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NASA NTRS1998

Quantum Well Infrared Photodetector Research and Development at Jet Propulsion Laboratory

In this paper we discuss the optimization of the detector design, material growth and processing that has culminated in realization of 15 micron cutoff 128 x 128 QWIP focal plane array camera, hand-held and palmize 256 x 256 long-wavelength QWIP cameras and 648 x 480 long-wavelength cameras, holding forth great promise for myriad applications in 6-25 micron wavelength range in science, medicine, defense and industry.

Quantum↗

NASA NTRS2000

"Genetically Engineered" Nanoelectronics

The quantum mechanical functionality of nanoelectronic devices such as resonant tunneling diodes (RTDs), quantum well infrared-photodetectors (QWIPs), quantum well lasers, and heterostructure field effect transistors (HFETs) is enabled by material variations on an atomic scale. The design and optimization of such devices requires a fundamental understanding of electron transport in such dimensions. The Nanoelectronic Modeling Tool (NEMO) is a general-purpose quantum device design and analysis tool based on a fundamental non-equilibrium electron transport theory. NEW was combined with a parallelized genetic algorithm package (PGAPACK) to evolve structural and material parameters to match a desired set of experimental data. A numerical experiment that evolves structural variations such as layer widths and doping concentrations is performed to analyze an experimental current voltage characteristic. The genetic algorithm is found to drive the NEMO simulation parameters close to the experimentally prescribed layer thicknesses and doping profiles. With such a quantitative agreement between theory and experiment design synthesis can be performed.

Klimeck, Gerhard↗

NASA NTRSDate not supplied

Image-to-Image Wildfire Detection via Quantum-Compatible Variational Segmentation from Remotely-sensed Data

Over the last decade, the incidence of wildfires has surged, causing widespread destruction globally. To better comprehend and manage these incidents, remote sensing and aerial missions have been implemented in recent efforts. However, this has resulted in an exponential rise in the amount of remote sensing data utilization, leading to a need for intelligent automation of data extraction in wildfire studies. Machine learning provides an accurate automated approach for detecting these natural anomalies and facilitates decision-makers to take prompt actions. To make insightful decisions in wildfire management, it is imperative to move beyond simple detection and explore the potential of probabilistic generative machine learning for creating "what-if" scenarios for various wildfire conditions. Such models offer improved representation of the stochastic nature of wildfire events. However, the optimization of these models can be computationally expensive, especially when using classical computers. Quantum computers have recently emerged as a promising solution to reduce the computational cost of training such models and improve their performance. In this study, we aim to utilize quantum-compatible machine learning techniques to implement our probabilistic generative approach. To that end, we propose a supervised probabilistic variational model consisting of a U-NET-based image-to-image component along with encoder and decoder networks which work as a variational autoencoder (VAE) component. Additionally, we explore the type of latent distribution type in the VAE component and implement different means for modeling the prior distribution. We further investigate the quantum-compatible versions of the model compared to the classical counterpart and benchmark potential benefits of quantum compatibility over the classical model.

quantum machine learning↗

NASA NTRS1993

Heterodyne efficiency for a coherent laser radar with diffuse or aerosol targets

The performance of a Coherent Laser Radar is determined by the statistics of the coherent Doppler signal. The heterodyne efficiency is an excellent indication of performance because it is an absolute measure of beam alignment and is independent of the transmitter power, the target backscatter coefficient, the atmospheric attenuation, and the detector quantum efficiency and gain. The theoretical calculation of heterodyne efficiency for an optimal monostatic lidar with a circular aperture and Gaussian transmit laser is presented including beam misalignment in the far-field and near-field regimes. The statistical behavior of estimates of the heterodyne efficiency using a calibration hard target are considered. For space based applications, a biased estimate of heterodyne efficiency is proposed that removes the variability due to the random surface return but retains the sensitivity to misalignment. Physical insight is provided by simulation of the fields on the detector surface. The required detector calibration is also discussed.

Frehlich, R. G.↗

NASA NTRS2005

High-Efficiency Multi-Junction Space Solar Development Utilizing Lattice Grading

Progress towards achieving a high one-sun air mass 0 (AM0) efficiency in a monolithic dual junction solar cell comprised of a 1.62 eV InGaP top cell and a 1.1 eV InGaAs bottom cell grown on buffered GaAs is reported. The performance of stand-alone 1.62 eV InGaP and 1.1 eV InGaAs cells is compared to that of the dual junction cell. Projected AM0 efficiencies of 15.7% and 16.5% are expected for the 1.62 eV InGaP and 1.1 eV InGaAs cells grown on buffered GaAs. The dual junction cell has a projected one-sun AM0 conversion efficiency of 17%. The projected efficiencies are based upon the application of an optimized anti-reflective coating (ARC) to the as-grown cells. Quantum efficiency (QE) data obtained from the dual junction cell indicate that is is bottom cell current limited with the top cell generating 50% more current than the bottom cell. A comparison of the QE data for the stand-alone 1.1 eV InGaAs cell to that of the 1.1 eV InGaAs bottom cell in the tandem configuration indicates a degradation of the bottom cell conversion efficiency in the tandem configuration. The origin of this performance degradation is at present unknown. If the present limitation can be overcome, then a one-sun AM0 efficiency of 26% is achievable with the 1.62 eV/1.1 eV dual junction cell grown lattice-mismatched to GaAs.

Stan, Mark A.↗

NASA NTRS2026

Optimization of X-ray Absorbers for High-Resolution TES Detectors in NewATHENA X-IFU

NewATHENA’s X-ray Integral Field Unit (X-IFU) uses a large-format array of Transition Edge Sensor (TES) microcalorimeters coupled with absorbers made of gold and bismuth providing 4 eV FWHM resolution up to 7 keV in the 0.2- 12 keV band. X-ray absorbers must be thick enough for photon absorption, maintain low heat capacity for energy resolution, and have good thermalization properties. The X-IFU requires total Quantum Efficiency (QE) of 96%, 87%, and 63% at 1, 7, and 9.5 keV, respectively. Optimal thicknesses are 1.05 µm gold and 5.5 µm bismuth to achieve 0.731 pJ/K at 90 mK and 87% QE at 7 keV. While typical TES absorbers are 3-4 µm thick, producing the required 5.5 µm Bi absorbers via standard electroplating creates enlarged grains and surface roughness. The increased roughness and grain size variability produced with our standard recipe have resulted in instances of 1) low energy spectral tails 2) spectral broadening of the energy resolution and 3) thermal shorts between pixels when a large grain bridges the gap between absorbers. In this study, we have introduced a new fabrication process and altered the morphology of the thick Bi through changes in the electroplating process. The process changes are intended to address all three issues as well as reduce particulate contamination observed on the finished detector arrays.

Absorber↗

NASA NTRSDate not supplied

Microcalorimeter Absorber Optimization for 0.2 to 12 keV X-Rays

The Advanced Telescope for High ENergy Astrophysics (ATHENA) mission requires high quantum efficiency (QE) x-ray absorption, >90.6% at 7 keV and low specific heat capacity, 0.731 pJ/K. The designed ATHENA x-ray absorbers are cantilevered square tiles (pitch of 317 microns) of 1.05 μm thick Au and 5.51 μm thick Bi electroplated films supported by stems that connect the absorber to the detector below. We discuss some of the methods used to produce x-ray absorbers meeting these specifications for ATHENA. To tune the thermal conductance of the device and the effect on the normal-to superconducting transition shape, the stems need to be small diameter and can have a weak bottle-neck connection to the substrate. A funnel shape of the stem using a proximity exposed photoresist mold has been developed to improve the strength of the connections. Further requirements on the absorbers include low levels of fine particulate remaining on the substrate after production and zero shorts between absorbers due to incomplete ion milling or trapped fine particular between absorbers. To optimize for post patterning substrate cleanliness and absorber yield, we have examined several methods of absorber fabrication. Three such methods are 1) an ion mill/wet etch combination, 2) a photoresist mold for electroplating followed by wet or dry etch to remove the seed layer, and 3) an electroplating process with leveling to smooth the surface followed by ion mill to separate the absorbers. The different combinations of wet and dry etches lead to different yields and surface appearance of the absorber sidewalls. We present results on the achieved pixel yields and the energy resolution of pixels made with the various fabrication methods. We discuss the impact of absorber patterning method on performance, uniformity, and yield.

Edward J Wassell↗

NASA NTRS2004

Random Matrix Approach to Quantum Adiabatic Evolution Algorithms

We analyze the power of quantum adiabatic evolution algorithms (Q-QA) for solving random NP-hard optimization problems within a theoretical framework based on the random matrix theory (RMT). We present two types of the driven RMT models. In the first model, the driving Hamiltonian is represented by Brownian motion in the matrix space. We use the Brownian motion model to obtain a description of multiple avoided crossing phenomena. We show that the failure mechanism of the QAA is due to the interaction of the ground state with the "cloud" formed by all the excited states, confirming that in the driven RMT models. the Landau-Zener mechanism of dissipation is not important. We show that the QAEA has a finite probability of success in a certain range of parameters. implying the polynomial complexity of the algorithm. The second model corresponds to the standard QAEA with the problem Hamiltonian taken from the Gaussian Unitary RMT ensemble (GUE). We show that the level dynamics in this model can be mapped onto the dynamics in the Brownian motion model. However, the driven RMT model always leads to the exponential complexity of the algorithm due to the presence of the long-range intertemporal correlations of the eigenvalues. Our results indicate that the weakness of effective transitions is the leading effect that can make the Markovian type QAEA successful.

Boulatov, Alexei↗

NASA NTRSDate not supplied

Assessment of Quantum ML Applicability for Climate Actions: Comparison of the Variational Quantum Classifier and the Quantum Support Vector Classifier with Classical ML Models

Climate change refers to significant and long-term alterations in the Earth’s climate patterns, typically resulting from human activities that increase greenhouse gas emissions. Addressing climate change is not merely an option but a necessity, demanding creative solutions and efforts from individuals, researchers, communities, and governments. Despite the capabilities of machine learning (ML) with data-driven solutions promising to combat climate change-related problems, they face challenges stemming from traditional computational methods and prolonged training times, impeding their practical utility. Recent strides in quantum computing have permeated diverse domains, spanning from manufacturing engineering and pharmaceutical discovery to the latest frontier of detecting climate anomalies. With the potential to substantially reduce time and computational complexity, quantum computing shows promise in addressing climate change impacts. Its distinctive features will enable the concurrent exploration of expansive solution spaces, making it well-suited for analyzing extensive climate datasets, simulating intricate climate models, optimizing resource allocation, and discerning patterns in climate data for mitigation and adaptation endeavors. This study explores the potential of using Quantum machine learning (QML) techniques on climate and weather data obtained from NASA Giovannis. We used two QML algorithms, the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC) models, using the IBM Qiskit ML 0.7.2 ecosystem. We used an actual 127-Qubit IBM Quantum Computer (IBM 127-qubit Eagle) in this study. The methodology and results sections describe the experiences gained from applying and evaluating quantum ML results on climate and weather data obtained from NASA satellites as a novel practical application of quantum computing.

Earth Observational Data↗

NASA NTRSDate not supplied

Quantum Distributed Algorithms for Approximate Steiner Trees and Directed Minimum Spanning Trees

We present two algorithms in the Quantum CONGEST- CLIQUE model of distributed computation that succeed with high probability; one for producing an approximately optimal Steiner Tree, and one for producing an exact directed minimum spanning tree, each of which uses O ̃(n1/4) rounds of communication and O ̃(n9/4) messages, achieving a lower asymptotic round and message complexity than any known algorithms in the classical CONGEST-CLIQUE model. At a high level, we achieve these results by combining classical algorithms with fast quantum subroutines. Additionally, we characterize the constants and logarithmic factors involved in our algorithms, as well as related classical algorithms, revealing that advances are needed to render both practical.

quantum computing↗

NASA NTRSDate not supplied

Quantum Distributed Algorithms for Approximate Steiner Trees and Directed Minimum Spanning Trees

We present two algorithms in the Quantum CONGEST- CLIQUE model of distributed computation that succeed with high probability; one for producing an approximately optimal Steiner Tree, and one for producing an exact directed minimum spanning tree, each of which uses O ̃(n 1/4 ) rounds of communication and O ̃(n 9/4 ) messages, achieving a lower asymptotic round and message complexity than any known algorithms in the classical CONGEST-CLIQUE model. At a high level, we achieve these results by combining classical algorithms with fast quantum subroutines. Additionally, we characterize the constants and logarithmic factors involved in our algorithms, as well as related classical algorithms, revealing that advances are needed to render both practical.

quantum computing↗

NASA NTRS2022

Control Implemented on Quantum Computers: Effects of Noise, Non-Determinism, and Entanglement

Quantum computing has advanced in recent years to the point that there are now some quantum computers and quantum simulators available to the public for use. In addition, quantum computing is beginning to receive attention within the process systems engineering community for directions such as machine learning and optimization. A logical next step for its evaluation within process systems engineering is for control, specifically, for computing control actions to be applied to process systems. In this work, we provide some initial studies regarding the implementation of control on quantum computers, including the implementation of a single-input/single-output proportional control law on a quantum simulator with noise, evaluation of potential impacts of non-determinism on theory for advanced control laws, and discussion of consequences of the way that entanglement works for next-generation manufacturing communication objectives.

Kip Nieman↗

NASA NTRS1990

Characteristics of AlGaAs/GaAs multiple quantum well infrared detectors

Researchers fabricated and characterized several AlGaAs/GaAs multiple quantum well infrared detectors to evaluate the ultimate performance of these devices for low infrared background applications. The detectors were designed to have a single bound state in the quantum well and the first excited state in the continuum above the AlGaAs conduction band edge. The difference in energy between the two levels, as determined by the quantum well width and aluminum mole fraction in the barrier, was chosen such that peak absorption would occur near 8 microns. The initial structures studied comprised 50 periods with 40 A well widths and 300 A Al(0.28)Ga(0.72)As barriers. The performance of these detectors are summarized. To better interpret these results and design optimized detectors, researchers modeled both the detector noise and tunneling currents. The noise model correctly predicts that multiple quantum well detectors will, indeed, exhibit noise lower than full shot noise. The tunneling current model predicts the dark current versus bias for any choice of design parameters in a multiple quantum well detector. This model predicts a substantially reduced dark current (x 10(exp 04)) for samples with 400 A barriers. To evaluate structures with thicker barriers, researchers fabricated and characterized detectors with 400 A and 500 A barriers; a comparison of detector dark currents is shown. These results are consistent with the predictions of the dark current model.

Janousek, Bruce K.↗

NASA NTRS1996

Quantum State Engineering Via Coherent-State Superpositions

The quantum interference between the two parts of the optical Schrodinger-cat state makes possible to construct a wide class of quantum states via discrete superpositions of coherent states. Even a small number of coherent states can approximate the given quantum states at a high accuracy when the distance between the coherent states is optimized, e. g. nearly perfect Fock state can be constructed by discrete superpositions of n + 1 coherent states lying in the vicinity of the vacuum state.

Janszky, Jozsef↗

NASA NTRS1986

Biosynthetic Porphyrins and the Origin of Photosynthesis

Since the prebiotic atmosphere was anaerobic, if not reducing, a useful function of primordial photosynthesis would have been to photooxidize reduced substrates such as Fe(+2), S(-2) or reduced organic molecules and to emit hydrogen. Experiments have shown that the early biogenic pigments uroporphyrin and coproporphyrin do photooxidize organic compounds and emit hydrogen in the presence of a platinum catalyst. These experiments were carried out in dilute aqueous solution near neutral pH under anaerobic atmosphere, and quantum yields near 10-2 were obtained. Thus relevant prebiotic conditions were maintained. Rather then to further optimize conditions, attempts were made to replace the platinum catalyst by a more prebiotically suitable catalyst. Trials with an Fe4S4(SR)4 cluster, in analogy to the present hydrogenase and nitrogenase, were not successful. However, experiments using cobalt complexes to catalyze the formation of hydrogen are promising. In analogy with biological photosynthetic systems which group pigments, electron transfer molecules and enzymes in clusters for efficiency, it was found that binding the biogenic porphyrins to the polyvinyl alcohol used to support the platinum catalyst did increase the quantum yield of the reaction. It was also found that ultraviolet light can serve to photo-oxidize porphyrinogens to porphyrins under anaerobic conditions. Thus the formation of the colorless porphyriogens by the extraordinarily simple biosynthetic pathway would not be a problem because of the prevalence of UV light in the prebiotic, anoxic atmosphere.

David Mauzerall↗

NASA NTRS1991

Charge coupled devices (CCDs) in X-ray astronomy

The application of CCDs to X-ray imaging and spectroscopy in astronomy is discussed with special attention given to the special requirements of CCDs for X-ray astronomy (as opposed to those for traditional optical applications). The development status of CCDs for X-ray astronomy applications is described, and results of recent research in the area of optimizing the CCD X-ray response are presented. It is shown that very high quantum efficiencies and Fano noise-limited energy resolutions can be obtained using CCDs.

Lumb, D. H.↗

NASA NTRS1992

Source conductance scaling for high frequency superconducting quasiparticle receivers

It has been suggested that the optimum source conductance G(sub s) for the superconductor-insulator-superconductor (SIS) quasiparticle mixer should have a l/f dependence. This would imply that the critical current density of SIS junctions used for mixing should increase as frequency squared, a stringent constraint on the design of submillimeter SIS mixers, rather than in simple proportion to frequency as previously believed. We have used Tucker's quantum theory of mixing for extensive numerical calculations to determine G(sub s) for an optimized SIS receiver. We find that G(sub s) is very roughly independent of frequency (except for the best junctions at low frequency), and discuss the implications of our results for the design of submillimeter SIS mixers.

Ke, Qing↗

NASA NTRSDate not supplied

Quantum Mechanical Simulations of Dynamics of Electronic Excitations in Low-Dimensional Photovoltaics Materials

Dynamics of electronically excited states including exciton generation and relaxation, exciton-exciton interaction, and charge/energy transfer and transport, are critical to understanding and optimizing photovoltaic materials. Although considerable progress has been made, accurate experimental characterization of such crucial dynamics remains extremely challenging, especially in low-dimensional materials. In this talk, I will discuss how the advancement of quantum mechanical simulations of the dynamics of electronic excitations can reveal the physical mechanisms hindering or improving solar energy harvest. Specifically, we find that surface dangling-bond defects on silicon quantum dots cause rapid charge recombination and low carrier mobility in the inorganic-organic PV cells. Although small quantum dots are essentially free of defects, their optical gaps are too large. To solve this issue, a full-spectrum-light-absorption scheme is computationally designed, taking advantage of complexity and flexibility of low-dimensional materials consisting of different characteristics and dimensions. In this scheme the multifunctional ligands are used to (1) passivate against oxidation, (2) facilitate interfacial charge transfer, and (3) improve both charge transport and optical absorption. A joint theoretical and experimental study has demonstrated the feasibility of this approach. Finally, I will briefly talk about how this scheme and the well-tested quantum mechanical simulations can be employed to help improve energy-conversion efficiency in perovskite photovoltaics including perovskite-based tandem cells.

Zhigang Wu↗