-occurrence of river floods and coastal storm surges. To better understand the contribution of non-linear tide-surge-river interactions to compound flooding, the unstructured-grid Finite Volume Community Ocean Model was applied to simulate coastal storm surge and flooding in the Delaware Bay Estuary in the United States. The model was validated with tide gauge data in the estuary for selected hurricane events. Non-linear interactions between tide-surge-river were investigated using a non-stationary tidal analysis method, which decomposes the interactions’ components at the frequency domain. Model results indicated that tide-river interactions damped semidiurnal tides, while the tide-surge interactions mainly influenced diurnal tides. Tide-river interactions suppressed the water level upstream while tide-surge interaction increased the water level downstream, which resulted in a transition zone of damping and enhancing effects where the tide-surge-river interaction was prominent. Evident compound flooding was observed as a result of non-linear tide-surge-river interactions. Furthermore, sensitivity analysis was carried out to evaluate the effect of river flooding on the non-linear interactions. The transition zone of damping and enhancing effects shifted downstream as the river flow rate increased.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Note on the Effects of Linear Topology Preservation in Monte Carlo Simulations of Knotted Proteins

Monte Carlo simulations are a powerful technique and are widely used in different fields. When applied to complex molecular systems with long chains, such as those in synthetic polymers and proteins, they have the advantage of providing a fast and computationally efficient way to sample equilibrium ensembles and calculate thermodynamic and structural properties under desired conditions. Conformational Monte Carlo techniques employ a move set to perform the transitions in the simulation Markov chain. While accepted conformations must preserve the sequential bonding of the protein chain model and excluded volume among its units, the moves themselves may take the chain across itself. We call this a break in linear topology preservation. In this manuscript, we show, using simple protein models, that there is no difference in equilibrium properties calculated with a move set that preserves linear topology and one that does not. However, for complex structures, such as those of deeply knotted proteins, the preservation of linear topology provides correct equilibrium results but only after long relaxation. In any case, to analyze folding pathways, knotting mechanisms and folding kinetics, the preservation of linear topology may be an unavoidable requirement.

Especial, João N. C. (ORCID:0000000254765370)↗
DOE OSTI2025

Potential absence of observed π2 linear-chain structures in 14O via 10C(α,α) resonances scattering

Background: The preference for light nuclear systems to coagulate into -particle clusters has been well-studied. The possibility of a linear chain configuration of -particles would allow for a new way to study this phenomenon. Purpose: A rotational band of states in C has been claimed showing a linear chain structure. The mirror system, O, has been studied here to examine how this linear chain structure is affected by replacing the valence neutrons with protons. Method: A beam of C was incident into a chamber filled with He:CO gas with the tracks recorded inside the TexAT Time Projection Chamber and the recoil -particles detected by a silicon detector array to measure the cross section. Results: The experimental cross section was compared with previous studies and fit using R-Matrix theory with the previously-observed O states being transformed to the C using mirror symmetry. The measured cross section does not replicate the claimed states, with the predicted cross section exceeding that observed at several energies and angles. Conclusion: A series of possibilities are highlighted with the most likely being that the originally-seen C states did not constitute a rotational band with a potentially incorrect spin assignment due to the limitations of the angular correlation method with non-zero spin particles. The work highlights the difficulties in measuring broad resonances corresponding to a linear chain state in a high level density.

Bishop, J.↗