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DOE OSTI2025

Measurements of W + W − production cross-sections in pp collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

Measurements of W + W − → e ± νμ ∓ ν production cross-sections are presented, providing a test of the predictions of perturbative quantum chromodynamics and the electroweak theory. The measurements are based on data from pp collisions at $\sqrt{s}$ = 13 TeV recorded by the ATLAS detector at the Large Hadron Collider in 2015–2018, corresponding to an integrated luminosity of 140 fb −1 . The number of events due to top-quark pair production, the largest background, is reduced by rejecting events containing jets with b-hadron decays. An improved methodology for estimating the remaining top-quark background enables a precise measurement of W + W − cross-sections with no additional requirements on jets. The fiducial W + W − cross-section is determined in a maximum-likelihood fit with an uncertainty of 3.1%. The measurement is extrapolated to the full phase space, resulting in a total W + W − cross-section of 127 ± 4 pb. Differential cross-sections are measured as a function of twelve observables that comprehensively describe the kinematics of W + W − events. The measurements are compared with state-of-the-art theory calculations and excellent agreement with predictions is observed. A charge asymmetry in the lepton rapidity is observed as a function of the dilepton invariant mass, in agreement with the Standard Model expectation. A CP-odd observable is measured to be consistent with no CP violation. Limits on Standard Model effective field theory Wilson coefficients in the Warsaw basis are obtained from the differential cross-sections.

Accelerator Physics↗

DOE OSTI2025

Machine learning for improved current-density reconstruction from two-dimensional vector magnetic images

The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high-spatial-frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional images of vector magnetic fields acquired by a quantum diamond microscope . Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower-signal-to-noise-ratio data, significantly reducing the data collection time and permitting reconstructions of weaker and three-dimensional current sources. Published by the American Physical Society 2025

Reed, Niko R. (ORCID:0009000305222403)↗

NASA NTRS1975

Vibrational energy exchange in CO-CO collisions at low temperature

The V-V exchange rates for CO in the ground electronic state have been measured at a temperature of 75 K. The rates determined were for the processes CO(nu = n) + CO(nu = 0) yields CO(nu = n - 1) + CO(nu = 1) - increment E, with n lying in the closed interval from 2 to 8. The values obtained are close to published 100 K data, thus indicating an insensitivity to temperature. It appears that multirotational quantum jumps may be important in explaining the measured rates.

Gower, M. C.↗

NASA NTRS1977

Absorption spectrum of Pb I between 1350 and 2041 A

The high resolution absorption spectrum of Pb I is reported between 1350 and 2041 A. Transitions are observed from the 6p2 (1/2,1/2)0, (3/2,1/2)1, and (3/2,1/2)2 levels to levels with J less than or equal to 2 associated with 6pns and 6pnd configurations. Energy levels have been determined with n(asterisk) values as high as 74. More than 500 spectral features and 370 odd parity energy levels are reported, a major part of which are new. These observations include five electric quadrupole transitions and 31 nuclear-spin-induced transitions from the Pb-207 isotope. Ionization limits of 59819.57 per cm and 73900.64 per cm have been determined for levels converging on the 6p 2P(1/2)O and 6p 2P(3/2)O levels of Pb II, respectively. An analysis of these data in terms of Lu-Fano graphical methods and multichannel quantum defect parametrization also is presented.

Brown, C. M.↗

NASA NTRS1993

Momentum loss in proton-nucleus and nucleus-nucleus collisions

An optical model description, based on multiple scattering theory, of longitudinal momentum loss in proton-nucleus and nucleus-nucleus collisions is presented. The crucial role of the imaginary component of the nucleon-nucleon transition matrix in accounting for longitudinal momentum transfer is demonstrated. Results obtained with this model are compared with Intranuclear Cascade (INC) calculations, as well as with predictions from Vlasov-Uehling-Uhlenbeck (VUU) and quantum molecular dynamics (QMD) simulations. Comparisons are also made with experimental data where available. These indicate that the present model is adequate to account for longitudinal momentum transfer in both proton-nucleus and nucleus-nucleus collisions over a wide range of energies.

Khan, Ferdous↗

NASA NTRS2011

Performance of the QWIP Focal Plane Arrays for NASA's Landsat Data Continuity Mission

The focal plane assembly for the Thermal Infrared Sensor (TIRS) instrument on NASA's Landsat Data Continuity Mission (LDCM) consists of three 512 x 640 GaAs Quantum Well Infrared Photodetector (QWIP) arrays. The three arrays are precisely mounted and aligned on a silicon carrier substrate to provide a continuous viewing swath of 1850 pixels in two spectral bands defined by filters placed in close proximity to the detector surfaces. The QWIP arrays are hybridized to Indigo ISC9803 readout integrated circuits (ROICs). QWIP arrays were evaluated from four laboratories; QmagiQ, (Nashua, NH), Army Research Laboratory, (Adelphi, MD}, NASA/ Goddard Space Flight Center, (Greenbelt, MD) and Thales, (Palaiseau, France). All were found to be suitable. The final discriminating parameter was the spectral uniformity of individual pixels relative to each other. The performance of the QWIP arrays and the fully assembled, NASA flight-qualified, focal plane assembly will be reviewed. An overview of the focal plane assembly including the construction and test requirements of the focal plane will also be described.

Jhabvala, M.↗

NASA NTRS2006

NASA Tech Briefs, April 2006

The topics covered include: 1) Replaceable Sensor System for Bioreactor Monitoring; 2) Unitary Shaft-Angle and Shaft-Speed Sensor Assemblies; 3) Arrays of Nano Tunnel Junctions as Infrared Image Sensors; 4) Catalytic-Metal/PdO(sub x)/SiC Schottky-Diode Gas Sensors; 5) Compact, Precise Inertial Rotation Sensors for Spacecraft; 6) Universal Controller for Spacecraft Mechanisms; 7) The Flostation - an Immersive Cyberspace System; 8) Algorithm for Aligning an Array of Receiving Radio Antennas; 9) Single-Chip T/R Module for 1.2 GHz; 10) Quantum Entanglement Molecular Absorption Spectrum Simulator; 11) FuzzObserver; 12) Internet Distribution of Spacecraft Telemetry Data; 13) Semi-Automated Identification of Rocks in Images; 14) Pattern-Recognition Algorithm for Locking Laser Frequency; 15) Designing Cure Cycles for Matrix/Fiber Composite Parts; 16) Controlling Herds of Cooperative Robots; 17) Modification of a Limbed Robot to Favor Climbing; 18) Vacuum-Assisted, Constant-Force Exercise Device; 19) Production of Tuber-Inducing Factor; 20) Quantum-Dot Laser for Wavelengths of 1.8 to 2.3 micron; 21) Tunable Filter Made From Three Coupled WGM Resonators; and 22) Dynamic Pupil Masking for Phasing Telescope Mirror Segments.

Source record↗

NASA NTRS2018

Analysis of the Two-Level NO PLIF Model for Low-Temperature High-Speed Flow Applications

The current work compares experimentally obtained nitric oxide (NO) laser-induced fluorescence (LIF) spectra with the equivalent spectra obtained analytically. The experimental spectra are computed from captured images of fluorescence in a gas cell and from a laser sheet passing through the fuel-air mixing flowfield produced by a high-speed fuel injector. The fuel injector is a slender strut that is currently being studied as a part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center. This injector is placed downstream of a Mach 6 facility nozzle, which simulates the high Mach number airflow at the entrance of a scramjet combustor, and injects helium, which is used as an inert substitute for hydrogen fuel. Experimental planar (P) LIF is obtained by using a UV laser to excite fluorescence from the NO molecules that are present in either a gas cell or the facility air used for the EIMP experiments. The experimental data are obtained for several segments of the NO fluorescence spectrum. The selected segments encompass LIF lines with rotational quantum numbers appropriate for low-to-moderate temperature flows similar to those corresponding to the nominal experimental flow conditions. The experimental LIF spectra are then evaluated from the data and compared with those obtained from the theoretical models. The theoretical spectra are obtained from LIFBASE and LINUS software, and from a simplified version of the two-level fluorescence model. The equivalent analytic PLIF images are also obtained by applying only the simplified model to the results of the Reynolds-averaged simulations (RAS) of the mixing flowfield. Good agreement between the experimental and theoretical results provides increased confidence in both the simplified LIF modeling and CFD simulations for further investigations of high-speed injector performance using this approach.

Drozda, Tomasz G.↗

NASA NTRS2018

Analysis of the Two-Level NO PLIF Model for Low-Temperature High-Speed Flow Applications

Tomasz G Drozda↗

DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Quantum Transfer Learning to Boost Dementia Detection

Dementia is a devastating condition with profound implications for individuals, families, and healthcare systems. Early and accurate detection of dementia is critical for timely intervention and improved patient outcomes. While classical machine learning and deep learning approaches have been explored extensively for dementia prediction, these solutions often struggle with high-dimensional biomedical data and large-scale datasets, quickly reaching computational and performance limitations. To address this challenge, quantum machine learning (QML) has emerged as a promising paradigm, offering faster training and advanced pattern recognition capabilities. This work aims to demonstrate the potential of quantum transfer learning (QTL) to enhance the performance of a weak classical deep learning model applied to a binary classification task for dementia detection. Besides, we show the effect of noise on the QTL-based approach, investigating the reliability and robustness of this method. Using the OASIS 2 dataset, we show how quantum techniques can transform a suboptimal classical model into a more effective solution for biomedical image classification, highlighting their potential impact on advancing healthcare technology.

Bhowmik, Sounak [University of Tennessee, Knoxvill↗

NASA NTRS2013

NASA Brief: Q-Thruster Physics

Q-thrusters are a low-TRL form of electric propulsion that operates on the principle of pushing off of the quantum vacuum. A terrestrial analog to this is to consider how a submarine uses its propeller to push a column of water in one direction, while the sub recoils in the other to conserve momentum -the submarine does not carry a "tank" of sea water to be used as propellant. In our case, we use the tools of Magnetohydrodynamics (MHD) to show how the thruster pushes off of the quantum vacuum which can be thought of as a sea of virtual particles -principally electrons and positrons that pop into and out of existence, and where fields are stronger, there are more virtual particles. The idea of pushing off the quantum vacuum has been in the technical literature for a few decades, but to date, the obstacle has been the magnitude of the predicted thrust which has been derived analytically to be very small, and therefore not likely to be useful for human spaceflight. Our recent theoretical model development and test data suggests that we can greatly increase the magnitude of the negative pressure of the quantum vacuum and generate a specific force such that technology based on this approach can be competitive for in-space propulsion approx. 0.1N/kW), and possibly for terrestrial applications (approx. 10N/kW). As an additional validation of the approach, the theory allows calculation of physics constants from first principles: Gravitational constant, Planck constant, Bohr radius, dark energy fraction, electron mass.

White, Harold↗

DOE OSTI2024

Quantum Annealing for Real-World Machine Learning Applications

Optimizing the training of a machine learning pipeline is important for reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-Wave systems and is available to the research community for experiments. Recent experimental results on a variety of machine learning applications have shown interesting results especially under the conditions where the performance of classical machine learning techniques are limited such as limited training data and high dimensional features. This chapter explores the application of D-Wave’s quantum annealer for optimizing machine learning pipelines for real-world classification problems. We review the application domains on which a physical quantum annealer has been used to train machine learning classifiers. We discuss and analyze the experiments performed on the D-Wave quantum annealer for applications such as image recognition, remote sensing imagery, security, computational biology, biomedical sciences, and physics. We discuss the possible advantages and the problems for which quantum annealing is likely to be advantageous over classical computation.

Kumar nath, Rajdeep↗

DOE OSTI2024

Quantum surrogate models for uncertainty quantification

Surrogate models are a critical ingredient to computation-based design and validation of many DOE mission-relevant physical systems. When first-principles computation of properties of a physical systems becomes pro hibitive, surrogate models are the only path towards achieving tasks such as uncertainty quantification (UQ), exploration of design space, and validation of design choices. In this project we have developed and demonstrated a new surro gate modeling paradigm for complex models that is data-driven, non-intrusive, and has the potential to be versatile and equipped with performance guaran tees. This combination of features is absent in existing surrogate modeling tools. The framework we have developed in this project exploits a quantum-classical correspondence to establish a quantum system that mimics the dynamics of the classical Hamiltonian system from which data in the form of temporal snapshots is provided. Since quantum dynamics propagates distributions over observables, the framework is naturally suited to propagation of epistemic uncertainties in the form of distributions over initial state and parametric uncertainties. In this project, we take the first step in establishing this novel framework by deriving a quantization and de-quantization procedure, demonstrating the accuracy of the quantum surrogate models these define using two model systems, and defining the next steps in maturing the framework towards a tool applicable to Sandia mission-relevant problems.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1992

Direct solar pumping of semiconductor lasers: A feasibility study

This report describes results of NASA Grant NAG-1-1148, entitled Direct Solar Pumping of Semiconductor Lasers: A Feasibility Study. The goals of this study were to provide a preliminary assessment of the feasibility of pumping semiconductor lasers in space with directly focused sunlight and to identify semiconductor laser structures expected to operate at the lowest possible focusing intensities. It should be emphasized that the structures under consideration would provide direct optical-to-optical conversion of sunlight into laser light in a single crystal, in contrast to a configuration consisting of a solar cell or storage battery electrically pumping a current injection laser. With external modulation, such lasers could perhaps be efficient sources for intersatellite communications. We proposed specifically to develop a theoretical model of semiconductor quantum-well lasers photopumped by a broadband source, test it against existing experimental data where possible, and apply it to estimating solar pumping requirements and identifying optimum structures for operation at low pump intensities. These tasks have been accomplished, as described in this report of our completed project. The report is organized as follows: Some general considerations relevant to the solar-pumped semiconductor laser problem are discussed in Section 2, and the types of structures chosen for specific investigation are described. The details of the laser model we developed for this work are then outlined in Section 3. In Section 4, results of our study are presented, including designs for optimum lattice-matched and strained-layer solar-pumped quantum-well lasers and threshold pumping estimates for these structures. It was hoped at the outset of this work that structures could be identified which could be expected to operate continuously at solar photoexcitation intensities of several thousand suns, and this indeed turned out to be the case as described in this section. Our project is summarized in Section 5, and information on publications resulting from this work is provided in Section 6.

Anderson, Neal G.↗

DOE OSTI2026

CRADA Number NFE-24-10110 with Qubit Engineering Inc. (CRADA Final Report)

Over the past year, the Qubit Engineering team has pushed the frontiers of power‑grid optimization, working in close collaboration with Oak Ridge National Laboratory (ORNL) and the Tennessee Valley Authority (TVA). Their progress is reflected in three newly submitted conference papers, “Unified Relational GNN Architecture for AC Optimal Power Flow Calculations in Electric Grids,” “Graph‑Based Attention Mechanisms for Solving the AC Optimal Power Flow Problem in Electrical‑Power Networks,” and “Enhanced Power‑Grid Maintenance Planning and Quantum‑Inspired Combinatorial Prospects.” These publications showcase state‑of‑the‑art graph‑neural‑network methods for AC‑OPF and novel quantum‑inspired heuristics for maintenance scheduling. Beyond the academic results, the Qubit team has converted the research into two production‑grade tools built on TVA data: Neuro‑Grid, an AI‑driven power‑flow simulator that provides instant, interactive full‑grid load‑flow visualizations, and Quanta‑Grid, a quantum‑inspired maintenance‑scheduling engine to support logistics optimization for power utilities. Together, these advances demonstrate how Qubit’s partnership with ORNL and TVA is delivering practical, physics‑grounded analytics for next‑generation grid management.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Recurrent convolutional neural networks for modeling nonadiabatic dynamics of quantum-classical systems

Recurrent neural networks (RNNs) have recently been extensively applied to model the time evolution in fluid dynamics, weather predictions, and even chaotic systems due to their ability to capture temporal dependencies and sequential patterns in data. Here we present an RNN model based on convolutional neural networks for modeling the nonlinear nonadiabatic dynamics of hybrid quantum-classical systems. The dynamical evolution of the hybrid systems is governed by equations of motion for classical degrees of freedom and von Neumann equation for electrons. The Physics-Aware Recurrent Convolution (PARC) neural network structure incorporates a differentiator-integrator architecture that inductively models the spatiotemporal dynamics of generic physical systems. Here, we apply our RNN approach to learn the space-time evolution of a one-dimensional semiclassical Holstein model after an interaction quench. For shallow quenches (small changes in electron-lattice coupling), the deterministic dynamics can be accurately captured using a single-CNN-based recurrent network. In contrast, deep quenches induce chaotic evolution, making long-term trajectory prediction significantly more challenging. Nonetheless, we demonstrate that the PARC-CNN architecture can effectively learn the statistical climate of the Holstein model under deep-quench conditions.

Holstein model↗

DOE OSTI2025

Synthesis and Characterization of Metastable Cobalt Honeycomb KCoAsO 4

The Kitaev model has served as a long-sought-after target in the realization of a quantum spin liquid that could host Majorana Fermions. Such non-Abelian anyons could revolutionize quantum computing if properly implemented to overcome decoherence. A 3d 7 electronic configuration, like Co 2+ , has been explored by theory and experimental work to design Kitaev materials. Here, in this study, we report the synthesis of a new cobaltate honeycomb material KCoAsO 4 . The compound is synthesized through a low-temperature solution route and crystallized in space group R¯3 with lattice parameters a = 5.0394(1) and c = 28.6790(1) as determined by neutron powder diffraction. The crystal structure follows motifs similar to those of the honeycomb compound BaCo2(AsO 4 ) 2 but presents differing magnetic behavior. Magnetization/heat capacity measurements on the powder show antiferromagnetic transition T N = 14 K. Two lower-temperature transitions are present in susceptibility at low field that resemble spin reorientations. Magnetization data as a function of field have curvature indicative of metamagnetic behavior below the magnetic ordering temperature, with the magnetic ordering suppressed upon application of a higher magnetic field. Computational studies suggest the presence of a weak nearest-neighbor Kitaev term, K 1 , consistent with related honeycomb cobaltates. Together, the data suggest that this material should present a new platform for developing Kitaev quantum spin liquids.

cobaltate↗