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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Including contributions from entanglement islands to the reflected entropy

Recent work has demonstrated the need to include contributions from entanglement islands when computing the entanglement entropy in quantum field theory states coupled to regions of semiclassical gravity. We propose a new formula for the reflected entropy that includes additional contributions from such islands. We derive this formula from the gravitational path integral by finding additional saddles that include generalized replica wormholes. We also demonstrate that our covariant formula satisfies all the inequalities required of the reflected entropy. We use this formula in various examples that demonstrate its relevance in illustrating the structure of multipartite entanglement that are invisible to the entropies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Applications of flow models to the generation of correlated lattice QCD ensembles

Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different action parameters. This work demonstrates how these correlations can be exploited for variance reduction in the computation of observables. Three different proof-of-concept applications are demonstrated using a novel residual flow architecture: continuum limits of gauge theories, the mass dependence of QCD observables, and hadronic matrix elements based on the Feynman–Hellmann approach. In all three cases, it is shown that statistical uncertainties are significantly reduced when machine-learned flows are incorporated as compared with the same calculations performed with uncorrelated ensembles or direct reweighting. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Terahertz Second-Harmonic Generation from Lightwave Acceleration of Symmetry-Breaking Nonlinear Supercurrents

We report terahertz (THz) light-induced second harmonic generation, in superconductors with inversion symmetry that forbid even-order nonlinearities. The THz second harmonic emission vanishes above the superconductor critical temperature and arises from precession of twisted Anderson pseudospins at a multicycle, THz driving frequency that is not allowed by equilibrium symmetry. We explain the microscopic physics by a dynamical symmetry breaking principle at sub-THz-cycle by using quantum kinetic modeling of the interplay between strong THz-lightwave nonlinearity and pulse propagation. In this work, the resulting nonzero integrated pulse area inside the superconductor leads to light-induced nonlinear supercurrents due to subcycle Cooper pair acceleration, in contrast to dc-biased superconductors, which can be controlled by the band structure and THz driving field below the superconducting gap.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Magnetic phase diagram and multiple field-induced states in the intermetallic triangular-lattice antiferromagnet NdAuAl 4 Ge 2 with Ising-like spins

Geometrical frustration and the enhancement of strong quantum fluctuations in two-dimensional triangular antiferromagnets can lead to various intriguing phenomena. Here, in this work, we studied the spin-1/2 triangular lattice antiferromagnet NdAuAl 4 Ge 2 . Thermodynamic and transport properties, such as magnetization and specific heat together with the resistivity measurements were performed. In zero field, two successive phase transitions were observed at T N1 = 1.75 ± 0.02 and T N2 = 0.49 ± 0.02 K, respectively. Under magnetic field, XXZ-type anisotropy was revealed with the moments pointing along the easy c axis. For B∥c, multiple field-induced states were observed, and the magnetic phase diagram was established based on the specific-heat and magnetization data. The temperature-dependent resistivity measurements indicate that NdAuAl 4 Ge 2 is a good metal. It is very likely that both the long-range Ruderman-Kittel-Kasuya-Yosida interactions and the geometrical frustration play important roles in this case.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Kolmogorov-Arnold wavefunctions

Here, this work investigates Kolmogorov-Arnold network-based (KAN) wave-function Ansätz as viable representations for quantum Monte Carlo simulations. Through systematic analysis of one-dimensional model systems, we evaluate their computational efficiency and representational power against established methods. Our numerical experiments suggest some efficient training methods and we explore how the computational cost scales with desired precision, particle number, and system parameters. Roughly speaking, KANs seem to be 10 times cheaper computationally than other neural-network-based Ansätz . We also introduce a novel approach for handling strong short-range potentials—a persistent challenge for many numerical techniques—which generalizes efficiently to higher-dimensional, physically relevant systems with short-ranged strong potentials common in atomic and nuclear physics.

1-dimensional systems↗
DOE OSTI2024

GeoCricket

SAND2025-12229O Geospatial Critical Infrastructure and Census Data Stockpile Tool (GeoCricket) is a set of functions that collect critical infrastructure and census data for use in the Resilient Node Cluster Analysis Tool (ReNCAT) and Quantum Geographic Information System Social Burden Calculator. It can also act to inform other place-based work. The code queries public-facing Representational State Transfer (REST) servers to collect geospatial data related to a specific area. It then exports that data as standard geographic information system file types or as a .csv file. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Haines, John↗
DOE OSTI2026

Final Report for Electro-oxidative valorization of biomass: design strategies for selective and stable catalysis

This project aimed to develop a fundamental understanding of the factors that dictate activity and selectivity during electrochemical partial oxidation of multi-carbon organic molecules derived from biomass. Value-adding selective conversions of alcohols to aldehydes, and aldehydes to carboxylic acids were considered, with the ultimate goal to establish general principles for controlling selectivity in organic oxidations. Specific aims involved the use of furfural (FF) and 5-hydoxymethyfurfural (HMF) as model systems and seek to develop knowledge for control of mechanisms that permit selective conversions between oxygenate functional groups with prevention or control of C-C cleavage steps. Another major focus of the work was in achieving these transformations in acidic conditions, which contrasts most existing work (done in base), but is crucial toward compatibility with common pretreatments used to generate small molecules from lignocellulose (e.g. acid hydrolysis or pyrolysis). The work involved well-defined catalyst material synthesis, comparative kinetics, and operando spectroscopies (PI Holewinski), supported by quantum chemical simulations (PI Janik), enabling progress toward efficient, acid-stable oxidation catalysis for biomass upgrading.

Holewinski, Adam [University of Colorado Boulder] ↗
DOE OSTI2026

Confinement and String Breaking in the Compact Abelian Higgs Model

While real-time simulation of Quantum Chromodynamics remains technologically out of reach, simplified models for studying elements of QCD phenomenology abound. This work presents a simple model, a spin-1 truncation of the Compact Abelian Higgs Model simulated on qutrit sites, in which confinement and string breaking is accessible to current simulation methods. In the low-energy regime of 1+1D scalar electrodynamics, the heavy modes are integrated out, producing a spin chain effective Hamiltonian in which Gauss' law is implicitly satisfied. We study the spectrum of string-like excitations using DMRG methods on the order of 100 sites. We demonstrate that an added, local chemical potential, playing a role analogous to external charges, permits parameter-dependent measurements of physical features of interest like the string tension and effective meson mass. Varying the chemical potential also permits a characterization of string stability not assessed in prior studies of confining lattice models.

Senseman, Blake [Iowa U.]↗
DOE OSTI2022

Magnetic Imaging with Spin Defects in Hexagonal Boron Nitride

Optically active spin defects hosted in hexagonal boron nitride (ℎ-BN) are promising candidates for the development of a two-dimensional (2D) quantum sensing unit. Here, we demonstrate quantitative magnetic imaging with ℎ-BN flakes doped with negatively charged boron-vacancy ($V^{-}_{B}$) centers through neutron irradiation. As a proof-of-concept, we image the magnetic field produced by Cr⁢Te 2 , a van der Waals ferromagnet with a Curie temperature slightly above 300 K. Compared to other quantum sensors embedded in 3D materials, the advantages of the ℎ-BN-based magnetic sensor described in this work are its ease of use, high flexibility, and, more importantly, its ability to be placed in close proximity to a target sample. Further, such a sensing unit will likely find numerous applications in 2D materials research by offering a simple way to probe the physics of van der Waals heterostructures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Distributionally Robust Variational Quantum Algorithms With Shifted Noise

Given their potential to demonstrate near-term quantum advantage, variational quantum algorithms (VQAs) have been extensively studied. Although numerous techniques have been developed for VQA parameter optimization, it remains a significant challenge. A practical issue is the high sensitivity of quantum noise to environmental changes, and its propensity to shift in real time. This presents a critical problem as an optimized VQA ansatz may not perform effectively under a different noise environment. For the first time, we explore how to optimize VQA parameters to be robust against unknown shifted noise. We model the noise level as a random variable with an unknown probability density function (PDF), and we assume that the PDF may shift within an uncertainty set. This assumption guides us to formulate a distributionally robust optimization problem, with the goal of finding parameters that maintain effectiveness under shifted noise. We utilize a distributionally robust Bayesian optimization solver for our proposed formulation. This provides numerical evidence in both the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE) with hardware-efficient ansatz, indicating that we can identify parameters that perform more robustly under shifted noise. We regard this work as the first step towards improving the reliability of VQAs influenced by real-time noise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Bending effects and optical properties of WSe 2 nanoribbons of topological phase

A WSe 2 monolayer of 1T' phase is a large band gap quantum spin Hall insulator, supporting dissipationless charge and spin transports through the topologically protected edge states. Here, in this work, we explore the nanoribbon forms of 1T' phase WSe 2 by first-principles density functional calculations and the many-body perturbation GW and Bethe-Salpeter equation method. We found that the 1T' WSe 2 nanoribbon can show topological edge states with a ribbon width of ~4-6 nm. Those edge bands show crossing through the Fermi level an odd number of times, with one kind of spin-polarization connecting the valence band continuum and conduction band continuum. The topological features of the edge bands hold even under small and medium bending in the nanoribbon, while large bending induces large band splitting, resulting in a topological switch-off in the edge bands. The semiconducting 1T' WSe2 nanoribbon shows a large tunability with bending in optical absorption spectra and exciton states. The lowest-energy exciton is changed from optically dark in the flat nanoribbon to bright in the bent nanoribbons. These properties in the 1T' WSe 2 nanoribbons suggest potential applications in controllable quantum electronics and exciton-based quantum information processes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Experimental error mitigation using linear rescaling for variational quantum eigensolving with up to 20 qubits

Quantum computers have the potential to help solve a range of physics and chemistry problems, but noise in quantum hardware currently limits our ability to obtain accurate results from the execution of quantum-simulation algorithms. Various methods have been proposed to mitigate the impact of noise on variational algorithms, including several that model the noise as damping expectation values of observables. In this work, we benchmark various methods, including a new method proposed here. Here, we compare their performance in estimating the ground-state energies of several instances of the 1D mixed-field Ising model using the variational-quantum-eigensolver algorithm with up to 20 qubits on two of IBM's quantum computers. We find that several error-mitigation techniques allow us to recover energies to within 10% of the true values for circuits containing up to about 25 ansatz layers, where each layer consists of CNOT gates between all neighboring qubits and Y-rotations on all qubits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Optimization of Simultaneous Measurement for Variational Quantum Eigensolver Applications

Variational quantum eigensolver (VQE) is a promising algorithm suitable for near-term quantum computers. VQE aims to approximate solutions to exponentially-sized optimization problems by executing a polynomial number of quantum sub-problems. However, the number of subproblems scales as N-4 for typical problems of interest-a daunting growth rate that poses a serious limitation for emerging applications such as quantum computational chemistry. We mitigate this issue by exploiting the simultaneous measurability of subproblems corresponding to commuting terms. Our technique transpiles VQE instances into a format optimized for simultaneous measurement, ultimately yielding 8-30x lower cost. Our work also encompasses a synthesis tool for compiling simultaneous measurement circuits with minimal overhead. We demonstrate experimental validation of our techniques by estimating the ground state energy of deuteron with a quantum computer. We also investigate the underlying statistics of simultaneous measurement and devise an adaptive strategy for mitigating harmful covariance terms.

quantum computing↗
DOE OSTI2021

Quantum-assisted associative adversarial network: applying quantum annealing in deep learning

Abstract Generative models have the capacity to model and generate new examples from a dataset and have an increasingly diverse set of applications driven by commercial and academic interest. In this work, we present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a graphical model. This graphical model is learned by a Boltzmann machine which learns low-dimensional feature representation of data extracted by the discriminator. A quantum processor can be used to sample from the model to train the Boltzmann machine. This novel hybrid quantum-classical algorithm joins a growing family of algorithms that use a quantum processor sampling subroutine in deep learning, and provides a scalable framework to test the advantages of quantum-assisted learning. For the latent space model, fully connected, symmetric bipartite and Chimera graph topologies are compared on a reduced stochastically binarized MNIST dataset, for both classical and quantum sampling methods. The quantum-assisted associative adversarial network successfully learns a generative model of the MNIST dataset for all topologies. Evaluated using the Fréchet inception distance and inception score, the quantum and classical versions of the algorithm are found to have equivalent performance for learning an implicit generative model of the MNIST dataset. Classical sampling is used to demonstrate the algorithm on the LSUN bedrooms dataset, indicating scalability to larger and color datasets. Though the quantum processor used here is a quantum annealer, the algorithm is general enough such that any quantum processor, such as gate model quantum computers, may be substituted as a sampler.

Wilson, Max (ORCID:0000000207983391)↗
DOE OSTI2022

Algebraic Bethe Circuits

The Algebraic Bethe Ansatz (ABA) is a highly successful analytical method used to exactly solve several physical models in both statistical mechanics and condensed-matter physics. Here we bring the ABA into unitary form, for its direct implementation on a quantum computer. This is achieved by distilling the non-unitary R matrices that make up the ABA into unitaries using the QR decomposition. Our algorithm is deterministic and works for both real and complex roots of the Bethe equations. We illustrate our method on the spin-$\frac{1}{2}$ XX and XXZ models. We show that using this approach one can efficiently prepare eigenstates of the XX model on a quantum computer with quantum resources that match previous state-of-the-art approaches. We run small-scale error mitigated implementations on the IBM quantum computers, including the preparation of the ground state for the XX and XXZ models on 4 sites. Finally, we derive a new form of the Yang-Baxter equation using unitary matrices, and also verify it on a quantum computer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Terahertz Spectroscopy of Gas Mixtures with Dual Quantum Cascade Laser Frequency Combs

Terahertz laser frequency combs based on quantum cascade lasers provide coherent, broadband, electrically pumped, THz radiation sources for use in future spectroscopic applications. In this work, we explore the feasibility of such lasers in a dual-comb spectroscopy configuration for the detection of multiple molecular samples in the gas phase. The lasers span approximately 180 GHz of optical bandwidth, centered at 3.4 THz, with submilliwatt total optical power. One of the main advantages of dual-comb spectroscopy is its high speed, which opens up the possibility for direct observations of chemical reaction dynamics in the terahertz spectral region. As a proof-of-concept, we recorded continuously evolving spectra from gas mixtures with 1 ms temporal resolution.

42 ENGINEERING↗
DOE OSTI2021

NENCI-2021. I. A large benchmark database of non-equilibrium non-covalent interactions emphasizing close intermolecular contacts

In this work, we present NENCI-2021, a benchmark database of ~8000 Non-Equilibirum Non-Covalent Interaction energies for a large and diverse selection of intermolecular complexes of biological and chemical relevance. To meet the growing demand for large and high-quality quantum mechanical data in the chemical sciences, NENCI-2021 starts with the 101 molecular dimers in the widely used S66 and S101 databases and extends the scope of these works by (i) including 40 cation–π and anion–π complexes, a fundamentally important class of non-covalent interactions that are found throughout nature and pose a substantial challenge to theory, and (ii) systematically sampling all 141 intermolecular potential energy surfaces (PESs) by simultaneously varying the intermolecular distance and intermolecular angle in each dimer. Designed with an emphasis on close contacts, the complexes in NENCI-2021 were generated by sampling seven intermolecular distances along each PES (ranging from 0.7× to 1.1× the equilibrium separation) and nine intermolecular angles per distance (five for each ion–π complex), yielding an extensive database of 7763 benchmark intermolecular interaction energies (E int ) obtained at the coupled-cluster with singles, doubles, and perturbative triples/complete basis set [CCSD(T)/CBS] level of theory. The E int values in NENCI-2021 span a total of 225.3 kcal/mol, ranging from -38.5 to +186.8 kcal/mol, with a mean (median) E int value of -1.06 kcal/mol (-2.39 kcal/mol). In addition, a wide range of intermolecular atom-pair distances are also present in NENCI-2021, where close intermolecular contacts involving atoms that are located within the so-called van der Waals envelope are prevalent—these interactions, in particular, pose an enormous challenge for molecular modeling and are observed in many important chemical and biological systems. A detailed symmetry-adapted perturbation theory (SAPT)- based energy decomposition analysis also confirms the diverse and comprehensive nature of the intermolecular binding motifs present in NENCI-2021, which now includes a significant number of primarily induction-bound dimers (e.g., cation–π complexes). NENCI-2021 thus spans all regions of the SAPT ternary diagram, thereby warranting a new four-category classification scheme that includes complexes primarily bound by electrostatics (3499), induction (700), dispersion (1372), or mixtures thereof (2192). A critical error analysis performed on a representative set of intermolecular complexes in NENCI-2021 demonstrates that the E int values provided herein have an average error of ±0.1 kcal/mol, even for complexes with strongly repulsive E int values, and maximum errors of ±0.2–0.3 kcal/mol (i.e., ~±1.0 kJ/mol) for the most challenging cases. For these reasons, we expect that NENCI-2021 will play an important role in the testing, training, and development of next-generation classical and polarizable force fields, density functional theory approximations, wavefunction theory methods, and machine learning based intra- and inter-molecular potentials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

More buck-per-shot: Why learning trumps mitigation in noisy quantum sensing

Quantum sensing is one of the most promising applications for quantum technologies. However, reaching the ultimate sensitivities enabled by the laws of quantum mechanics can be a challenging task in realistic scenarios where noise is present. While several strategies have been proposed to deal with the detrimental effects of noise, these come at the cost of an extra shot budget. Given that shots are a precious resource for sensing – as infinite measurements could lead to infinite precision – care must be taken to truly guarantee that any shot not being used for sensing is actually leading to some metrological improvement. In this work, we study whether investing shots in error-mitigation, inference techniques, or combinations thereof, can improve the sensitivity of a noisy quantum sensor on a (shot) budget. We present a detailed bias–variance error analysis for various sensing protocols. Our results show that the costs of zero-noise extrapolation techniques outweigh their benefits. We also find that pre-characterizing a quantum sensor via inference techniques leads to the best performance, under the assumption that the sensor is sufficiently stable.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗