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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↗
DOE OSTI2013

Photo-driven autonomous hydrogen generation system based on hierarchically shelled ZnO nanostructures

A quantum dot semiconductor sensitized hierarchically shelled one-dimensional ZnO nanostructure has been applied as a quasi-artificial leaf for hydrogen generation. The optimized ZnO nanostructure consists of one dimensional nanowire as a core and two-dimensional nanosheet on the nanowire surface. Furthermore, the quantum dot semiconductors deposited on the ZnO nanostructures provide visible light harvesting properties. To realize the artificial leaf, we applied the ZnO based nanostructure as a photoelectrode with non-wired Z-scheme system. The demonstrated un-assisted photoelectrochemical system showed the hydrogen generation properties under 1 sun condition irradiation. In addition, the quantum dot modified photoelectrode showed 2 mA/cm{sup 2} current density at the un-assisted condition.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Spectral optimization simulation of white light based on the photopic eye-sensitivity curve

Spectral optimization simulation of white light is studied to boost maximum attainable luminous efficacy of radiation at high color-rendering index (CRI) and various color temperatures. The photopic eye-sensitivity curve V(λ) is utilized as the dominant portion of white light spectra. Emission spectra of a blue InGaN light-emitting diode (LED) and a red AlInGaP LED are added to the spectrum of V(λ) to match white color coordinates. It is demonstrated that at the condition of color temperature from 2500 K to 6500 K and CRI above 90, such white sources can achieve spectral efficacy of 330–390 lm/W, which is higher than the previously reported theoretical maximum values. We show that this eye-sensitivity-based approach also has advantages on component energy conversion efficiency compared with previously reported optimization solutions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

LLRF System Analysis for the Fermilab PIP-II LINAC

Developing long-lived quantum processing units (QPUs) capable of supporting high-fidelity quantum operations is a crucial challenge on the path toward fault-tolerant quantum computing. TESLA-shaped superconducting RF (SRF) cavities, known for photon relaxation times on the order of seconds, provide an excellent foundation for 3D QPUs and quantum memory. This talk presents a novel design that leverages TESLA cavity modes coupled to ancillary transmon qubits, optimized to preserve coherence and control. By carefully engineering the package geometry, optimizing Hamiltonian parameters, and minimizing lossy participation ratios, we achieve photon relaxation times of over 16 ms and 20 ms for the two cavity modes, representing a significant improvement over previous multimode quantum memories. Despite the reduced coupling between the qubit and cavity modes, which is necessary to preserve long lifetimes, the platform supports robust and universal control schemes that are not limited by low coupling strength. We will also discuss how this architecture can lead to scalable, modular quantum computing systems.

Varghese, P. [Fermilab]↗
DOE OSTI2022

Toward a QUBO-Based Density Matrix Electronic Structure Method

Density matrix electronic structure theory is used in many quantum chemistry methods to “alleviate” the computational cost that arises from directly using wave functions. Although density matrix based methods are computationally more efficient than wave function based methods, significant computational effort is involved. Because the Schrödinger equation needs to be solved as an eigenvalue problem, the time-to-solution scales cubically with the system size in mean-field type approaches such as Hartree–Fock and density functional theory and is solved as many times in order to reach charge or field self-consistency. We hereby propose and study a method to compute the density matrix by using a quadratic unconstrained binary optimization (QUBO) solver. This method could be useful to solve the problem with quantum computers and, more specifically, quantum annealers. Our proposed approach is based on a direct construction of the density matrix using a QUBO eigensolver. We explore the main parameters of the algorithm focusing on precision and efficiency. We show that, while direct construction of the density matrix using a QUBO formulation is possible, the efficiency and precision have room for improvement. Moreover, calculations performed with quantum annealing on D-Wave’s new Advantage quantum computer are compared with results obtained with classical simulated annealing, further highlighting some problems of the proposed method. Finally, we also suggest alternative methods that could lead to a more efficient QUBO-based density matrix construction.

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

Probing Interfacial Electronic States in CdSe Quantum Dots using Second Harmonic Generation Spectroscopy

Understanding and rationally controlling the properties of nanomaterial surfaces is a rapidly expanding field of research due to the dramatic role they play on the optical and electronic properties vital to light harvesting, emitting and detection technologies. This information is essential to the continued development of synthetic approaches designed to tailor interfaces for optimal nanomaterial based device performance. In this work, closely spaced electronic excited states in model CdSe quantum dots (QDs) are resolved using second harmonic generation (SHG) spectroscopy, and the corresponding contributions from surface species to these states are assessed. Two distinct spectral features are observed in the SHG spectra, which are not readily identified in linear absorption and photoluminescence excitation spectra. These features include a weak band at 395 6 nm, which coincides with transitions to the 2S1/2 1Se state, and a much more pronounced band at 423 4 nm arising from electronic transitions to the 1P3/2 1Pe state. Chemical modification of the QD surfaces through oxidation resulted in disappearance of the SHG band corresponding to the 1P3/2 1Pe state, indicating prominent surface contributions. Signatures of deep trap states localized on the surfaces of the QDs are also observed. We further find that the SHG signal intensities depend strongly on the electronic states being probed and their relative surface contributions, thereby offering additional insight into the surface specificity of SHG signals from QDs.

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

Automating NISQ Application Design with Meta Quantum Circuits with Constraints (MQCC)

Near-term intermediate scale quantum (NISQ) computers are likely to have very restricted hardware resources, where precisely controllable qubits are expensive, error-prone, and scarce. Programmers of such computers must therefore balance trade-offs among a large number of (potentially heterogeneous) factors specific to the targeted application and quantum hardware. To assist them, we propose Meta Quantum Circuits with Constraints (MQCC), a meta-programming framework for quantum programs. Programmers express their application as a succinct collection of normal quantum circuits stitched together by a set of (manually or automatically) added meta-level choice variables, whose values are constrained according to a programmable set of quantitative optimization criteria. MQCC’s compiler generates the appropriate constraints and solves them via an SMT solver, producing an optimized, runnable program. We showcase a few MQCC’s applications for its generality including an automatic generation of efficient error syndrome extraction schemes for fault-tolerant quantum error correction with heterogeneous qubits and an approach to writing approximate quantum Fourier transformation and quantum phase estimation that smoothly trades off accuracy and resource use. We also illustrate that MQCC can easily encode prior one-off NISQ application designs-–multi-programming (MP), crosstalk mitigation (CM)—as well as a combination of their optimization goals (i.e., a combined MP-CM).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Risk-Sensitive Control of Pure Jump Process on Countable Space with Near Monotone Cost

In this article, we study risk-sensitive control problem with controlled continuous time pure jump process on a countable space as state dynamics. We prove multiplicative dynamic programming principle, elliptic and parabolic Harnack’s inequalities. Using the multiplicative dynamic programing principle and the Harnack’s inequalities, we prove the existence and a characterization of optimal risk-sensitive control under the near monotone condition.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Physical principles of microwave assisted magnetic recording

While the basic physics of Microwave Assisted Magnetization Reversal (MAMR) phenomenon is well established both theoretically and experimentally, its application in a practical magnetic recording environment was so far studied primarily with the help of micromagnetic recording models. In this work, we instead attempt to use analytical formulation and simple numerical models to understand the main challenges as well as benefits that are associated with such a system. It appears that the main difference between the previously introduced theory [G. Bertotti et al., Phys. Rev. Lett. 86, 724 (2001); K. Rivkin et al., Appl. Phys. Lett. 92, 153104 (2008); S. Okamoto et al., J. Appl. Phys. 107, 123914 (2010).] and recording environment is that both the RF and DC magnetic fields are applied at a substantial angle to the anisotropy axis. While the associated symmetry breaking prevents one from describing the reversal process explicitly, it is possible to approximate the solutions well enough to satisfactorily match numerical models both in the case of wire and Spin Torque Oscillator generated RF fields. This approach allows for physical explanation of various effects associated with MAMR such as high gradient of writeable anisotropy and reduction of track width, and offers a clear guidance regarding future optimization of MAMR recording.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Multiobjective Hyperparameter Optimization for Deep Learning Interatomic Potential Training Using NSGA-II

Deep neural network (DNN) potentials are an emerging tool for simulation of dynamical atomistic systems, with the promise of quantum mechanical accuracy at speedups of 10000$\times$. As with other DNN methods, hyperparameters used during training can make a substantial difference in model accuracy, and optimal settings vary with dataset. To enable rapid tuning of hyperparameters for DNN potential training, we developed a scalable multiobjective optimization evolutionary algorithm for supercomputers and tested it on the Summit system at the Oak Ridge Leadership Computing Facility (OLCF). The multiobjective approach is required due to the coupling of two learned values defining the potential: the energy and force. Using a large-scale implementation of the NSGA-II algorithm adapted for training DNN potentials, we discovered several optimal multiobjective combinations, including best choices of activation functions, learning rate scaling scheme, and pairing of the two radial cutoffs used in the three dimensional descriptor function.

Coletti, Mark↗
DOE OSTI2021

Arrays of Si vacancies in 4 H -SiC produced by focused Li ion beam implantation

Point defects in SiC are an attractive platform for quantum information and sensing applications because they provide relatively long spin coherence times, optical spin initialization, and spin-dependent fluorescence readout in a fabrication-friendly semiconductor. The ability to precisely place these defects at the optimal location in a host material with nano-scale accuracy is desirable for integration of these quantum systems with traditional electronic and photonic structures. Here, we demonstrate the precise spatial patterning of arrays of silicon vacancy (${V}_{Si}$) emitters in an epitaxial 4H -SiC (0001) layer through mask-less focused ion beam implantation of Li + . We characterize these arrays with high-resolution scanning confocal fluorescence microscopy on the Si-face, observing sharp emission lines primarily coming from the ${V1}^{{\prime}}$ zero-phonon line (ZPL). The implantation dose is varied over 3 orders of magnitude, leading to ${V}_{Si}$ densities from a few per implantation spot to thousands per spot, with a linear dependence between ZPL emission and implantation dose. Optically-detected magnetic resonance (ODMR) is also performed, confirming the presence of V2 ${V}_{Si}$. Our investigation reveals scalable and reproducible defect generation.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Parallel implementation and performance optimization of the configuration-interaction method

The configuration-interaction (CI) method, long a popular approach to describe quantum many-body systems, is cast as a very large sparse matrix eigenpair problem with matrices whose dimension can exceed one billion. Such formulations place high demands on memory capacity and memory bandwidth - - two quantities at a premium today. In this paper, we describe an efficient, scalable implementation, BIGSTICK, which, by factorizing both the basis and the interaction into two levels, can reconstruct the nonzero matrix elements on the fly, reduce the memory requirements by one or two orders of magnitude, and enable researchers to trade reduced resources for increased computational time. We optimize BIGSTICK on two leading HPC platforms - - the Cray XC30 and the IBM Blue Gene/Q. Specifically, we not only develop an empirically-driven load balancing strategy that can evenly distribute the matrix-vector multiplication across 256K threads, we also developed techniques that improve the performance of the Lanczos reorthogonalization. Combined, these optimizations improved performance by 1.3-8× depending on platform and configuration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Enhanced red luminescence quantum yield from Gd3+/Eu3+: CaLa2ZnO5 phosphor spheres for photonic applications

Highlights: • Ravishing red emission is observed from Gd{sup 3+}/Eu{sup 3+}: CaLa{sub 2}ZnO{sub 5} phosphor spheres. • Red emission of Eu{sup 3+} was significantly enhanced by co-doping with Gd{sup 3+}. • Effective sensitization of Gd{sup 3+} ions and enhanced quantum yield have been elucidated. • Energy transfer from Gd{sup 3+} to Eu{sup 3+} was demonstrated by several fluorescence dynamics. • CIE coordinates are very close to National Television Standard Committee (NTSC). - Abstract: Novel bright red emission was obtained from co-doped Gd{sup 3+}/Eu{sup 3+}: CaLa{sub 2}ZnO{sub 5} (CLZO) phosphor spheres. We have successfully synthesized co-doped Gd{sup 3+}/Eu{sup 3+}: CLZO phosphor spheres by citrate sol-gel method. Structural and morphological analysis was carried out by XRD and FE-SEM. EDS and XPS studies were employed to analyze the presence of elements and their ionic states respectively. The complex formation studies were carried out by the FTIR spectral study. The photoluminescence spectral profiles confirm the dazzling red emission has been observed at 626 nm ({sup 5}D{sub 0} → {sup 7}F{sub 2}) from the Eu{sup 3+}: CLZO phosphors under the excitation of 467 nm. Upon increasing the Eu{sup 3+} concentration, the photoluminescence performance has also increased remarkably. The optimized concentration of the Eu{sup 3+} was noticed at 12 mol%. Upon co-doping with Gd{sup 3+} ions to Eu{sup 3+}: CLZO phosphors, the red emission pertaining to Eu{sup 3+} was appreciably enhanced through energy transfer from Gd{sup 3+} to Eu{sup 3+} ions. The energy transfer mechanism was substantiated by several fluorescent methods such as lifetime decay dynamics and quantum efficiencies. Commission International de I’Echairage (CIE) 1931 chromaticity coordinates (x–y) calculated for singly doped and co-doped CLZO samples. The CIE coordinates were found to be (0.6644, 0.3355) and (0.6688, 0.3311) respectively, which were good consistent with the standard values of NTSC surprisingly. Correlated color temperature (CCT) value was evaluated for optimised co-doped sample and it was found to be between 2000–5000 K which could be more preferable red component for warm white light applications. The lifetime and quantum efficiency of the optimized co-doped sample have been evaluated as 1.102 ms and 69% respectively. The ravishing red emission of the Gd{sup 3+}/Eu{sup 3+}: CLZO phosphor spheres which suggests that these phosphor materials could be promising candidates for red luminescent optical devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Multiobjective optimization of nuclear microreactor reactivity control system operation with swarm and evolutionary algorithms

To improve the marketability of novel microreactor designs, there is a need for automated and optimal control of these reactors. This paper presents a methodology for performing multiobjective optimization of control drum operation for a microreactor under normal and off-nominal conditions. Here, two different case studies are used where the control drum configuration is optimized for the reactor to be critical with some desired power distribution that would satisfy peaking limits. A surrogate model for power distribution is developed based on a feedforward neural network. The process for determining weights for scalarization of the multiobjective optimization problem is also detailed. Six optimization algorithms: evolutionary strategies, differential evolution, grey wolf optimization, Harris hawks optimization, moth flame optimization and particle swarm optimization, are all applied to these cases and the results analyzed. Although all these algorithms will demonstrate optima-seeking behavior, for real-time control it is necessary to identify the best algorithm to efficiently provide reasonable optima without operator interference. The moth flame optimization algorithm was found to perform particularly well on both cases. Overall, it was found that the algorithms capable of supplying the best optima were also the most consistent. Finally, the found optima were verified with the original model used to train surrogates.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Omnidirectional Harvesting of Weak Light Using a Graphene Quantum Dot-Modified Organic/Silicon Hybrid Device

Despite great improvements in traditional inorganic photodetectors and photovoltaics, more progress is needed in the detection/collection of light at low-level conditions. Traditional photodetectors tend to suffer from high noise when operated at room temperature; therefore, these devices require additional cooling systems to detect weak or dim light. Conventional solar cells also face the challenge of poor light-harvesting capabilities in hazy or cloudy weather. The real world features such varying levels of light, which makes it important to develop strategies that allow optical devices to function when conditions are less than optimal. In this work, we report an organic/inorganic hybrid device that consists of graphene quantum dot-modified poly(3,4-ethylenedioxythiophene) polystyrenesulfonate spin-coated on Si for the detection/harvest of weak light. The hybrid configuration provides the device with high responsivity and detectability, omnidirectional light trapping, and fast operation speed. To demonstrate the potential of this hybrid device in real world applications, we measured near-infrared light scattered through human tissue to demonstrate noninvasive oximetric photodetection as well as characterized the device's photovoltaic properties in outdoor (i.e., weather-dependent) and indoor weak light conditions. In conclusion, this organic/inorganic device configuration demonstrates a promising strategy for developing future high-performance low-light compatible photodetectors and photovoltaics.

14 SOLAR ENERGY↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Correlation functions from a unified variational principle: Trial Lie groups

Time-dependent expectation values and correlation functions for many-body quantum systems are evaluated by means of a unified variational principle. It optimizes a generating functional depending on sources associated with the observables of interest. It is built by imposing through Lagrange multipliers constraints that account for the initial state (at equilibrium or off equilibrium) and for the backward Heisenberg evolution of the observables. The trial objects are respectively akin to a density operator and to an operator involving the observables of interest and the sources. We work out here the case where trial spaces constitute Lie groups. This choice reduces the original degrees of freedom to those of the underlying Lie algebra, consisting of simple observables; the resulting objects are labeled by the indices of a basis of this algebra. Explicit results are obtained by expanding in powers of the sources. Zeroth and first orders provide thermodynamic quantities and expectation values in the form of mean-field approximations, with dynamical equations having a classical Lie–Poisson structure. At second order, the variational expression for two-time correlation functions separates–as does its exact counterpart–the approximate dynamics of the observables from the approximate correlations in the initial state. Two building blocks are involved: (i) a commutation matrix which stems from the structure constants of the Lie algebra; and (ii) the second-derivative matrix of a free-energy function. The diagonalization of both matrices, required for practical calculations, is worked out, in a way analogous to the standard RPA. The ensuing structure of the variational formulae is the same as for a system of non-interacting bosons (or of harmonic oscillators) plus, at non-zero temperature, classical Gaussian variables. This property is explained by mapping the original Lie algebra onto a simpler Lie algebra. The results, valid for any trial Lie group, fulfill consistency properties and encompass several special cases: linear responses, static and time-dependent fluctuations, zero- and high-temperature limits, static and dynamic stability of small deviations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Machine-learning accelerated geometry optimization in molecular simulation

Geometry optimization is an important part of both computational materials and surface science because it is the path to finding ground state atomic structures and reaction pathways. These properties are used in the estimation of thermodynamic and kinetic properties of molecular and crystal structures. This process is slow at the quantum level of theory because it involves an iterative calculation of forces using quantum chemical codes such as density functional theory (DFT), which are computationally expensive and which limit the speed of the optimization algorithms. It would be highly advantageous to accelerate this process because then one could do either the same amount of work in less time or more work in the same time. Here, we provide a neural network (NN) ensemble based active learning method to accelerate the local geometry optimization for multiple configurations simultaneously. We illustrate the acceleration on several case studies including bare metal surfaces, surfaces with adsorbates, and nudged elastic band for two reactions. In all cases, the accelerated method requires fewer DFT calculations than the standard method. In addition, we provide an Atomic Simulation Environment (ASE)-optimizer Python package to make the usage of the NN ensemble active learning for geometry optimization easier.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗