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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Broken bridges: a counter-example of the ER=EPR conjecture

In this paper, we provide a counter-example to the ER=EPR conjecture. In an anti-de Sitter space, we construct a pair of maximally entangled but separated black holes. Due to the vacuum decay of the anti-de Sitter background toward a deeper vacuum, these two parts can be trapped by bubbles. If these bubbles are reasonably large, then within the scrambling time, there should appear an Einstein-Rosen bridge between the two black holes. Now by tracing more details on the bubble dynamics, one can identify parameters such that one of the two bubbles either monotonically shrinks or expands. Because of the change of vacuum energy, one side of the black hole would evaporate completely. Due to the shrinking of the apparent horizon, a signal of one side of the Einstein-Rosen bridge can be viewed from the opposite side. We analytically and numerically demonstrate that within a reasonable semi-classical parameter regime, such process can happen. Bubbles are a non-perturbative effect, which is the crucial reason that allows the transmission of information between the two black holes through the Einstein-Rosen bridge, even though the probability is highly suppressed. Therefore, the ER=EPR conjecture cannot be generic in its present form and its validity maybe restricted.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

SRF Levitation and Trapping of Nanoparticles Experiment

The levitation and trapping of micro particles using lasers is not a new accomplishment and has been applied in containerless processing and spectroscopy of microparticles. Recent developments in the field of optomechanics have achieved levitating nanoparticles such that quantum phenomena was observed which would have relevance in quantum science and technology. This two-year project sought to levitate and trap mesoscopic particles using radio frequency (RF) fields in a resonating superconducting RF (SRF) cavity. By exploiting the intrinsic characteristics of an SRF resonating cavity, the project was to explore overcoming a major limit faced by current laser trapping techniques. The goal was to establish a foundation to enable the observation of quantum phenomena of an isolated mechanical oscillator interacting with microwave fields. If successful the experiment could establish its ground-breaking relevance to quantum information science and technology.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Programmable photonic integrated meshes for modular generation of optical entanglement links

Abstract Large-scale generation of quantum entanglement between individually controllable qubits is at the core of quantum computing, communications, and sensing. Modular architectures of remotely-connected quantum technologies have been proposed for a variety of physical qubits, with demonstrations reported in atomic and all-photonic systems. However, an open challenge in these architectures lies in constructing high-speed and high-fidelity reconfigurable photonic networks for optically-heralded entanglement among target qubits. Here we introduce a programmable photonic integrated circuit (PIC), realized in a piezo-actuated silicon nitride (SiN)-in-oxide CMOS-compatible process, that implements an N × N Mach–Zehnder mesh (MZM) capable of high-speed execution of linear optical transformations. The visible-spectrum photonic integrated mesh is programmed to generate optical connectivity on up to N = 8 inputs for a range of optically-heralded entanglement protocols. In particular, we experimentally demonstrated optical connections between 16 independent pairwise mode couplings through the MZM, with optical transformation fidelities averaging 0.991 ± 0.0063. The PIC’s reconfigurable optical connectivity suffices for the production of 8-qubit resource states as building blocks of larger topological cluster states for quantum computing. Our programmable PIC platform enables the fast and scalable optical switching technology necessary for network-based quantum information processors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

Characterization of rhenium compounds obtained by electrochemical synthesis after aging process

The proper identification of the molecular nature of the aged rhenium compound obtained by means of electrodeposition from an alkaline aqueous electrolyte was determined. Chemical, structural and vibrational experimental characterization of the aged Re compound showed agreement with quantum-computations, thereby allowing the unambiguous identification of the Re compound as H(ReO{sub 4})H{sub 2}O. - Graphical abstract: Rhenium oxides were electrodeposited on a copper surface and after environmental aging was formed the H(ReO{sub 4})H{sub 2}O compound. The characterization of the synthesized material was made through the comparison of experimental evidence with quantum mechanical computations carried out by means of density functional theory (DFT). - Highlights: • Aged rhenium compound obtained by means of electrodeposition was studied. • The study was made by combining experimental and DFT-computational information. • The aged electrodeposited material is consistent with the H(ReO{sub 4})H{sub 2}O compound.

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

Architectures and random properties of symplectic quantum circuits

Parametrized and random unitary (or orthogonal) n-qubit circuits play a central role in quantum information. As such, one could naturally assume that circuits implementing symplectic transformations would attract similar attention. However, this is not the case, as $\mathbb{SP}(d/2)$—the group of d × d unitary symplectic matrices—has thus far been overlooked. In this work, we aim at starting to fill this gap. We begin by presenting a universal set of generators $\mathcal{G}$ for the symplectic algebra $\mathfrak{sp}(d/2)$, consisting of one- and two-qubit Pauli operators acting on neighboring sites in a one-dimensional lattice. Here, we uncover two critical differences between such set, and equivalent ones for unitary and orthogonal circuits. Namely, we find that the operators in $\mathcal{G}$ cannot generate arbitrary local symplectic unitaries and that they are not translationally invariant. We then review the Schur–Weyl duality between the symplectic group and the Brauer algebra, and use tools from Weingarten calculus to prove that Pauli measurements at the output of Haar random symplectic circuits can converge to Gaussian processes. As a by-product, such analysis provides us with concentration bounds for Pauli measurements in circuits that form t-designs over $\mathbb{SP}(d/2)$. To finish, we present tensor-network tools to analyze shallow random symplectic circuits, and we use these to numerically show that computational-basis measurements anti-concentrate at logarithmic depth.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Challenges and transformative opportunities in superconductor vortex physics

In superconductors, the motion of vortices introduces unwanted dissipation that is disruptive to applications. Fortunately, material defects can immobilize vortices, acting as vortex pinning centers, which engenders dramatic improvements in superconductor material properties and device operation. This has motivated decades of research into developing methods of tailoring the disorder landscape in superconductors to increase the strength of vortex pinning. Yet, efficacious materials engineering still eludes us. The electromagnetic properties of real (disordered) superconducting materials cannot yet be reliably predicted, such that designing superconductors for applications remains a largely inefficient process of trial and error. This is ultimately due to large gaps in our knowledge of vortex dynamics: the field is challenged by the extremely complex interplay between vortex elasticity, vortex-vortex interactions, and material disorder. Here, we review obstacles and recent successes in understanding and controlling vortex dynamics in superconducting materials and devices. We further identify major open questions and discuss opportunities for transformative research in the field. This includes improving our understanding of vortex creep, determining and reaching the ceiling for the critical current, advanced microscopy to garner accurate structure-property relationships, frontiers in predictive simulations and the benefits of artificial intelligence, as well as controlling and exploiting vortices in quantum information applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Benchmarking Information Scrambling

Information scrambling refers to the rapid spreading of initially localized information over an entire system, via the generation of global entanglement. This effect is usually detected by measuring a temporal decay of the out-of-time order correlators. However, in experiments, decays of these correlators suffer from fake positive signals from various sources, e.g., decoherence due to inevitable couplings to the environment, or errors that cause mismatches between the purported forward and backward evolutions. Here, in this Letter, we provide a simple and robust approach to single out the effect of genuine scrambling. This allows us to benchmark the scrambling process by quantifying the degree of the scrambling from the noisy backgrounds. We also demonstrate our protocol with simulations on IBM cloud-based quantum computers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Characterizing Quantum Classifier Utility in Natural Language Processing Workflows

Quantum Natural Language Processing (QNLP) develops natural language processing (NLP) models for deployment on quantum computers. We explore feature and data prototype selection techniques to address challenges posed by encoding high dimensional features. Our study builds quantum circuit classifiers that includes classical feature pre-processing, quantum embedding and quantum model training. The quantum models are built on 4 or 6 qubits and the quantum neural network (QNN) uses the established bricklayer design. We compare the dependence of model performance (in terms of accuracy and F1 scores) on feature length, embedding gates and parameterized unitary design. We compare the performance of quantum machine learning models to classical convolution neural network model (CNN) on binary and multi-class classification tasks using two datasets of synthetic features and labels. The first is the ECP-CANDLE P3B3 dataset a corpus of synthetically generated cancer pathology reports. The second dataset is extracted from well-known benchmark dataset (MADELON) - features are generated with a combination of informative, repeated and uninformative features. Both datasets are used for binary classification and multi-class classification with 3 classes. We observe robust, accurate performance from all models on the binary classification tasks, but multiclass classification is a challenge for the quantum models-there is a notable decrease in accuracy when using 3 classes. Overall the performance is comparable in terms of recall and accuracy between QNNs and CNNs, even with large datasets. These results provide a point of comparison between quantum and classical models on real-world datasets.

Hamilton, Kathleen↗
DOE OSTI2018

Optimal structure and parameter learning of Ising models

Reconstruction of the structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning. The focus of the research community shifted toward developing universal reconstruction algorithms that are both computationally efficient and require the minimal amount of expensive data. Here, we introduce a new method, interaction screening, which accurately estimates model parameters using local optimization problems. The algorithm provably achieves perfect graph structure recovery with an information-theoretically optimal number of samples, notably in the low-temperature regime, which is known to be the hardest for learning. Here, the efficacy of interaction screening is assessed through extensive numerical tests on synthetic Ising models of various topologies with different types of interactions, as well as on real data produced by a D-Wave quantum computer. Finally, this study shows that the interaction screening method is an exact, tractable, and optimal technique that universally solves the inverse Ising problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Bias-pulsed atomic layer etching of 4H-silicon carbide producing subangstrom surface roughness

A new approach to atomic layer etching (ALE) has been demonstrated, and its application to 4H-SiC is reported here. By pulsing only the DC bias for an Ar/Cl2 inductively coupled plasma-reactive ion etching system, the etch cycle duration is reduced by more than an order of magnitude relative to conventional ALE processes. Gas flows are not changed throughout the ALE process. With this process protocol, we achieved an etch rate of 2.48±0.09 Å/cycle with 6 s cycles, an RMS surface roughness (Rq) of 0.83±0.08 Å, and an ALE synergy value of S = 99%. The parameters explored within this ALE process demonstrate effective subangstrom smoothening of 4H-SiC surfaces and is well-suited for a variety of classical and quantum device nanofabrication.

42 ENGINEERING↗
DOE OSTI2026

Ultrafast Spectroscopy in Chemistry

Ultrafast spectroscopy has become an indispensable investigation tool in numerous areas of chemical research encompassing purely synthetic to semiconductor material domains. Harnessing light on its femtosecond time scales offers a glimpse into submolecular processes that direct reaction pathways, energy transfer, charge separation, and many other fundamental chemical phenomena. In this Perspective, we highlight select examples that introduce how ultrafast spectroscopy became a pillar for modern chemical science. We emphasize the achievements of conventional pump–probe spectroscopy by showing its contribution to disentangling peculiar mechanisms of carrier relaxation in semiconductor nanocrystals, charge separation in organic photovoltaic devices, and photoredox catalyst activation. Our examples show that even traditional pump–probe experiments can provide extensive insights that go beyond the resolution of ultrafast time scales if combined with a thorough preliminary assessment of the target system. We conclude with suggestions for how ultrafast spectroscopy in tandem with chemical science can embark on advancing practical quantum information research.

Excitons↗
DOE OSTI2024

Subnanometer Scale Mapping of Hydrogen Doping in Vanadium Dioxide

Hydrogen donor doping of correlated electron systems such as vanadium dioxide (VO 2 ) profoundly modifies the ground state properties. The electrical behavior of H x VO 2 is strongly dependent on the hydrogen concentration; hence, atomic scale control of the doping process is necessary. It is however a nontrivial problem to quantitatively probe the hydrogen distribution in a solid matrix. As hydrogen transfers its sole electron to the material, the ionization mechanism is suppressed. Here in this study, a methodology mapping the doping distribution at subnanometer length scale is demonstrated across a H x VO 2 thin film focusing on the oxygen-hydrogen bonds using electron energy loss spectroscopy (EELS) coupled with first-principles EELS calculations. The hydrogen distribution was revealed to be nonuniform along the growth direction and between different VO 2 grains, calling for intricate hydrogenation mechanisms. Our study points to a powerful approach to quantitatively map dopant distribution in quantum materials relevant to energy and information science

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Operator Relaxation and the Optimal Depth of Classical Shadows

Classical shadows are a powerful method for learning many properties of quantum states in a sample-efficient manner, by making use of randomized measurements. Here we study the sample complexity of learning the expectation value of Pauli operators via “shallow shadows,” a recently proposed version of classical shadows in which the randomization step is effected by a local unitary circuit of variable depth t. Here we show that the shadow norm (the quantity controlling the sample complexity) is expressed in terms of properties of the Heisenberg time evolution of operators under the randomizing (“twirling”) circuit—namely the evolution of the weight distribution characterizing the number of sites on which an operator acts nontrivially. For spatially contiguous Pauli operators of weight k, this entails a competition between two processes: operator spreading (whereby the support of an operator grows over time, increasing its weight) and operator relaxation (whereby the bulk of the operator develops an equilibrium density of identity operators, decreasing its weight). From this simple picture we derive (i) an upper bound on the shadow norm which, for depth t~log⁡(k), guarantees an exponential gain in sample complexity over the t=0 protocol in any spatial dimension, and (ii) quantitative results in one dimension within a mean-field approximation, including a universal subleading correction to the optimal depth, found to be in excellent agreement with infinite matrix product state numerical simulations. Our Letter connects fundamental ideas in quantum many-body dynamics to applications in quantum information science, and paves the way to highly optimized protocols for learning different properties of quantum states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Visualizing the strong field–induced molecular breakup of C 60 via x-ray diffraction

Laser-driven dynamics in polyatomic molecules poses a complex many-body problem. Understanding intense light-matter interaction is crucial for steering intramolecular quantum dynamical processes. Here, we record time-resolved x-ray diffraction images of C 60 molecules during and after their interaction with intense near-infrared fields, giving direct access to structural changes of the molecules and their fragmentation in real time. Tuning the intensity of the excitation pulses, we uncover a transition from a weak-field regime of excited but stable molecules to a high-field regime dominated by Coulomb explosion. In the transition region, the molecules expand by up to 50% of their initial size within just 140 fs, with major fragmentation only setting in afterward. This work demonstrates that x-ray diffractive imaging is capable of retrieving time-resolved structural information of large molecules reshaped by intense laser fields. Laser-driven fragmentation is a first step toward observing molecular processes modified by laser fields of increasing intensity.

Schnorr, Kirsten [Paul Scherrer Inst. (PSI), Villi↗
DOE OSTI2020

In-situ measurement of pyrolysis and combustion gases from biomass burning using swept wavelength external cavity quantum cascade lasers

Broadband high-speed absorption spectroscopy using swept-wavelength external cavity quantum cascade lasers (ECQCLs) is applied to measure multiple pyrolysis and combustion gases in biomass burning experiments. Two broadly-tunable swept-ECQCL systems were used, with the first tuned over a range of 2089-2262 cm -1 (4.42 – 4.79 µm) to measure spectra of CO 2 , H 2 O, and CO. The second was tuned over a range of 920-1150 cm -1 (8.70 – 10.9 µm) to measure spectra of ammonia (NH 3 ), ethene (C 2 H 4 ), and methanol (MeOH). Absorption spectra were measured continuously at a 100 Hz rate throughout the burn process, including inhomogeneous flame regions, and analyzed to determine time-resolved gas concentrations and temperature. The results provide in-situ, dynamic information regarding gas-phase species as they are generated, close to the biomass fuel source.

09 BIOMASS FUELS↗
DOE OSTI2026

Toward a microscopic picture of hadronization and multi-parton processes

This project advanced the understanding of how quarks and gluons produced in high-energy collisions transform into the hadrons observed in particle detectors, a fundamental process known as quantum chromodynamics (QCD) hadronization. By combining theoretical calculations, quantum simulation methods, and modern AI techniques, the research developed new tools to study multi-parton dynamics and nonperturbative effects that are essential for interpreting data from current and future nuclear physics experiments. Key outcomes include new theoretical frameworks for jet and hadron measurements, pioneering quantum simulation algorithms for real-time dynamics in field theories, and the development of advanced machine-learning models, such as diffusion models and explainable classifiers, to simulate and analyze collider events. These results are directly relevant to experiments at Jefferson Lab, Brookhaven National Laboratory, and the future Electron-Ion Collider, and they also have a broader impact in areas such as quantum information science and data-driven modeling of complex systems. The project supported the training of graduate students and postdoctoral fellows and contributed to the broader scientific community through publications, workshops, and collaborative activities. Overall, this work provides new insights into the microscopic mechanisms of hadron formation and establishes a foundation for future studies at the intersection of nuclear physics, artificial intelligence, and quantum computing.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Thermo-mechanical simulation of liquid-supported stretch blow molding

Stretch blow molding is the well-established plastics forming method to produce Polyehtylene therephtalate (PET) bottles. An injection molded preform is heated up above the PET glass transition temperature (Tg∼85°C) and subsequently inflated by pressurized air into a closed cavity. In the follow-up filling process, the resulting bottle is filled with the final product. A recently developed modification of the process combines the blowing and filling stages by directly using the final liquid product to inflate the preform. In a previously published paper, a mechanical simulation and successful evaluation of this liquid-driven stretch blow molding process was presented. In this way, a realistic process parameter dependent simulation of the preform deformation throughout the forming process was enabled, whereas the preform temperature evolution during forming was neglected. However, the formability of the preform is highly reduced when the temperature sinks below Tg during forming. Experimental investigations show temperature-induced failure cases due to the fast heat transfer between hot preform and cold liquid. Therefore, in this paper, a process dependent simulation of the temperature evolution during processing to avoid preform failure is presented. For this purpose, the previously developed mechanical model is used to extract the time dependent thickness evolution. This information serves as input for the heat transfer simulation. The required material parameters are calibrated from preform cooling experiments recorded with an infrared-camera. Furthermore, the high deformation ratios during processing lead to strain induced crystallization. This exothermal reaction is included into the simulation by extracting data from preform measurements at different stages of deformation via Differential Scanning Calorimetry (DSC). Finally, the thermal simulation model is evaluated by free forming experiments, recorded by a high-speed infrared camera.

36 MATERIALS SCIENCE↗
DOE OSTI2017

A transformed path integral approach for solution of the Fokker–Planck equation

A novel path integral (PI) based method for solution of the Fokker–Planck equation is presented. The proposed method, termed the transformed path integral (TPI) method, utilizes a new formulation for the underlying short-time propagator to perform the evolution of the probability density function (PDF) in a transformed computational domain where a more accurate representation of the PDF can be ensured. The new formulation, based on a dynamic transformation of the original state space with the statistics of the PDF as parameters, preserves the non-negativity of the PDF and incorporates short-time properties of the underlying stochastic process. New update equations for the state PDF in a transformed space and the parameters of the transformation (including mean and covariance) that better accommodate nonlinearities in drift and non-Gaussian behavior in distributions are proposed (based on properties of the SDE). Owing to the choice of transformation considered, the proposed method maps a fixed grid in transformed space to a dynamically adaptive grid in the original state space. The TPI method, in contrast to conventional methods such as Monte Carlo simulations and fixed grid approaches, is able to better represent the distributions (especially the tail information) and better address challenges in processes with large diffusion, large drift and large concentration of PDF. Additionally, in the proposed TPI method, error bounds on the probability in the computational domain can be obtained using the Chebyshev's inequality. The benefits of the TPI method over conventional methods are illustrated through simulations of linear and nonlinear drift processes in one-dimensional and multidimensional state spaces. The effects of spatial and temporal grid resolutions as well as that of the diffusion coefficient on the error in the PDF are also characterized.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗