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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Josephson Traveling Wave Parametric Amplifiers to Enable Future Neutrino Mass Measurements

Technologies for “quantum sensing" have the promise of dramatically improved sensitivity in some experiments that explore nuclear physics at the boundaries of our current knowledge. In particular, the Josephson Traveling Wave Parametric Amplifier (JTWPA) could enable future experiments such as Project 8 that use Cyclotron Radiation Emission Spectroscopy (CRES) to measure the absolute neutrino mass scale in tritium beta decays. CRES consists of detecting a signal of approximately one femtowatt of power radiated by electrons trapped in a 1-T magnetic field. The frequency of that radiation, around 27 GHz for a tritium endpoint electron, is inversely proportional to the electron energy. One of the significant challenges of a CRES experiment is the detection of minimal signal power levels. State-of-the-art transistor-based technologies will not likely achieve sufficient signal-to-noise ratio (SNR) in a competitive neutrino mass experiment. However, emerging quantum technologies have been demonstrated to operate at the fundamental lower limits of noise permitted by the laws of quantum mechanics. Of those technologies, JTWPAs appear to satisfy future performance needs with speed gain, bandwidth, and very low noise. We propose to bring together a group of neutrino physicists, experts in the needs of future neutrino mass measurements, with leaders in the field of quantum information science and JTWPA technology to develop JTWPAs appropriate for neutrino mass measurements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2004

Thermal and Quantum Mechanical Noise of a Superfluid Gyroscope

A potential application of a superfluid gyroscope is for real-time measurements of the small variations in the rotational speed of the Earth, the Moon, and Mars. Such rotational jitter, if not measured and corrected for, will be a limiting factor on the resolution potential of a GPS system. This limitation will prevent many automation concepts in navigation, construction, and biomedical examination from being realized. We present the calculation of thermal and quantum-mechanical phase noise across the Josephson junction of a superfluid gyroscope. This allows us to derive the fundamental limits on the performance of a superfluid gyroscope. We show that the fundamental limit on real-time GPS due to rotational jitter can be reduced to well below 1 millimeter/day. Other limitations and their potential mitigation will also be discussed.

Chui, Talso↗
NASA NTRS1992

Receiver performance of laser ranging measurements between the Lunar Observer and a subsatellite for lunar gravity studies

The optimal receiver for a direct detection laser ranging system for slow Doppler frequency shift measurement is shown to consist of a phase tracking loop which can be implemented approximately as a phase lock loop with a 2nd or 3rd order loop filter. The laser transmitter consists of an AlGaAs laser diode at a wavelength of about 800 nm and is intensity modulated by a sinewave. The receiver performance is shown to be limited mainly by the preamplifier thermal noise when a silicon avalanche photodiode is used. A high speed microchannel plate photomultiplier tube is shown to outperform a silicon APD despite its relatively low quantum efficiency at wavelengths near 800 nm. The maximum range between the Lunar Observer and the subsatellite for lunar gravity studies is shown to be about 620 km when using a state-of-the-art silicon APD and about 1000 km when using a microchannel plate photomultiplier tube in order to achieve a relative velocity measurement accuracy of 1 millimeter per second. Other parameters such as the receiver time base jitter and drift also limit performance and have to be considered in the design of an actual system.

Davidson, Frederic M.↗
NASA NTRS2000

Highly Parallel Computing Architectures by using Arrays of Quantum-dot Cellular Automata (QCA): Opportunities, Challenges, and Recent Results

There has been significant improvement in the performance of VLSI devices, in terms of size, power consumption, and speed, in recent years and this trend may also continue for some near future. However, it is a well known fact that there are major obstacles, i.e., physical limitation of feature size reduction and ever increasing cost of foundry, that would prevent the long term continuation of this trend. This has motivated the exploration of some fundamentally new technologies that are not dependent on the conventional feature size approach. Such technologies are expected to enable scaling to continue to the ultimate level, i.e., molecular and atomistic size. Quantum computing, quantum dot-based computing, DNA based computing, biologically inspired computing, etc., are examples of such new technologies. In particular, quantum-dots based computing by using Quantum-dot Cellular Automata (QCA) has recently been intensely investigated as a promising new technology capable of offering significant improvement over conventional VLSI in terms of reduction of feature size (and hence increase in integration level), reduction of power consumption, and increase of switching speed. Quantum dot-based computing and memory in general and QCA specifically, are intriguing to NASA due to their high packing density (10(exp 11) - 10(exp 12) per square cm ) and low power consumption (no transfer of current) and potentially higher radiation tolerant. Under Revolutionary Computing Technology (RTC) Program at the NASA/JPL Center for Integrated Space Microelectronics (CISM), we have been investigating the potential applications of QCA for the space program. To this end, exploiting the intrinsic features of QCA, we have designed novel QCA-based circuits for co-planner (i.e., single layer) and compact implementation of a class of data permutation matrices, a class of interconnection networks, and a bit-serial processor. Building upon these circuits, we have developed novel algorithms and QCA-based architectures for highly parallel and systolic computation of signal/image processing applications, such as FFT and Wavelet and Wlash-Hadamard Transforms.

Fijany, Amir↗
DOE OSTI2021

Quantum-Based Molecular Dynamics Simulations Using Tensor Cores

Tensor cores, along with tensor processing units, represent a new form of hardware acceleration specifically designed for deep neural network calculations in artificial intelligence applications. Tensor cores provide extraordinary computational speed and energy efficiency but with the caveat that they were designed for tensor contractions (matrix–matrix multiplications) using only low-precision floating-point operations. Despite this perceived limitation, we demonstrate how tensor cores can be applied with high efficiency to the challenging and numerically sensitive problem of quantum-based Born–Oppenheimer molecular dynamics, which requires highly accurate electronic structure optimizations and conservative force evaluations. The interatomic forces are calculated on-the-fly from an electronic structure that is obtained from a generalized deep neural network, where the computational structure naturally takes advantage of the exceptional processing power of the tensor cores and allows for high performance in excess of 100 Tflops on a single Nvidia A100 GPU. Stable molecular dynamics trajectories are generated using the framework of extended Lagrangian Born–Oppenheimer molecular dynamics, which combines computational efficiency with long-term stability, even when using approximate charge relaxations and force evaluations that are limited in accuracy by the numerically noisy conditions caused by the low-precision tensor core floating-point operations. A canonical ensemble simulation scheme is also presented, where the additional numerical noise in the calculated forces is absorbed into a Langevin-like dynamics.

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

Scintillation of colloidal nanocrystals

Scintillators are materials that convert ionizing radiation in the form of particles or photons into low-energy photons in the ultraviolet to near-infrared spectral range. Here, this work reviews efforts to use colloidal nanocrystals as scintillator materials. To date, research on colloidal nanocrystals as scintillators has focused on doped phosphor systems, quantum dots and related structures, and perovskite-based nanocrystals. Among various material classes and forms, colloidal semiconductor nanocrystals stand out thanks to their appealing fluorescence properties, yet understanding of their radioluminescence and cathodoluminescence is incomplete. This review discusses fundamental limits and material design challenges toward achieving high brightness, fast speed, and durable scintillator performance with nanocrystal scintillators. First, this review describes the basic principles and efficiency limitations of scintillation, particularly the large influence of multiple exciton generation in many nanocrystal species, as well as performance metrics. Second, methods for measurement are described. Third, we review the results of the main classes of nanocrystal scintillators, including quantum dots and related particles, perovskites, and doped-phosphor particles. Fourth, scintillation imaging is discussed in terms of the relevant performance metrics and results obtained using nanocrystal materials. Finally, we note the strengths and weaknesses of nanocrystal scintillators and discuss potential areas of further development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Let Each Quantum Bit Choose Its Basis Gates

Near-term quantum computers are primarily limited by errors in quantum operations (or gates) between two quantum bits (or qubits). A physical machine typically provides a set of basis gates that include primitive 2-qubit (2Q) and 1-qubit (1Q) gates that can be implemented in a given technology. 2Q entangling gates, coupled with some 1Q gates, allow for universal quantum computation. In superconducting technologies, the current state of the art is to implement the same 2Q gate between every pair of qubits (typically an XX-or XY-type gate). This strict hardware uniformity requirement for 2Q gates in a large quantum computer has made scaling up a time and resource-intensive endeavor in the lab. We propose a radical idea – allow the 2Q basis gate(s) to differ between every pair of qubits, selecting the best entangling gates that can be calibrated between given pairs of qubits. This work aims to give quantum scientists the ability to run meaningful algorithms with qubit systems that are not perfectly uniform. Scientists will also be able to use a much broader variety of novel 2Q gates for quantum computing. We develop a theoretical framework for identifying good 2Q basis gates on “nonstandard” Cartan trajectories that deviate from “standard” trajectories like XX. We then introduce practical methods for calibration and compilation with nonstandard 2Q gates, and discuss possible ways to improve the compilation. To demonstrate our methods in a case study, we simulated both standard XY-type trajectories and faster, nonstandard trajectories using an entangling gate architecture with far-detuned transmon qubits. We identify efficient 2Q basis gates on these nonstandard trajectories and use them to compile a number of standard benchmark circuits such as QFT and QAOA. Furthermore, our results demonstrate an 8x improvement over the baseline 2Q gates with respect to speed and coherence-limited gate fidelity.

quantum computing↗
NASA NTRS2003

Block QCA Fault-Tolerant Logic Gates

Suitably patterned arrays (blocks) of quantum-dot cellular automata (QCA) have been proposed as fault-tolerant universal logic gates. These block QCA gates could be used to realize the potential of QCA for further miniaturization, reduction of power consumption, increase in switching speed, and increased degree of integration of very-large-scale integrated (VLSI) electronic circuits. The limitations of conventional VLSI circuitry, the basic principle of operation of QCA, and the potential advantages of QCA-based VLSI circuitry were described in several NASA Tech Briefs articles, namely Implementing Permutation Matrices by Use of Quantum Dots (NPO-20801), Vol. 25, No. 10 (October 2001), page 42; Compact Interconnection Networks Based on Quantum Dots (NPO-20855) Vol. 27, No. 1 (January 2003), page 32; Bit-Serial Adder Based on Quantum Dots (NPO-20869), Vol. 27, No. 1 (January 2003), page 35; and Hybrid VLSI/QCA Architecture for Computing FFTs (NPO-20923), which follows this article. To recapitulate the principle of operation (greatly oversimplified because of the limitation on space available for this article): A quantum-dot cellular automata contains four quantum dots positioned at or between the corners of a square cell. The cell contains two extra mobile electrons that can tunnel (in the quantummechanical sense) between neighboring dots within the cell. The Coulomb repulsion between the two electrons tends to make them occupy antipodal dots in the cell. For an isolated cell, there are two energetically equivalent arrangements (denoted polarization states) of the extra electrons. The cell polarization is used to encode binary information. Because the polarization of a nonisolated cell depends on Coulomb-repulsion interactions with neighboring cells, universal logic gates and binary wires could be constructed, in principle, by arraying QCA of suitable design in suitable patterns. Heretofore, researchers have recognized two major obstacles to realization of QCA-based logic gates: One is the need for (and the difficulty of attaining) operation of QCA circuitry at room temperature or, for that matter, at any temperature above a few Kelvins. It has been theorized that room-temperature operation could be made possible by constructing QCA as molecular-scale devices. However, in approaching the lower limit of miniaturization at the molecular level, it becomes increasingly imperative to overcome the second major obstacle, which is the need for (and the difficulty of attaining) high precision in the alignments of adjacent QCA in order to ensure the correct interactions among the quantum dots.

Firjany, Amir↗
DOE OSTI2023

Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning

Knowledge of the physical properties of ionic liquids (ILs), such as the surface tension and speed of sound, is important for both industrial and research applications. Unfortunately, technical challenges and costs limit exhaustive experimental screening efforts of ILs for these critical properties. Previous work has demonstrated that the use of quantum-mechanics-based thermochemical property prediction tools, such as the conductor-like screening model for real solvents, when combined with machine learning (ML) approaches, may provide an alternative pathway to guide the rapid screening and design of ILs for desired physiochemical properties. However, the question of which machine-learning approaches are most appropriate remains. In the present study, we examine how different ML architectures, ranging from tree-based approaches to feed-forward artificial neural networks, perform in generating nonlinear multivariate quantitative structure–property relationship models for the prediction of the temperature- and pressure-dependent surface tension of and speed of sound in ILs over a wide range of surface tensions (16.9–76.2 mN/m) and speeds of sound (1009.7–1992 m/s). The ML models are further interrogated using the powerful interpretation method, shapley additive explanations. We find that several different ML models provide high accuracy, according to traditional statistical metrics. The decision tree-based approaches appear to be the most accurate and precise, with extreme gradient-boosting trees and gradient-boosting trees being the best performers. However, our results also indicate that the promise of using machine-learning to gain deep insights into the underlying physics driving structure–property relationships in ILs may still be somewhat premature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Dynamical constraints on RG flows and cosmology

Sum rules connecting low-energy observables to high-energy physics are an interesting way to probe the mechanism of inflation and its ultraviolet origin. Unfortunately, such sum rules have proven difficult to study in a cosmological setting. Motivated by this problem, we investigate a precise analogue of inflation in anti-de Sitter spacetime, where it becomes dual to a slow renormalization group flow in the boundary quantum field theory. This dual description provides a firm footing for exploring the constraints of unitarity, analyticity, and causality on the bulk effective field theory. We derive a sum rule that constrains the bulk coupling constants in this theory. In the bulk, the sum rule is related to the speed of radial propagation, while on the boundary, it governs the spreading of nonlocal operators. When the spreading speed approaches the speed of light, the sum rule is saturated, suggesting that the theory becomes free in this limit. We also discuss whether similar results apply to inflation, where an analogous sum rule exists for the propagation speed of inflationary fluctuations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Strictly non-adiabatic quantum control of the acetylene dication using an infrared field

We demonstrate the existence of a strictly non-adiabatic control pathway in deprotonation of the acetylene dication. This pathway is identified experimentally by measuring a kinetic energy shift in an ion coincidence experiment. We use a time dependent Schrödinger equation simulation to identify which properties most strongly affect our control. We find that resonant control around conical intersections is limited by the speed of non-adiabatic dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

SYMBA: symbolic computation of squared amplitudes in high energy physics with machine learning

The cross section is one of the most important physical quantities in high-energy physics and the most time consuming to compute. While machine learning has proven to be highly successful in numerical calculations in high-energy physics, analytical calculations using machine learning are still in their infancy. In this work, we use a sequence-to-sequence model, specifically, a transformer, to compute a key element of the cross section calculation, namely, the squared amplitude of an interaction. We show that a transformer model is able to predict correctly 97.6% and 99% of squared amplitudes of quantum chromodynamics and quantum electrodynamics processes, respectively, at a speed that is up to orders of magnitude faster than current symbolic computation frameworks. We discuss the performance of the current model, its limitations and possible future directions for this work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Quantum maximum entropy closure for small flavor coherence

Quantum angular moment transport schemes are an important avenue toward describing neutrino flavor mixing phenomena in dense astrophysical environments such as supernovae and merging neutron stars. Successful implementation will require new closure relations that go beyond those used in classical transport. In this paper, we derive the first analytic expression for a quantum M1 closure, valid in the limit of small flavor coherence, based on the maximum entropy principle. We verify that the resulting closure relation has the appropriate limits and characteristic speeds in the diffusive and free-streaming regimes. We then use this new closure in a moment linear stability analysis to search for fast flavor instabilities in a binary neutron star merger simulation and find better results as compared with previously designed, ad hoc , semiclassical closures.

astrophysical & cosmological simulations↗
DOE OSTI2021

A Hybrid System for Learning Classical Data in Quantum States

Deep neural network-powered artificial intelligence has rapidly changed our daily life with various applications. However, as one of the essential steps of deep neural networks, training a heavily-weighted network requires a tremendous amount of computing resources. Especially in the post Moore’s Law era, the limit of semiconductor fabrication technology has restricted the development of learning algorithms to cope with the increasing high intensity training data. Meanwhile, quantum computing has exhibited its significant potential in terms of speeding up the traditionally compute-intensive workloads. For example, Google illustrates quantum supremacy by completing a sampling calculation task in 200 seconds, which is otherwise impracticable on the world’s largest supercomputers. To this end, quantum-based learning becomes an area of interest, with the promising of a quantum speedup. In this paper, we propose GenQu, a hybrid and general-purpose quantum framework for learning classical data through quantum states. We evaluate GenQu with real datasets and conduct experiments on both simulations and real quantum computer IBM-Q. Our evaluation demonstrates that, comparing with classical solutions, the proposed models running on GenQu framework achieve similar accuracy with a much smaller number of qubits, while significantly reducing the parameter size by up to 95.86% and converging speedup by 66.67% faster.

Stein, Samuel A.↗
NASA NTRS1992

Lidar receivers for picosecond remote sensing

The lidars of picosecond resolution are an attractive tool for remote probing of some highly dynamic objects like sea subsurface waters, small-scale turbulences in the atmosphere, etc. The picosecond lasers are suitable illuminating sources, but the main restrictions are due to the lack of proper receiving methods, combining the both high temporal and amplitude resolution, good sensitivity, short integration time, and wide dynamic range. The methods for short pulse measurements are not suitable for picosecond lidars, operating at low level, with highly dynamic signals. The streak-cameras are of high cost, lower sensitivity, and lower dynamic range (approximately 10(exp 3)). Because of the background, the single quantum regime in photomultipliers (PMT) is ineffective. The sampling of highly dynamic optical signals with resolution less than or equal to 1ns is a serious problem, limiting the application of the high speed PMT-MCP (microchannel plate) in the picosecond lidar systems. The goal of this work is to describe the use of a new photodetection technique which combines the picosecond resolution with the high amplitude resolution, dynamic range, and sensitivity.

Stoyanov, D. V.↗
DOE OSTI2021

Quantum Foundations of Classical Reversible Computing

The reversible computation paradigm aims to provide a new foundation for general classical digital computing that is capable of circumventing the thermodynamic limits to the energy efficiency of the conventional, non-reversible digital paradigm. However, to date, the essential rationale for, and analysis of, classical reversible computing (RC) has not yet been expressed in terms that leverage the modern formal methods of non-equilibrium quantum thermodynamics (NEQT). In this paper, we begin developing an NEQT-based foundation for the physics of reversible computing. We use the framework of Gorini-Kossakowski-Sudarshan-Lindblad dynamics (a.k.a. Lindbladians) with multiple asymptotic states, incorporating recent results from resource theory, full counting statistics and stochastic thermodynamics. Important conclusions include that, as expected: (1) Landauer’s Principle indeed sets a strict lower bound on entropy generation in traditional non-reversible architectures for deterministic computing machines when we account for the loss of correlations; and (2) implementations of the alternative reversible computation paradigm can potentially avoid such losses, and thereby circumvent the Landauer limit, potentially allowing the efficiency of future digital computing technologies to continue improving indefinitely. We also outline a research plan for identifying the fundamental minimum energy dissipation of reversible computing machines as a function of speed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Quantum interference between the optical Stark effect and resonant harmonic generation in WS 2

An applied field can modulate optical signals by resonance shifting via the Stark effect. The optical Stark effect uses ultrafast light in the transparency region of a material to shift resonances with speeds limited by the pulse duration or system coherence. In this Rapid Communication, we investigate the optical Stark effect in resonant optical harmonic generation using the A exciton transition of WS 2 . Multidimensional pump-harmonic-probe measurements, in which the probe is second- or third-harmonic emission, reveal not only large Stark shifts that are commensurate with the large optical susceptibilities common to WS 2 excitons, but also behaviors more complex than simple optical Stark effect treatments predict. We show how another manifestation of the Stark effect, brought forth by coherent photon exchange between the pump and harmonic generation fundamental fields, can strongly enhance or suppress harmonic generation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗