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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Realizing the scientific program with polarized ion beams at the future BNL Electron Ion Collider

Polarized ion beams at the Electron Ion Collider (EIC) are essential to address some of the most important open questions at the twenty-first century frontiers of understanding of the fundamental structure of matter. Here, in this work, we summarize the science case and identify polarized 2 H, 3 He, 6 Li, and 7 Li ion beams as critical technology that will enable experiments which address the most important science. Furthermore, we discuss the required ion polarimetry and spin manipulation at the EIC. The current EIC accelerator design is presented. We identify a significant research and development effort across national and international laboratories and universities that is required over about a decade to realize the polarized ion beams and estimate (based on previous experience) that it will require about 20 full-time equivalent (FTE) over 10 years (or a total of about 200 FTE-years) of personnel, including graduate students, postdoctoral researchers, technicians, and engineers. Attracting, educating, and training a new generation of physicists in experimental spin techniques will be essential for the successful realization. Artificial intelligence and machine learning are seen as having significant potential for both acceleration of research and development and amplification of discovery in the optimal realization of this unique quantum technology on a cutting-edge collider. The research and development effort is synergistic with research in atomic physics and fusion energy science.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Quantum State Transfer between Heterogeneous 3D QPUs

We present detailed analyses and microwave simulations of a quantum interconnect architecture that couples two distinct three-dimensional quantum processing units (QPUs): (i) a two-cell superconducting radio-frequency (SRF) cavity coupled to multiple transmon qubits and (ii) a waveguide-based cavity hosting a planar circuit with transmons and resonators. The modules are linked via a low-loss superconducting coaxial cable, which serves as a quantum bus. Finite-element electromagnetic simulations are performed to optimize coupling strengths and mode frequencies for high-fidelity quantum state transfer between cavities. These simulation results provide a comprehensive blueprint for the experimental realization of a flexible quantum interconnect architecture, coupling heterogeneous QPUs.

Yaker, Joey [Fermilab]↗
DOE OSTI2019

MRG8 Random Number Generator Library (MRG8) v1.0

Pseudo random number generators (PRNGs) are crucial for nu- merous applications in HPC ranging from molecular dynamics to quantum chemistry, and hydrodynamics. Our optimized implementation generates the same random number sequence as the original well-characterized MRG8. MRG8-AVX512 achieves a substantial 69% improvement compared to Intel's MKL, and MRG8-GPU shows a maximum 3.36x speedup compared to NVIDIA's cuRAND library.

Shalf, JohnM.↗
DOE OSTI2015

Engineered core/shell quantum dots as phosphors for solid-state lighting

Light-emitting diodes (LEDs) for solid state light ing (SSL) typically combine a blue or near- ultraviolet drive LED with one or more dow nconverting phosphors to produce “white” light. Further advances in both efficiency and wh ite-light quality will re quire new phosphors with narrow-band, highly efficient emission, particul arly in the red. A team led by principal investigator Dr. Victor Klim ov of Los Alamos National Labo ratory proposes to develop engineered semiconductor nanocrystal quantum dots (QDs) that combine optimal luminescent properties with long-term stability under ty pical downconverting conditions to enable new performance levels in SSL. The white LED phosphor industry is estimated to have sales of roughly $400 million in 2018 and would significantly benefit from the development of bright and narrow red-emitting QD phosphors because they woul d enable warmer whites without wasting energy by emission of light beyond the response of the human eye. In order to capitalize on the market opportunity, the LANL team is partnering with a local company called UbiQD that will facilitate US manufacturing.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Many Body Methods from Chemistry to Physics: Novel Computational Techniques for Materials-Specific Modelling: A Computational Materials Science and Chemistry Network

Understanding the behavior of interacting electrons in molecules and solids so that one can predict new superconductors, catalysts, light harvesters, energy and battery materials and optimize existing ones is the ``quantum many-body problem’’. This is one of the scientific grand challenges of the 21 st century. A complete solution to the problem has been proven to be exponentially hard, meaning that straightforward numerical approaches fail. New insights and new methods are needed to provide accurate yet feasible approximate solutions. This CMSCN project brought together chemists and physicists to combine insights from the two disciplines to develop innovative new approaches. Outcomes included the Density Matrix Embedding method, a new, computationally inexpensive and extremely accurate approach that may enable first principles treatment of superconducting and magnetic properties of strongly correlated materials, new techniques for existing methods including an Adaptively Truncated Hilbert Space approach that will vastly expand the capabilities of the dynamical mean field method, a self-energy embedding theory and a new memory-function based approach to the calculations of the behavior of driven systems. The methods developed under this project are now being applied to improve our understanding of superconductivity, to calculate novel topological properties of materials and to characterize and improve the properties of nanoscale devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

mystic : software for autonomous discovery and design under uncertainty

Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.

42 ENGINEERING↗
DOE OSTI2023

Architecture for Quantum-in-the Loop Real-Time Simulations for Designing Resilient Smart Grids

With the power grid growing more complex every day with the inclusion of new sensors and regulatory approvals that enable end-users and small local developers to participate in the grid, it is becoming challenging for conventional smart grid simulation, emulation, and testing technologies to keep up. In this work, we propose that quantum-encoded real-time simulations can be helpful under the new paradigm and operational circumstances to solve optimization problems for power grids. By leveraging the principles of quantum mechanics, the proposed quantum-in-loop (QIL) framework will enable better and faster optimization solutions based on real-world, real-time data streams, facilitating the real-time planning and operations of electrical grids that rely on millions of distributed sensors and controllers. Furthermore, QIL will allow researchers and engineers to assist utilities in designing, developing, and de-risking algorithms to optimize power grid operation and resilience by considering inputs from millions of grid-connected devices. QIL framework is being developed to have a self-limiting triage mechanism, which will help engineers and practitioners identify fundamental physical limits on quantum processors, revealing what quantum algorithms can and cannot do in utility-specific use cases and must continue to count on classical high-performance computing infrastructure.

digital real-time simulation↗
DOE OSTI2021

Adaptive Circuit Learning for Quantum Metrology

Quantum sensing is an important application of emerging quantum technologies. We explore whether a hybrid system of quantum sensors and quantum circuits can surpass the classical limit of sensing. In particular, we use optimization techniques to search for encoder and decoder circuits that scalably improve sensitivity under given application and noise characteristics. Furthermore, our approach uses a variational algorithm that can learn a quantum sensing circuit based on platform-specific control capacity, noise, and signal distribution. The quantum circuit is composed of an encoder which prepares the optimal sensing state and a decoder which gives an output distribution containing information of the signal. We optimize the full circuit to maximize the Signal-to-Noise Ratio (SNR). Furthermore, this learning algorithm can be run on real hardware scalably by using the "parameter-shift" rule which enables gradient evaluation on noisy quantum circuits, avoiding the exponential cost of quantum system simulation. We demonstrate up to 13.12x SNR improvement over existing fixed protocol (GHZ), and 3.19x Classical Fisher Information (CFI) improvement over the classical limit on 15 qubits using IBM quantum computer. More notably, our algorithm overcomes the decreasing performance of existing entanglement-based protocols with increased system sizes.

42 ENGINEERING↗
DOE OSTI2020

Tucker-1 Boolean Tensor Factorization with Quantum Annealers

Quantum annealers are an emerging computational architecture that have the potential to address some challenging computational issues that will be left unresolved as we approach the end of the Moore's Law era of computing. D-Wave quantum annealers are designed to solve a challenging set of problems - quadratic unconstrained binary optimization problems. This makes them a natural fit for solving problems with binary or Boolean variables. Here, we explore the use of a quantum annealer to solve Boolean tensor factorization. The goal of Boolean tensor factorization is to represent a high-dimensional tensor filled with Boolean values as a product of Boolean matrices and a Boolean core tensor. We show that a particular Boolean tensor factorization problem (called Tucker-1 factorization) can be decomposed into a sequence of quadratic unconstrained binary optimization problems that can be solved with a D-Wave 2000Q quantum annealer. While quantum annealers specifically and quantum computers in general are at a fairly early stage in their development, they are currently capable of solving these Boolean tensor factorization problems. Importantly, our results show that for fairly small tensors, we are frequently able to obtain an accurate (sometimes exact) factorization using quantum annealing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Learning to Predict Arbitrary Quantum Processes

We present an efficient machine-learning (ML) algorithm for predicting any unknown quantum process ℰ over 𝑛 qubits. For a wide range of distributions 𝒟 on arbitrary 𝑛-qubit states, we show that this ML algorithm can learn to predict any local property of the output from the unknown process ℰ, with a small average error over input states drawn from 𝒟. The ML algorithm is computationally efficient even when the unknown process is a quantum circuit with exponentially many gates. Our algorithm combines efficient procedures for learning properties of an unknown state and for learning a low-degree approximation to an unknown observable. The analysis hinges on proving new norm inequalities, including a quantum analogue of the classical Bohnenblust-Hille inequality, which we derive by giving an improved algorithm for optimizing local Hamiltonians. Numerical experiments on predicting quantum dynamics with evolution time up to 10 6 and system size up to 50 qubits corroborate our proof. Overall, our results highlight the potential for ML models to predict the output of complex quantum dynamics much faster than the time needed to run the process itself.

quantum computation↗
DOE OSTI2016

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

Current Deep Learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power.In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determine network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation.The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware.This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

Current Deep Learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power. In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determine network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation. The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware. This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.

Potok, Thomas E↗
DOE OSTI2015

Coherent phase control of internal conversion in pyrazine

Shaped ultrafast laser pulses were used to study and control the ionization dynamics of electronically excited pyrazine in a pump and probe experiment. For pump pulses created without feedback from the product signal, the ion growth curve (the parent ion signal as a function of pump/probe delay) was described quantitatively by the classical rate equations for internal conversion of the S{sub 2} and S{sub 1} states. Very different, non-classical behavior was observed when a genetic algorithm (GA) employing phase-only modulation was used to minimize the ion signal at some pre-determined target time, T. Two qualitatively different control mechanisms were identified for early (T < 1.5 ps) and late (T > 1.5 ps) target times. In the former case, the ion signal was largely suppressed for t < T, while for t ≫ T, the ion signal produced by the GA-optimized pulse and a transform limited (TL) pulse coalesced. In contrast, for T > 1.5 ps, the ion growth curve followed the classical rate equations for t < T, while for t ≫ T, the quantum yield for the GA-optimized pulse was much smaller than for a TL pulse. We interpret the first type of behavior as an indication that the wave packet produced by the pump laser is localized in a region of the S{sub 2} potential energy surface where the vertical ionization energy exceeds the probe photon energy, whereas the second type of behavior may be described by a reduced absorption cross section for S{sub 0} → S{sub 2} followed by incoherent decay of the excited molecules. Amplitude modulation observed in the spectrum of the shaped pulse may have contributed to the control mechanism, although this possibility is mitigated by the very small focal volume of the probe laser.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

High power, 1060-nm diode laser with an asymmetric hetero-waveguide

By introducing an asymmetric hetero-waveguide into the epitaxial structure of a diode laser, a 6.21-W output is achieved at a wavelength of 1060 nm. A different design in p- and n-confinement, based on optimisation of energy bands, is used to reduce voltage loss and meet the requirement of high power and high wall-plug efficiency. A 1060-nm diode laser with a single quantum well and asymmetric hetero-structure waveguide is fabricated and analysed. Measurement results show that the asymmetric hetero-structure waveguide can be efficiently used for reducing voltage loss and improving the confinement of injection carriers and wall-plug efficiency. (lasers)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Quantum-enhanced plasmonic sensing

Quantum resources can enhance the sensitivity of a device beyond the classical shot noise limit and, as a result, revolutionize the field of metrology through the development of quantum-enhanced sensors. In particular, plasmonic sensors, which are widely used in biological and chemical sensing applications, offer a unique opportunity to bring such an enhancement to real-life devices. Here, we use bright entangled twin beams to enhance the sensitivity of a plasmonic sensor used to measure local changes in the refractive index. We demonstrate a 56% quantum enhancement in the sensitivity of a state-of-the-art plasmonic sensor when compared with the corresponding classical configuration and a 24% quantum enhancement when compared to an optimal single-beam classical configuration. We measure sensitivities on the order of 10 –10 RIU / √Hz, nearly 5 orders of magnitude better than previous proof-of-principle implementations of quantum-enhanced plasmonic sensors. Furthermore, these results promise significant enhancements in ultratrace label-free plasmonic sensing and will find their way into areas ranging from biomedical applications to chemical detection.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Quantum Solver of Contracted Eigenvalue Equations for Scalable Molecular Simulations on Quantum Computing Devices

The accurate computation of ground and excited states of many-fermion quantum systems is one of the most consequential, contemporary challenges in the physical and computational sciences whose solution stands to benefit significantly from the advent of quantum computing devices. Existing methodologies using phase estimation or variational algorithms have potential drawbacks such as deep circuits requiring substantial error correction or non-trivial high-dimensional classical optimization. In this work, we introduce a quantum solver of contracted eigenvalue equations, the quantum analogue of classical methods for the energies and reduced density matrices of ground and excited states. The solver does not require deep circuits or difficult classical optimization and achieves an exponential speed-up over its classical counterpart. We demonstrate the algorithm though computations on both a quantum simulator and two IBM quantum processing units.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Experimental investigation of spin-orbit coupling in n-type PbTe quantum wells

The spin-orbit coupling is studied experimentally in two PbTe quantum wells by means of weak antilocalization effect. Using the Hikami-Larkin-Nagaoka model through a computational global optimization procedure, we extracted the spin-orbit and inelastic scattering times and estimated the strength of the zero field spin-splitting energy Δ{sub so}. The values of Δ{sub so} are linearly dependent on the Fermi wave vector (k{sub F}) confirming theoretical predictions of the existence of large spin-orbit coupling in IV-VI quantum wells originated from pure Rashba effect.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗