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DOE OSTI2023

Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry

Due to intense interest in the potential applications of quantum computing, it is critical to understand the basis for potential exponential quantum advantage in quantum chemistry. Here we gather the evidence for this case in the most common task in quantum chemistry, namely, ground-state energy estimation, for generic chemical problems where heuristic quantum state preparation might be assumed to be efficient. The availability of exponential quantum advantage then centers on whether features of the physical problem that enable efficient heuristic quantum state preparation also enable efficient solution by classical heuristics. Through numerical studies of quantum state preparation and empirical complexity analysis (including the error scaling) of classical heuristics, in both ab initio and model Hamiltonian settings, we conclude that evidence for such an exponential advantage across chemical space has yet to be found. While quantum computers may still prove useful for ground-state quantum chemistry through polynomial speedups, it may be prudent to assume exponential speedups are not generically available for this problem.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Variational quantum state eigensolver

Extracting eigenvalues and eigenvectors of exponentially large matrices will be an important application of near-term quantum computers. The variational quantum eigensolver (VQE) treats the case when the matrix is a Hamiltonian. Here, we address the case when the matrix is a density matrix ρ. We introduce the variational quantum state eigensolver (VQSE), which is analogous to VQE in that it variationally learns the largest eigenvalues of ρ as well as a gate sequence V that prepares the corresponding eigenvectors. VQSE exploits the connection between diagonalization and majorization to define a cost function C=Tr(ρ~H) where H is a non-degenerate Hamiltonian. Due to Schur-concavity, C is minimized when ρ~=VρV† is diagonal in the eigenbasis of H. VQSE only requires a single copy of ρ (only n qubits) per iteration of the VQSE algorithm, making it amenable for near-term implementation. We heuristically demonstrate two applications of VQSE: (1) Principal component analysis, and (2) Error mitigation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Quantifying motion blur by imaging shock front propagation with broadband and narrowband X-ray sources

Time-integrated radiography using MeV Bremsstrahlung X-ray sources is the norm for imaging during system-level testing of components and structures under dynamic condition. One source of error in the analysis of the time-integrated radiography data sets stems from motion blur which smears out sharp interfaces to a greater degree with longer exposure times, which become necessary to provide sufficient signal-to-noise with low X-ray penetration of objects of interest. To quantify motion blur, a 1D shock wave through PMMA was investigated experimentally at The Dynamic Compression Sector at The Advanced Photon Source (DCS@APS) with tapered broadband and 25.46 ± 1.06 keV narrowband X-rays. Four cameras with different exposure times were used for each experiment to compare the effect that exposure time has on motion blur. In addition, our methodology to accurately simulate motion blur in terms of transmission and shape is presented and compared to our experimental results and quantified. There is a high level of agreement between the experimental and simulation results across the range of data sets investigated in this study with a percent difference range of 0.29–1.31% for the four shots. The methodology of this work serves as a steppingstone towards a physically validated model that could be used in conjunction with experimental results to deconvolve physical parameters, densities, and interfaces of interest in a way that would not be possible with experimental results alone.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

Determination of transition metal ion distribution in cubic spinel Co{sub 1.5}Fe{sub 1.5}O{sub 4} using anomalous x-ray diffraction

We report anomalous x-ray diffraction studies on Co ferrite with composition Co{sub 1.5}Fe{sub 1.5}O{sub 4} to obtain the distribution of transition metal ions in tetrahedral and octahedral sites. We synthesize spinel oxide (Co{sub 1.5}Fe{sub 1.5}O{sub 4}) through co-precipitation and subsequent annealing route. The imaginary part (absorption) of the energy dependent anomalous form factor is measured and the real part is calculated theoretically through Kramers–Krönig transformation to analyze anomalous x-ray diffraction peak intensities. Fe and Co K-edge x-ray absorption near edge structure (XANES) spectra are used to estimate charge states of transition metals. Our analysis, within experimental errors, suggests 44% of the tetrahedral sites contain Co in +2 oxidation state and the rest 56% sites contain Fe in +2 and +3 oxidation states. Similarly, 47% of the octahedral sites contain Fe in +3 oxidation states, whereas, the rest of the sites contain Co in +2 and +3 oxidation states. While a distinct pre-edge feature in the Fe K-edge XANES is observed, Co pre-edge remains featureless. Implications of these results to magnetism are briefly discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Calculating the free energy of transfer of small solutes into a model lipid membrane: Comparison between metadynamics and umbrella sampling

We compare the performance of two well-established computational algorithms for the calculation of free-energy landscapes of biomolecular systems, umbrella sampling and metadynamics. We look at benchmark systems composed of polyethylene and polypropylene oligomers interacting with lipid (phosphatidylcholine) membranes, aiming at the calculation of the oligomer water-membrane free energy of transfer. We model our test systems at two different levels of description, united-atom and coarse-grained. We provide optimized parameters for the two methods at both resolutions. We devote special attention to the analysis of statistical errors in the two different methods and propose a general procedure for the error estimation in metadynamics simulations. Metadynamics and umbrella sampling yield the same estimates for the water-membrane free energy profile, but metadynamics can be more efficient, providing lower statistical uncertainties within the same simulation time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Prediction challenge: First principles simulation of the ultrafast electron diffraction spectrum of cyclobutanone

Computer simulation has long been an essential partner of ultrafast experiments, allowing the assignment of microscopic mechanistic detail to low-dimensional spectroscopic data. However, the ability of theory to make a priori predictions of ultrafast experimental results is relatively untested. Herein, as a part of a community challenge, we attempt to predict the signal of an upcoming ultrafast photochemical experiment using state-of-the-art theory in the context of preexisting experimental data. Specifically, we employ ab initio Ehrenfest with collapse to a block mixed quantum–classical simulations to describe the real-time evolution of the electrons and nuclei of cyclobutanone following excitation to the 3s Rydberg state. The gas-phase ultrafast electron diffraction (GUED) signal is simulated for direct comparison to an upcoming experiment at the Stanford Linear Accelerator Laboratory. Following initial ring-opening, dissociation via two distinct channels is observed: the C3 dissociation channel, producing cyclopropane and CO, and the C2 channel, producing CH2CO and C2H4. Direct calculations of the GUED signal indicate how the ring-opened intermediate, the C2 products, and the C3 products can be discriminated in the GUED signal. We also report an a priori analysis of anticipated errors in our predictions: without knowledge of the experimental result, which features of the spectrum do we feel confident we have predicted correctly, and which might we have wrong?

Chemistry↗

DOE OSTI2020

Quantum approximate optimization of the long-range Ising model with a trapped-ion quantum simulator

Quantum computers and simulators may offer significant advantages over their classical counterparts, providing insights into quantum many-body systems and possibly improving performance for solving exponentially hard problems, such as optimization and satisfiability. Here, we report the implementation of a low-depth Quantum Approximate Optimization Algorithm (QAOA) using an analog quantum simulator. We estimate the ground-state energy of the Transverse Field Ising Model with long-range interactions with tunable range, and we optimize the corresponding combinatorial classical problem by sampling the QAOA output with high-fidelity, single-shot, individual qubit measurements. We execute the algorithm with both an exhaustive search and closed-loop optimization of the variational parameters, approximating the ground-state energy with up to 40 trapped-ion qubits. We benchmark the experiment with bootstrapping heuristic methods scaling polynomially with the system size. We observe, in agreement with numerics, that the QAOA performance does not degrade significantly as we scale up the system size and that the runtime is approximately independent from the number of qubits. We finally give a comprehensive analysis of the errors occurring in our system, a crucial step in the path forward toward the application of the QAOA to more general problem instances.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2013

ENVIRONMENTAL EFFECTS ON STAR FORMATION ACTIVITY AT z {approx} 0.9 IN THE COSMOS FIELD

We investigated the fraction of [O II] emitters in galaxies at z {approx} 0.9 as a function of the local galaxy density in the Hubble Space Telescope (HST) COSMOS 2 deg{sup 2} field. [O II] emitters are selected by the narrowband excess technique with the NB711-band imaging data taken with Suprime-Cam on the Subaru telescope. We carefully selected 614 photo-z-selected galaxies with M{sub U3500} < -19.31 at z = 0.901 - 0.920, which includes 195 [O II] emitters, to directly compare the results with our previous study at z {approx} 1.2. We found that the fraction is almost constant at 0.3 Mpc{sup -2} < {Sigma}{sub 10th} < 10 Mpc{sup -2}. We also checked the fraction of galaxies with blue rest-frame colors of NUV - R < 2 in our photo-z-selected sample, and found that the fraction of blue galaxies does not significantly depend on the local density. On the other hand, the semi-analytic model of galaxy formation predicted that the fraction of star-forming galaxies at z {approx} 0.9 decreases with increasing projected galaxy density even if the effects of the projection and the photo-z error in our analysis were taken into account. The fraction of [O II] emitters decreases from {approx}60% at z {approx} 1.2 to {approx}30% at z {approx} 0.9 independent of galaxy environment. The decrease of the [O II] emitter fraction could be explained mainly by the rapid decrease of star formation activity in the universe from z {approx} 1.2 to z {approx} 0.9.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

Unravelling the Excitation Spectrum of the Nucleon

The low-energy structure of QCD lies encoded in the excited states of the nucleon, a complicated overlap of many resonances. Recent Lattice calculations have confirmed the longstanding quark model predictions of many more excited states than have been identified. Reactions that probe the spectrum are clouded by effects that dress the interactions and complicate the identification of excited levels and the interpretation of their structure. Recent theoretical work has exposed dramatic effects from such dressings. On the experimental side, new complete measurements of pseudoscalar meson photo-production are being pursued at several laboratories, where here the designation of complete refers to measurements of most if not all of the 16 possible reaction observables. This has been the focus of a series of experiments at Jefferson Lab culminating in the recently completed g9/FROST and g14/HDice runs which are now under analysis. With realistic errors, the number of observables needed to constrain the production amplitude is many more than required of a mathematical solution.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

Constraining the mass–richness relationship of redMaPPer clusters with angular clustering

The potential of using cluster clustering for calibrating the mass–richness relation of galaxy clusters has been recognized theoretically for over a decade. In this paper, we demonstrate the feasibility of this technique to achieve high-precision mass calibration using redMaPPer clusters in the Sloan Digital Sky Survey North Galactic Cap. By including cross-correlations between several richness bins in our analysis, we significantly improve the statistical precision of our mass constraints. The amplitude of the mass–richness relation is constrained to 7 per cent statistical precision by our analysis. However, the error budget is systematics dominated, reaching a 19 per cent total error that is dominated by theoretical uncertainty in the bias–mass relation for dark matter haloes. We confirm the result from Miyatake et al. that the clustering amplitude of redMaPPer clusters depends on galaxy concentration as defined therein, and we provide additional evidence that this dependence cannot be sourced by mass dependences: some other effect must account for the observed variation in clustering amplitude with galaxy concentration. Assuming that the observed dependence of redMaPPer clustering on galaxy concentration is a form of assembly bias, we find that such effects introduce a systematic error on the amplitude of the mass–richness relation that is comparable to the error bar from statistical noise. Finally, the results presented here demonstrate the power of cluster clustering for mass calibration and cosmology provided the current theoretical systematics can be ameliorated.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Complete quantification of parametric uncertainties in ( d , p ) transfer reactions

Deuteron-induced transfer reactions are a popular probe in nuclear structure and nuclear astro physics studies. The interpretation of these transfer measurements relies on reaction theory that takes as input effective interactions between the nucleons and the target nucleus. Previous work quantified the uncertainty associated with the optical potentials between the nucleons and the target. In this study, we extend that work by also including the parameters of the mean field associated with the overlap function of the final bound state, thus obtaining the full parametric uncertainty on transfer observables. We use Bayesian Markov Chain Monte Carlo simulations to obtain parameter posterior distributions. We use elastic-scattering cross sections to constrain the optical potential parameters and use the asymptotic normalization coefficient of the final state to constrain the bound state interaction. We then propagate these posteriors to the transfer angular distributions and obtain confidence intervals for this observable. We study (d,p) reactions on 14 C, 16 O, and 48 Ca at energies in the range $E$ $d$ = 7 - 24 MeV. Our results show a strong reduction in uncertainty by using the asymptotic normalization coefficient as a constraint, particularly for those reactions most sensitive to ambiguities in the mean field. For those reactions, the importance of constraining the bound state interaction is equal to that of constraining the optical potentials. Furthermore, the case of 14 C is an outlier because the cross section is less sensitive to the nuclear interior. When minimal constraints are used on the parameters of the nucleon-target interaction, the 1σ uncertainties on the differential cross sections are large (~ 500-100%). However, if elastic-scattering data and the asymptotic normalization coefficient are used in the analysis, with an error of 10% (5%), this uncertainty reduces to ~ 30% (~ 15%).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Visual Analytics of Performance of Quantum Computing Systems and Circuit Optimization

Driven by potential exponential speedups in business, security, and scientific scenarios, interest in quantum computing is surging. This interest feeds the development of quantum computing hardware, but several challenges arise in optimizing application performance for hardware metrics (e.g., qubit coherence and gate fidelity). In this work, we describe a visual analytics approach for analyzing the performance properties of quantum devices and quantum circuit optimization. Our approach allows users to explore spatial and temporal patterns in quantum device performance data and it computes similarities and variances in key performance metrics. Detailed analysis of the error properties characterizing individual qubits is also supported. We also describe a method for visualizing the optimization of quantum circuits. The resulting visualization tool allows researchers to design more efficient quantum algorithms and applications by increasing the interpretability of quantum computations.

Chae, Junghoon↗

DOE OSTI2024

Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method

Traditional power flow methods often adopt certain assumptions designed for passive balanced distribution systems, thus lacking practicality for unbalanced operation. moreover, their computation accuracy and efficiency are heavily subject to unknown errors and bad data in measurements or prediction data of distributed energy resources (ders). to address these issues, this paper proposes a hybrid data-aided robust power flow algorithm in unbalanced distribution systems, which combines taylor series expansion knowledge with a data-driven regression technique. the proposed method initiates a linearization power flow model to derive an explicitly analytical solution by modified taylor expansion. to mitigate the approximation loss that surges due to the der integration and bad data, we further develop a data-aided robust support vector regression approach to estimate the errors efficiently. comparative analysis in the 13-bus and 123-bus ieee unbalanced feeders shows that the proposed hybrid algorithm achieves superior computational efficiency, with guaranteed accuracy and robustness against outliers.

data-driven↗

DOE OSTI2023

zPerf: A Statistical Gray-Box Approach to Performance Modeling and Extrapolation for Scientific Lossy Compression

With the scaling up of simulation-based scientific discovery on high-performance computing systems, the disparity between compute and I/O has increased, forcing domain scientists to save only a small amount of simulation data to persistent storage. This can result in the loss of essential physics fields that are needed for data analysis. While error-bounded lossy compression has made tremendous progress in bridging the gap between compute and I/O, the lack of understanding of compression performance remains a key hurdle to its wide adoption. Here, in this work, we present zPerf, a statistical gray-box performance modeling approach for scientific lossy compression. Our contributions are threefold: 1) We develop zPerf to estimate the performance of lossy compression techniques, based on in-depth understanding and statistical modeling for data features and core compression metrics; 2) We demonstrate the in-detailed implementation of zPerf using two case studies, where we derive the performance modeling for SZ and ZFP, two leading lossy compressors; 3) We evaluate the effectiveness of zPerf on real-world datasets across various domains. Based on the evaluation, we demonstrate the efficacy of the zPerf performance model; 4) We further discuss three case studies where zPerf is applied to extrapolate the compression ratio of SZ and ZFP with alternative encoding schemes as well as ZFP with an alternative transform scheme. Through the case studies, we demonstrate the potential of zPerf for exploring the design space of lossy compression, which has hardly been studied in the literature.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

RF transient analysis and stabilization of the phase and energy of the proposed PIP-II LINAC

Here, this paper describes a recent effort to develop and benchmark a simulation tool for the analysis of RF transients and their compensation in an H- linear accelerator. Existing tools in this area either focus on electron LINACs or lack fundamental details about the LLRF system that are necessary to provide realistic performance estimates. In our paper we begin with a discussion of our computational models followed by benchmarking with existing beam-dynamics codes and measured data. We then analyze the effect of RF transients and their compensation in the PIP-II LINAC, followed by an analysis of calibration errors and how a Newton’s Method based feedback scheme can be used to regulate the beam energy to within the specified limits.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design

Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. Here, the present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity (dOS)), and using ANNs trained with QSPR descriptors to directly predict OS. Twenty-five ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. Twenty-five additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.

09 BIOMASS FUELS↗

DOE OSTI2016

TU-H-CAMPUS-TeP2-04: Measurement of Stereotactic Output Factors with DNA Double-Strand Breaks

Purpose: Radiotherapy treatment is specified by radiation dose prescriptions, but biological DNA damage actually controls treatment effectiveness. It is impractical to directly measure dose in the clinic, so we measure quantities, such as collected charge, and calculate the relationship to dose. At small fields, such as those in stereotactic radiosurgery (SRS), charged-particle equilibrium (CPE) breaks down and the accuracy of the measurement for delivered dose decreases. By measuring DNA double-strand breaks (DSB) directly, we believe treatment accuracy could improve by providing a more meaningful measurement. Methods: A DNA dosimeter, consisting of magnetic streptavidin beads attached to 4 kilobase pair DNA strands labeled with biotin and fluorescein amidite (FAM) on opposing ends, was suspended in phosphate-buffered saline (PBS). Twenty µL samples were placed in plastic micro-capillary tubes inside a water tank setup and irradiated with 10 cm, 3 cm, 1.25 cm, 0.75 cm, and 0.5 cm radiation field sizes, where the three smallest sizes were cones. After irradiation, the dosimeters were mechanically separated into beads (intact DNA) and supernatant (broken DNA/FAM) using a magnet. The fluorescence was read and the probability of DSB was calculated. This was used to calculate the output factor for an SRS beam and compared to that measured using a diode detector. Results: The output factors relative to a 10 cm field were 0.89±0.07, 0.76±0.08, 0.59±0.04, and 0.78±0.12 for the field sizes of 3 cm, 1.25 cm, 0.75 cm, and 0.5 cm, respectively. Some of the diode measurements do not fall within these uncertainties. Conclusion: This was the first attempt to measure output factors in a water tank with the DNA dosimeter. Although differences compared to the diode were observed, the uncertainty analysis ignored systematic errors. For future work, we will repeat this experiment to quantify and correct systematic errors, such as those caused by positional alignment and sample contamination. This work was funded in part by CPRIT (RP140105).

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

A Predictor-Corrector Strategy for Adaptivity in Dynamical Low-Rank Approximations

Here, in this paper, we present a predictor-corrector strategy for constructing rank-adaptive, dynamical low-rank approximations (DLRAs) of matrix-valued ODE systems. The strategy is a compromise between (i) low-rank step-truncation approaches that alternately evolve and compress solutions and (ii) strict DLRA approaches that augment the low-rank manifold using subspaces generated locally in time by the DLRA integrator. The strategy is based on an analysis of the error between a forward temporal update into the ambient full-rank space, which is typically computed in a step-truncation approach before recompressing, and the standard DLRA update, which is forced to live in a low-rank manifold. We use this error, without requiring its full-rank representation, to correct the DLRA solution. A key ingredient for maintaining a low-rank representation of the error is a randomized SVD, which introduces some degree of stochastic variability into the implementation. The strategy is formulated and implemented in the context of discontinuous Galerkin spatial discretizations of PDEs and applied to several versions of DLRA methods found in the literature as well as a new variant. Numerical experiments comparing the predictor-corrector strategy to other methods demonstrate robustness to overcome shortcomings of step truncation or strict DLRA approaches: The former may require more memory than is strictly needed, while the latter may miss transients solution features that cannot be recovered. The effect of randomization, tolerances, and other implementation parameters is also explored.

97 MATHEMATICS AND COMPUTING↗