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Results for “quantum computing for simulation”

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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NASA NTRS1994

Theoretical investigation of wave-vector-dependent analytical and numerical formulations of the interband impact-ionization transition rate for electrons in bulk silicon and GaAs

The electron interband impact-ionization rate for both silicon and gallium arsenide is calculated using an ensemble Monte Carlo simulation with the expressed purpose of comparing different formulations of the interband ionization transition rate. Specifically, three different treatments of the transition rate are examined: the traditional Keldysh formula, a new k-dependent analytical formulation first derived by W. Quade, E. Scholl, and M. Rudan (1993), and a more exact, numerical method of Y. Wang and K. F. Brennan (1994). Although the completely numerical formulation contains no adjustable parameters and as such provides a very reliable result, it is highly computationally intensive. Alternatively, the Keldysh formular, although inherently simple and computationally efficient, fails to include the k dependence as well as the details of the energy band structure. The k-dependent analytical formulation of Quade and co-workers overcomes the limitations of both of these models but at the expense of some new parameterization. It is found that the k-dependent analytical method of Quade and co-workers produces very similar results to those obtained with the completely numerical model for some quantities. Specifically, both models predict that the effective threshold for impact ionization in GaAs and silicon is quite soft, that the majority of ionization events originate from the second conduction band in both materials, and that the transition rate is k dependent. Therefore, it is concluded that the k-dependent analytical model can qualitatively reproduce results similar to those obtained with the numerical model yet with far greater computational efficiency. Nevertheless, there exist some important drawbacks to the k-dependent analytical model of Quade and co-workers: These are that it does not accurately reproduce the quantum yield data for bulk silicon, it requires determination of a new parameter, related physically to the overlap intergrals of the Bloch state which can only be adjusted by comparison to experiment, and fails to account for any wave-vector dependence of the overlap integrals. As such the transition rate may be overestimated at those points for which 'near vertical,' small change in k, transitions occur.

Kolnik, Jan↗
NASA NTRS2016

A Quantum Annealing Computer Team Addresses Climate Change Predictability

The near confluence of the successful launch of the Orbiting Carbon Observatory2 on July 2, 2014 and the acceptance on August 20, 2015 by Google, NASA Ames Research Center and USRA of a 1152 qubit D-Wave 2X Quantum Annealing Computer (QAC), offered an exceptional opportunity to explore the potential of this technology to address the scientific prediction of global annual carbon uptake by land surface processes. At UMBC,we have collected and processed 20 months of global Level 2 light CO2 data as well as fluorescence data. In addition we have collected ARM data at 2sites in the US and Ameriflux data at more than 20 stations. J. Dorband has developed and implemented a multi-hidden layer Boltzmann Machine (BM) algorithm on the QAC. Employing the BM, we are calculating CO2 fluxes by training collocated OCO-2 level 2 CO2 data with ARM ground station tower data to infer to infer measured CO2 flux data. We generate CO2 fluxes with a regression analysis using these BM derived weights on the level 2 CO2 data for three Ameriflux sites distinct from the ARM stations. P. Gentine has negotiated for the access of K34 Ameriflux data in the Amazon and is applying a neural net to infer the CO2 fluxes. N. Talik validated the accuracy of the BM performance on the QAC against a restricted BM implementation on the IBM Softlayer Cloud with the Nvidia co-processors utilizing the same data sets. G. Nearing and K. Harrison have extended the GSFC LIS model with the NCAR Noah photosynthetic parameterization and have run a 10 year global prediction of the net ecosystem exchange. C. Pellisier is preparing a BM implementation of the Kalman filter data assimilation of CO2 fluxes. At UMBC, R. Prouty is conducting OSSE experiments with the LISNoah model on the IBM iDataPlex to simulate the impact of CO2 fluxes to improve the prediction of global annual carbon uptake. J. LeMoigne and D. Simpson have developed a neural net image registration system that will be used for MODIS ENVI and will be converted to a BM algorithm implementation on the QAC. The first integer adder has been implemented on the D-Wave 2X by A. Shehab that will perform HAAR wavelets for image compression of MODIS scenes. Finally, based on the next generations of QACs, we are preparing a 5-year performance road map on the scalability of the current QAC algorithms.

Science Data Processing↗
NASA NTRS1997

Computer-aided design of a proton pump

The use of transmembrane proton gradients in energy transduction is an almost universal feature of life on earth. These proton gradients are established and maintained by specialized assemblies of proteins which actively pump protons across membranes. One broad class of proton pumps uses captured light energy to drive the proton pumping. Our goal is to elucidate the minimum structural requirements of a light-driven proton-pump. There are two basic components to a simple light-driven proton pump: a source of photo-generated protons and a "gate-keeper" which prevents these protons from reattaching themselves to their source. A wide variety of molecules in the membrane, even as simple as polycyclic aromatic hydrocarbons, are capable of releasing protons when illuminated. Our work is therefore focused on the design of the "gate-keeper." Our initial model involves a pair of proton acceptors, coupled to each other by a transient water bridge, and supported in the membrane by a small bundle of peptide helices. Upon illumination, the proton source transfers its proton to the:- first acceptor of the gate-keeper. While the reverse reaction is highly probable, all that is needed to ensure irreversibility is a nonvanishing probability that the proton will be transferred to the second acceptor across a transient water bridge. Back transfer of the proton to the first acceptor, and thence to the proton source, is impeded by the free energy required to move the proton uphill towards the. proton source and by the disruption of the transient water bridge. As a prototypical water-bridged proton transfer system, we are studying the transfer of a proton across a water bridge from a formic acid to a formate anion. With a pK(sub alpha), of 3.7. formic acid is a good model for the acidic amino acids glutamate and aspartate which are good candidates for gate-keeper proton acceptors. Simulations of proton transfer reactions in a membrane are complicated by the quantum mechanical nature of the process breaking and forming chemical bonds.

New, Michael H.↗