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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 1,801 records · Page 100
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Large-scale molecular dynamics simulations of bubble collapse in water: Effects of system size, water model, and nitrogen

Molecular dynamics simulations in the microcanonical ensemble are performed to study the collapse of a bubble in liquid water using the single-site mW and the four-site TIP4P/2005 water models. To study system size effects, simulations for pure water systems are performed using periodically replicated simulation boxes with linear dimensions, L, ranging from 32 to 512 nm with the largest systems containing 8.7 × 10 6 and 4.5 × 10 9 molecules for the TIP4P/2005 and mW water models, respectively. The computationally more efficient mW water model allows us to reach converging behavior when the bubble dynamics results are plotted in reduced units, and the limiting behavior can be obtained through linear extrapolation in L –1 . Qualitative differences are observed between simulations with the mW and TIP4P/2005 water models, but they can be explained by the models’ differences in predicted viscosity and surface tension. Although bubble collapse occurs on time scales of only hundreds of picoseconds, the system sizes used here are sufficiently large to obtain bubble dynamics consistent with the Rayleigh–Plesset equation when using the models’ thermophysical properties as input. For the conditions explored here, extreme heating of the interfacial water molecules near the time of collapse is observed for the larger mW water systems (but the model underpredicts the viscosity), whereas heating is less pronounced for the TIP4P/2005 water systems because its larger viscosity contribution slows the collapse dynamics. The presence of nitrogen within the bubble only starts to affect bubble dynamics near the very end of the initial collapse, leading to an incomplete collapse and strong rebound for the mW water model. Although nitrogen is non-condensable at 300 K, it becomes highly compressed and reaches a liquid-like density near the collapse point. We find that the dissolution of nitrogen is much slower than the movement of the collapsing water front, and the re-expansion of the dense nitrogen droplet gives rise to bubble rebound. The incompatibility of the collapse and dissolution time scales should be considered for continuum-scale modeling of bubble dynamics. Finally, we also confirm that the diffusion coefficient for dissolved nitrogen is insensitive to pressure as the liquid transitions from a compressed to a stretched state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Magnetized liner inertial fusion platform development to assess performance scaling with drive parameters

Magnetized liner inertial fusion (MagLIF) experiments have demonstrated fusion-relevant ion temperatures up to 3.1 keV and thermonuclear production of up to 1.1 × 1013 deuterium–deuterium neutrons. This performance was enabled through platform development that provided increases in applied magnetic field, coupled preheat energy, and drive current. Advanced coil designs with internal reinforcement enabled an increase from 10 to 20 T. An improved laser pulse shape, beam smoothing, and thinner laser entrance foils increased preheat energy coupling from less than 1 to 2.3 kJ. A redesign of the final transmission line and load region increased peak load current from 16 to 20 MA. The wider range of input parameters was leveraged to study target performance trends with preheat energy, applied magnetic field, and peak load current. Ion temperature and neutron yield generally followed trends in two-dimensional clean Lasnex calculations. Stagnation performance improved with peak load current when other input parameters were also increased such that convergence was maintained. This dataset suggests that reducing convergence to less than 30 would improve predictability of target performance. Lasnex was used to identify a simulation-optimized scaling path, which suggests 10+ kJ of fusion yield is possible on the Z facility with achievable input parameters. This path also indicates >10 MJ could be generated through volume burn on a future facility with a path to high yield (>200 MJ) using cryogenic dense fuel layers. The newly developed MagLIF platform enables exploration of both this simulation optimized scaling path and a recently developed similarity-scaling path.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Exploring the origins of the indentation size effect at submicron scales

Significance Downscaling material structure into ever-decreasing levels for modern technological advancements demands small-scale mechanical characterizations of materials, such as nanoindentation. Thus, a comprehensive understanding of indentation size effect (ISE) has a far-reaching impact, but such understanding remains incomplete at the submicron scale despite a decades-long quest since the 1980s. Here, we explored the origins of this effect experimentally by linking dislocation behavior evolution with nanoindentation mechanical data at various depths. Importantly, the dislocation behavior is found to be strongly depth dependent and gives rise to subgrain formation progressively. Aided by subgrain boundaries, the ISE mechanism transitions from dislocation source starvation to dislocation interaction as indentation depth increases. The critical mechanism transition elucidates the distinctive ISE behavior at the submicron scale.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unified understanding of the impact of semiflexibility, concentration, and molecular weight on macromolecular-scale ring diffusion

Conformationally fluctuating, globally compact macromolecules such as polymeric rings, single-chain nanoparticles, microgels, and many-arm stars display complex dynamic behaviors due to their rich topological structure and intermolecular organization. Synthetic rings are hybrid objects with conformations that display both ideal random walk and compact globular features, which can serve as models of genomic DNA. To date, emphasis has been placed on the effect of ring molecular weight on their unusual behaviors. Here, we combine simulations and a microscopic force-level theory to build a unified understanding for how key aspects of ring dynamics depend on different tunable molecular properties including backbone rigidity, monomer concentration, degree of traditional entanglement, and molecular weight. Our large-scale molecular dynamics simulations of ring melts with very different backbone stiffnesses reveal unanticipated behaviors which agree well with our generalized theory. This includes a universal master curve for center-of-mass diffusion constants as a function of molecular weight scaled by a chemistry and thermodynamic state-dependent critical molecular weight that generalizes the concept of an entanglement cross-over for linear chains. The key physics is how backbone rigidity and monomer concentration induced changes of the entanglement length, interring packing, degree of interpenetration, and liquid compressibility slow down space-time dynamic-force correlations on macromolecular scales. A power law decay of the center-of-mass diffusion constant with inverse molecular weight squared is the first consequence, followed by an ultraslow activated hopping transport regime. Our results set the stage to address slow dynamics and kinetic arrest in different families of compact synthetic and biological polymeric systems.

Science & Technology - Other Topics↗
DOE OSTI2022

Continuous-Energy ENDF/B-VIII.0 Cross Section and SCALE 6.2.4 Performance for Nuclear Criticality Safety Applications: 1 H, C, 58,60 Ni, 182,183,184,186 W, 235,238 U, 239 Pu

As part of the nuclear data evaluation and validation cycle, the ENDF/B-VIII.0 cross-section library released in 2018 requires testing to determine areas of improvement and deterioration. Previous work by the authors investigated the performance of 16 O, 56 Fe, and 63,65 Cu cross sections, with this study acting as an extension of the prior work. In addition to the isotopes and nuclear criticality safety benchmarks of interest to the prior work, benchmarks from the International Criticality Safety Benchmark Evaluation Project Handbook were selected for their k eff sensitivity to 1 H, C, 58,60 Ni, 182,183,184,186 W, 235,238U, or 239 Pu cross sections and were modeled in the SCALE code system maintained by Oak Ridge National Laboratory. In total, 253 benchmark configurations were selected for their sensitivities and modeled using SCALE 6.2.4 Criticality Safety Analysis Sequences (CSAS) continuous-energy Monte Carlo k eff calculations. This collection includes and expands upon the 99 benchmarks in the prior work. The AMPX-processed ENDF/B-VIII.0 library was decomposed into individual ENDF/B-VIII.0 datum libraries for each isotope of interest. Doing so allowed for the individual substitution of an ENDF/B-VIII.0 cross section in the place of ENDF/B-VII.1, determining isotope-specific effects of ENDF/B-VIII.0 relative to ENDF/B-VII.1. Full library calculations with entirely ENDF/B-VII.1 data or entirely ENDF/B-VIII.0 data were also executed. As a measure of performance, the average relative deviation was determined as the ratio of the deviation between calculated and experimental keff to the propagated calculational and experimental uncertainty. With calculated full library and isotope-specific ENDF/B-VIII.0 k eff ’s, an optimized combination of data libraries was estimated and confirmed with SCALE calculations. This showed that reverting 239 Pu, 58 Ni, 16 O, and 65 Cu cross sections to ENDF/B-VII.1 resulted in improved performance relative to the full ENDF/B-VIII.0 library. Across all 253 benchmarks, the average relative deviation was 1.29σ for the full ENDF/B-VII.1 library, 1.17σ for the full ENDF/B-VIII.0 library, and 0.97σ for the optimized combination. The reversion of 239 Pu, 58 Ni, 16 O, and 65 Cu cross sections to ENDF/B-VII.1 in the 99 benchmarks of the prior work resulted in further improved experimental agreement compared to the previously reported improvement from 16 O and 65 Cu alone. Therefore, it is suggested that applications with significant sensitivities to 239 Pu, 58 Ni, 16 O, and 65 Cu consider their choice of nuclear data library.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Estimating production cost for large-scale seaweed farms

Seaweed farming has the potential to produce feedstocks for many applications, including food, feeds, fertilizers, biostimulants, and biofuels. Seaweeds have advantages over land-based biomass in that they require no freshwater inputs and no allocation of arable land. To date, seaweed farming has not been practiced at scales relevant to meaningful biofuel production. Here we describe a techno-economic model of large-scale seaweed farms and its application to the cultivation of the cool temperate species Saccharina latissima (sugar kelp) and the tropical seaweed Eucheumatopsis isiformis. At farm scales of 1000 ha or more, our model suggests that farm gate production costs in waters up to 200 km from the onshore support base are likely to range between $\$$200 and $\$$300 per dry tonne. The model also suggests that production costs below $\$$100 per dry tonne may be achievable in some settings, which would make these seaweeds economically competitive with land-based biofuel feedstocks. While encouraging, these model results and some assumptions on which they are based require further field validation.

09 BIOMASS FUELS↗
DOE OSTI2023

An autoencoder compression approach for accelerating large-scale inverse problems

Abstract Partial differential equation (PDE)-constrained inverse problems are some of the most challenging and computationally demanding problems in computational science today. Fine meshes required to accurately compute the PDE solution introduce an enormous number of parameters and require large-scale computing resources such as more processors and more memory to solve such systems in a reasonable time. For inverse problems constrained by time-dependent PDEs, the adjoint method often employed to compute gradients and higher order derivatives efficiently requires solving a time-reversed, so-called adjoint PDE that depends on the forward PDE solution at each timestep. This necessitates the storage of a high-dimensional forward solution vector at every timestep. Such a procedure quickly exhausts the available memory resources. Several approaches that trade additional computation for reduced memory footprint have been proposed to mitigate the memory bottleneck, including checkpointing and compression strategies. In this work, we propose a close-to-ideal scalable compression approach using autoencoders to eliminate the need for checkpointing and substantial memory storage, thereby reducing the time-to-solution and memory requirements. We compare our approach with checkpointing and an off-the-shelf compression approach on an earth-scale ill-posed seismic inverse problem. The results verify the expected close-to-ideal speedup for the gradient and Hessian-vector product using the proposed autoencoder compression approach. To highlight the usefulness of the proposed approach, we combine the autoencoder compression with the data-informed active subspace (DIAS) prior showing how the DIAS method can be affordably extended to large-scale problems without the need for checkpointing and large memory.

Mathematics↗
DOE OSTI2021

Relativistically transparent magnetic filaments: scaling laws, initial results and prospects for strong-field QED studies

Abstract Relativistic transparency enables volumetric laser interaction with overdense plasmas and direct laser acceleration of electrons to relativistic velocities. The dense electron current generates a magnetic filament with field strength of the order of the laser amplitude (>10 5 T). The magnetic filament traps the electrons radially, enabling efficient acceleration and conversion of laser energy into MeV photons by electron oscillations in the filament. The use of microstructured targets stabilizes the hosing instabilities associated with relativistically transparent interactions, resulting in robust and repeatable production of this phenomenon. Analytical scaling laws are derived to describe the radiated photon spectrum and energy from the magnetic filament phenomenon in terms of the laser intensity, focal radius, pulse duration, and the plasma density. These scaling laws are compared to 3D particle-in-cell (PIC) simulations, demonstrating agreement over two regimes of focal radius. Preliminary experiments to study this phenomenon at moderate intensity ( a 0 ∼ 30) were performed on the Texas Petawatt Laser. Experimental signatures of the magnetic filament phenomenon are observed in the electron and photon spectra recorded in a subset of these experiments that is consistent with the experimental design, analytical scaling and 3D PIC simulations. Implications for future experimental campaigns are discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Quantum utility-scale error mitigation for quantum quench dynamics in Heisenberg spin chains

Here, we implement a quantum error mitigation method termed self-mitigation, which is comparable to zero-noise extrapolation, at large scales to achieve quantum utility on near-term, noisy quantum computers. We investigate the effectiveness of several quantum error mitigation strategies, including self-mitigation, by simulating quantum quench dynamics for Heisenberg spin chains with system sizes up to 104 qubits using IBM quantum processors. In particular, we discuss the limitations of zero-noise extrapolation and the advantages offered by self-mitigation at large scales. The self-mitigation method demonstrates stable accuracy with large systems of 104 qubits comprising more than 3,000 CNOT gates. Also, we combine the discussed quantum error mitigation methods with practical entanglement entropy measuring methods, and it shows a good agreement with the theoretical estimation. Our study illustrates the usefulness of near-term noisy quantum hardware in examining the quantum quench dynamics of many-body systems at large scales and lays the groundwork for surpassing classical simulations with quantum methods prior to the development of fault-tolerant quantum computers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The cosmological analysis of the SDSS/BOSS data from the Effective Field Theory of Large-Scale Structure

The Effective Field Theory of Large-Scale Structure is a formalism that allows us to predict the clustering of Cosmological Large-Scale Structure in the mildly non-linear regime in an accurate and reliable way. In this paper, after validating our technique against several sets of numerical simulations, we perform the analysis for the cosmological parameters of the DR12 BOSS data. We assume Λ CDM, a fixed value of the baryon/dark-matter ratio, Ω b /Ω c , and of the tilt of the primordial power spectrum, n s , and no significant input from numerical simulations. By using the one-loop power spectrum multipoles, we measure the primordial amplitude of the power spectrum, A s , the abundance of matter, Ω m , and the Hubble parameter, H 0 , {to about 13% , 3.2% and 3.2% respectively, obtaining ln (10 10 A s )=2.72± 0.13 , 0Ω m =0.309± 0.01 , H 0 =68.5± 2.2 km/(s Mpc) at 68% confidence level. If we then add a CMB prior on the sound horizon, the error bar on H 0 is reduced to 1.6% .} These results are a substantial qualitative and quantitative improvement with respect to former analyses, and suggest that the EFTofLSS is a powerful instrument to extract cosmological information from Large-Scale Structure.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The persistence of large scale structures. Part I. Primordial non-Gaussianity

Abstract We develop an analysis pipeline for characterizing the topology of large scale structure and extracting cosmological constraints based on persistent homology . Persistent homology is a technique from topological data analysis that quantifies the multiscale topology of a data set, in our context unifying the contributions of clusters, filament loops, and cosmic voids to cosmological constraints. We describe how this method captures the imprint of primordial local non-Gaussianity on the late-time distribution of dark matter halos, using a set of N-body simulations as a proxy for real data analysis. For our best single statistic, running the pipeline on several cubic volumes of size 40 (Gpc/h) 3 , we detect f NL loc =10 at 97.5% confidence on ~ 85% of the volumes. Additionally wetest our ability to resolve degeneracies betweenthe topological signature of f NL loc and variation of σ 8 and argue that correctly identifying nonzero f NL loc in this case is possible via an optimal template method. Our method relies on information living at $\mathcal{O}$(10) Mpc/h, a complementary scale with respect to commonly used methods such as the scale-dependent bias in the halo/galaxy power spectrum. Therefore, while still requiring a large volume, our method does not require sampling long-wavelength modes to constrain primordial non-Gaussianity. Moreover, our statistics are interpretable: we are able to reproduce previous results in certain limits and we make new predictions for unexplored observables, such as filament loops formed by dark matter halos in a simulation box.

Astronomy & Astrophysics↗
DOE OSTI2022

Neural network acceleration of large-scale structure theory calculations

Here, we make use of neural networks to accelerate the calculation of power spectra required for the analysis of galaxy clustering and weak gravitational lensing data. For modern perturbation theory codes, evaluation time for a single cosmology and redshift can take on the order of two seconds. In combination with the comparable time required to compute linear predictions using a Boltzmann solver, these calculations are the bottleneck for many contemporary large-scale structure analyses. Here, we construct neural network-based surrogate models for Lagrangian perturbation theory (LPT) predictions of matter power spectra, real and redshift space galaxy power spectra, and galaxy-matter cross power spectra that attain ~ 0.1% (at one sigma) accuracy over a broad range of scales in a ωCDM parameter space. The neural network surrogates can be evaluated in approximately one millisecond, a factor of 1000 times faster than the full Boltzmann code and LPT computations. In a simulated full-shape redshift space galaxy power spectrum analysis, we demonstrate that the posteriors obtained using our surrogates are accurate compared to those obtained using the full LPT model. We make our surrogate models public at https://github.com/sfschen/EmulateLSS, so that others may take advantage of the speed gains they provide to enable rapid iteration on analysis settings, something that is essential in complex contemporary large-scale structure analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Mitigation of DESI fiber assignment incompleteness effect on two-point clustering with small angular scale truncated estimators

We present a method to mitigate the effects of fiber assignment incompleteness in two-point power spectrum and correlation function measurements from galaxy spectroscopic surveys, by truncating small angular scales from estimators. We derive the corresponding modified correlation function and power spectrum windows to account for the small angular scale truncation in the theory prediction. We validate this approach on simulations reproducing the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) with and without fiber assignment. We show that we recover unbiased cosmological constraints using small angular scale truncated estimators from simulations with fiber assignment incompleteness, with respect to standard estimators from complete simulations. Additionally, we present an approach to remove the sensitivity of the fits to high k modes in the theoretical power spectrum, by applying a transformation to the data vector and window matrix. We find that our method efficiently mitigates the effect of fiber assignment incompleteness in two-point correlation function and power spectrum measurements, at low computational cost and with little statistical loss.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Testing predictions of electron scale turbulent pedestal transport in two DIII-D ELMy H-modes

In this study, we present linear and nonlinear gyrokinetic analyses in the pedestal region of two DIII-D ELMy H-mode discharges using the CGYRO code. The otherwise matched discharges employ different divertor configurations to investigate the impact of varying recycling and particle source on pedestal profiles. Linear gyrokinetic simulations find electrostatic ion-scale instabilities (ion temperature gradient and trapped electron modes, ITG-TEM) are present just inside the top of the pedestal with growth rates that are enhanced significantly by parallel velocity shear. In the sharp gradient region, E x B shearing rates are comparable or larger than ion scale growth rates, suggesting the suppression of ITG-TEM modes in this region. Instead, the electron temperature profiles are found to be correlated with and just above the electron temperature gradient (ETG) instability thresholds. Using gradients varied within experimental uncertainties, nonlinear electron-scale gyrokinetic simulations predict electron heat fluxes from ETG turbulence, that when added to neoclassical ion thermal transport simulated by NEO, account for 30-60% of the total experimental heat flux. In addition, the neoclassical electron particle flux is found to contribute significantly to the experimental fluxes inferred from SOLPS-ITER analysis. Additional nonlinear gyrokinetic simulations are run varying input gradients to develop a threshold-based reduced model for ETG transport, finding a relatively simple dependence on η e = L ne /L Te . Predictive transport simulations are used to validate this pedestal-specific ETG model, in conjunction with a model for neoclassical particle transport. In both discharges, the predicted electron temperatures are always overpredicted, indicative of the insufficient stiffness in the ETG pedestal model to account for all of the experimental electron thermal transport. In the case of the closed divertor discharge with lower particle source, the predicted electron density is close to the experiment, consistent with the magnitude of neoclassical particle transport in that discharge. However, the density profiles are overpredicted in the open divertor discharge (larger particle source), due to insufficient model transport. In conclusion, the implications for other mechanisms accounting for the remainder of transport in the sharp gradient region in the two discharges are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Physics insights from a large-scale 2D UEDGE simulation database for detachment control in KSTAR

A large-scale database of two-dimensional UEDGE simulations has been developed to study detachment physics in KSTAR and to support surrogate models for control applications. Nearly 70 000 steady-state solutions were generated, systematically scanning upstream density, input power, plasma current, impurity fraction, and anomalous transport coefficients, with magnetic and electric drifts across the magnetic field included. The database identifies robust detachment indicators, with strike-point electron temperature at detachment onset consistently T e,target ~ 3-4 eV, largely insensitive to upstream conditions. Scaling relations reveal weaker impurity sensitivity than one-dimensional models and show that heat flux widths follow Eich’s scaling only for uniform, low D and χ. Distinctive in–out divertor asymmetries are observed in KSTAR, differing qualitatively from DIII-D. Complementary time-dependent simulations quantify plasma response to gas puffing, with delays of 5 - 15 ms at the outer strike point and ∼ 40 ms for the low-magnetic-field-side radiation front. These dynamics are well captured by first-order-plus-dead-time models and are consistent with experimentally observed detachment-control behavior in KSTAR (Gupta et al 2025 Plasma Phys. Control. Fusion (submitted)).

Physics - Plasma physics↗
DOE OSTI2023

Linking repeat lidar with Landsat products for large scale quantification of fire-induced permafrost thaw settlement in interior Alaska

The permafrost–fire–climate system has been a hotspot in research for decades under a warming climate scenario. Surface vegetation plays a dominant role in protecting permafrost from summer warmth, thus, any alteration of vegetation structure, particularly following severe wildfires, can cause dramatic top–down thaw. A challenge in understanding this is to quantify fire-induced thaw settlement at large scales (>1000 km 2 ). In this study, we explored the potential of using Landsat products for a large-scale estimation of fire-induced thaw settlement across a well-studied area representative of ice-rich lowland permafrost in interior Alaska. Six large fires have affected ~1250 km 2 of the area since 2000. We first identified the linkage of fires, burn severity, and land cover response, and then developed an object-based machine learning ensemble approach to estimate fire-induced thaw settlement by relating airborne repeat lidar data to Landsat products. The model delineated thaw settlement patterns across the six fire scars and explained ~65% of the variance in lidar-detected elevation change. Our results indicate a combined application of airborne repeat lidar and Landsat products is a valuable tool for large scale quantification of fire-induced thaw settlement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

The birth of a field through a marriage of scales: urban meteorological modeling meets regional climate modeling

Urban meteorological modeling and regional climate modeling developed largely independently, with each discipline addressing different aspects of atmospheric processes across space and time. In this review, I show that urban climate modeling did not arise as a simple scaling extension of urban meteorological modeling or an add-on to regional climate modeling, but instead emerged through the selective inheritance of complementary strengths from its parents after both disciplines reached sufficient methodological maturity. By tracing the parallel evolution of these disciplines, I demonstrate that urban climate modeling inherited the physically explicit treatment of the built environment developed within urban meteorological modeling, and the hierarchical scale translation and climatological framing that matured within regional climate modeling, allowing urban effects to influence climate-relevant outcomes. Building on this synthesis, I propose an objective, and methodologically-grounded definition of urban climate modeling that distinguishes it from urban meteorological modeling. This distinction is increasingly important as urban climate data informs decisions with long-lived societal consequences, and as ambiguity in terminology risks conflating fundamentally different modeling frameworks with distinct physical meaning and decision relevance. In addition to a clarifying definition, this review outlines a research framework for advancing urban climate modeling through scale-aware coupling strategies that preserve physically consistent urban-atmosphere interactions.

regional climate↗
DOE OSTI2026

Classical combinatorial optimization scaling for random Ising models on 2D heavy-hex graphs

Motivated by near term quantum computing hardware limitations, combinatorial optimization problems that can be addressed by current quantum algorithms and noisy hardware with little or no overhead are used to probe capabilities of quantum algorithms such as the quantum approximate optimization algorithm. In this study, a specific class of near term quantum computing hardware defined combinatorial optimization problems, Ising models on heavy-hex graphs both with and without geometrically local cubic terms, are examined for their classical computational hardness via empirical computation time scaling quantification. Specifically the time-to-solution (TTS) metric using the classical heuristic simulated annealing is measured for finding optimal variable assignments (ground states), as well as the time required for the optimization software Gurobi to find an optimal variable assignment. Because of the sparsity of these Ising models, the classical algorithms are able to find optimal solutions efficiently even for large instances (i.e. 100 000 spin variables). The Ising models both with and without geometrically local cubic terms exhibit average-case linear-time or weakly quadratic scaling when solved exactly using Gurobi, and the Ising models with no cubic terms show evidence of exponential-time TTS scaling when sampled using simulated annealing. These findings point to the necessity of developing and testing more complex, namely more densely connected, optimization problems in order for quantum computing to ever have a practical advantage over classical computing. Our results are another illustration that different classical algorithms can indeed have exponentially different running times, thus making the identification of the best practical classical technique important in any quantum computing vs. classical computing comparison.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

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