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At least 739 records · Page 41

Numerical Analysis of 2-D and 3-D MHD Flows Relevant to Fusion Applications

Here, the analysis of many fusion applications such as liquid-metal blankets requires application of computational fluid dynamics (CFD) methods for electrically conductive liquids in geometrically complex regions and in the presence of a strong magnetic field. A current state of the art general purpose CFD code allows modeling of the flow in complex geometric regions, with simultaneous conjugated heat transfer analysis in liquid and surrounding solid parts. Together with a magnetohydrodynamics (MHD) capability, the general purpose CFD code will be a valuable tool for the design and optimization of fusion devices. This paper describes an introduction of MHD capability into the general purpose CFD code CFX, part of the ANSYS Workbench. The code was adapted for MHD problems using a magnetic induction approach. CFX allows introduction of user-defined variables using transport or Poisson equations. For MHD adaptation of the code three additional transport equations were introduced for the components of the magnetic field, in addition to the Poisson equation for electric potential. The Lorentz force is included in the momentum transport equation as a source term. Fusion applications usually involve very strong magnetic fields, with values of the Hartmann number of up to tens of thousands. In this situation a system of MHD equations become very rigid with very large source terms and very strong variable gradients. To increase system robustness, special measures were introduced during the iterative convergence process, such as linearization using source coefficient for momentum equations. The MHD implementation in general purpose CFD code was tested against benchmarks, specifically selected for liquid-metal blanket applications. Results of numerical simulations using present implementation closely match analytical solutions for a Hartmann number of up to 1500 for a 2-D laminar flow in the duct of square cross section, with conducting and nonconducting walls. Results for a 3-D test case are also included.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Volcanic Imprints in Last-Millennium Land Summer Temperatures in the Circum-North Atlantic Area

Summer cooling is one of the most direct consequences of explosive volcanic eruptions that can affect ecosystems and human societies. Recent studies revealed a multiyear cooling impact on hemispheric and global summer temperatures after tropical eruptions, yet the volcanic responses appear to vary on regional scales. Here, in this work, we revisit volcano-induced summer cooling in eastern Canada and northern and central Europe by applying superposed epoch analysis on CMIP6-PMIP4 simulations and millennial temperature reconstructions based on tree-ring density. We then examine potential causes modulating region-specific volcanic impact. While confirming that, on average, tropical eruptions over the last millennium have induced a longer cooling (>4 yr) than eruptions from extratropical Northern Hemisphere in all three North Atlantic regions, we show that the peak magnitude of cooling is stronger in eastern Canada. We also find that the detected volcanic temperature anomalies can be strongly affected by the selection and number of volcanic events and nonvolcanic signals embedded in the climate time series. This study highlights the risks of using highly noisy proxy records to investigate volcanic impacts, especially in regions with strong unforced climate variability. The CMIP6-PMIP4 simulations generally agree with the three reconstructions on the average response to tropical eruptions, but their performance is poorer regarding the production of significant cooling after extratropical eruptions. Our results further suggest that the particular sensitivity to tropical eruptions in eastern Canada is likely related to increased sea ice surrounding Quebec–Labrador associated with the positive Arctic Oscillation and North Atlantic Oscillation formed during the first posteruption winter.

54 ENVIRONMENTAL SCIENCES↗

Comparative genomics of geographically distant Fusarium fujikuroi isolates revealed two distinct pathotypes correlating with secondary metabolite profiles

Fusarium fujikuroi causes bakanae (“foolish seedling”) disease of rice which is characterized by hyper-elongation of seedlings resulting from production of gibberellic acids (GAs) by the fungus. This plant pathogen is also known for production of harmful mycotoxins, such as fusarins, fusaric acid, apicidin F and beauvericin. Recently, we generated the first de novo genome sequence of F. fujikuroi strain IMI 58289 combined with extensive transcriptional, epigenetic, proteomic and chemical product analyses. GA production was shown to provide a selective advantage during infection of the preferred host plant rice. Here, we provide genome sequences of eight additional F. fujikuroi isolates from distant geographic regions. The isolates differ in the size of chromosomes, most likely due to variability of subtelomeric regions, the type of asexual spores (microconidia and/or macroconidia), and the number and expression of secondary metabolite gene clusters. Whilst most of the isolates caused the typical bakanae symptoms, one isolate, B14, caused stunting and early withering of infected seedlings. In contrast to the other isolates, B14 produced no GAs but high amounts of fumonisins during infection on rice. Furthermore, it differed from the other isolates by the presence of three additional polyketide synthase (PKS) genes (PKS40, PKS43, PKS51) and the absence of the F. fujikuroi-specific apicidin F (NRPS31) gene cluster. Analysis of additional field isolates confirmed the strong correlation between the pathotype (bakanae or stunting/withering), and the ability to produce either GAs or fumonisins. Deletion of the fumonisin and fusaric acid-specific PKS genes in B14 reduced the stunting/withering symptoms, whereas deletion of the PKS51 gene resulted in elevated symptom development. Phylogenetic analyses revealed two subclades of F. fujikuroi strains according to their pathotype and secondary metabolite profiles.

59 BASIC BIOLOGICAL SCIENCES↗

The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)

Abstract. Large-scale interaction between the three tropical ocean basins is an area of intense research that is often conducted through experimentation with numerical models. A common problem is that modeling groups use different experimental setups, which makes it difficult to compare results and delineate the role of model biases from differences in experimental setups. To address this issue, an experimental protocol for examining interaction between the tropical basins is introduced. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) consists of experiments in which sea surface temperatures (SSTs) are prescribed to follow observed values in selected basins. There are two types of experiments. One type, called standard pacemaker, consists of simulations in which SSTs are restored to observations in selected basins during a historical simulation. The other type, called pacemaker hindcast, consists of seasonal hindcast simulations in which SSTs are restored to observations during 12-month forecast periods. TBIMIP is coordinated by the Climate and Ocean – Variability, Predictability, and Change (CLIVAR) Research Focus on Tropical Basin Interaction. The datasets from the model simulations will be made available to the community to facilitate and stimulate research on tropical basin interaction and its role in seasonal-to-decadal variability and climate change.

Richter, Ingo↗

2016 Renewable Energy Grid Integration Data Book

The 2016 Renewable Energy Grid Integration Data Book identifies the status, key trends, challenges, and solutions of renewable energy grid integration in a highly visual format. This data book provides an overview of selected key grid integration metrics that represent complex interactions among generation characteristics, market rules, and environmental and safety factors, which may vary by geography, season, and time of day; and presents metrics that either indicate how much variable renewable energy (VRE) is currently being integrated onto the grid, or measure factors that may increase or decrease the challenges associated with integrating VRE generation onto the grid.

variable, renewable, grid, integration, wind, sola↗

Transceivers and receivers for discrete-variable quantum key distribution

Various technologies for performing discrete-variable (DV) quantum key distribution (QKD) with integrated electro-optical circuits are described herein. An integrated DV-QKD system uses Mach-Zehnder modulators (MZMs) to modulate a polarization of photons at a transmitter and select a photon polarization measurement basis at a receiver. A transmitter of a DV-QKD system further uses phase shifters to correct for non-idealities of the MZM in output provided to a polarization beam splitter. A receiver of a DV-QKD system can use phase shifters between a polarization beam splitter and an MZM to correct for non-idealities of the polarization beam splitter and the MZM on the receiver side.

Lentine, Anthony L.↗

Precise determination of the CKM matrix element $\left| V_{cb}\right|$ with $\bar B^0 \to D^{*\,+} \, \ell^- \, \bar ν_\ell$ decays with hadronic tagging at Belle

The precise determination of the CKM matrix element $\left| V_{cb}\right|$ is important for carrying out tests of the flavour sector of the Standard Model. In this article we present a preliminary analysis of the $\bar B^0 \to D^{*\,+} \, \ell^- \, \bar \nu_\ell$ decay mode and its charge conjugate, selected in events that contain a fully reconstructed $B$-meson, using 772 million $e^+ \, e^- \to \Upsilon(4S) \to B \bar B$ events recorded by the Belle detector at KEKB. Unfolded differential decay rates of four kinematic variables fully describing the $\bar B^0 \to D^{*\,+} \, \ell^- \, \bar \nu_\ell$ decay in the $B$-meson rest frame are presented. We measure the total branching fraction $\mathcal{B}( \bar B^0 \to D^{*\,+} \, \ell^- \, \bar \nu_\ell ) = \left(4.95 \pm 0.11 \pm 0.22 \right) \times 10^{-2}$, where the errors are statistical and systematic respectively. The value of $\left|V_{cb} \right|$ is determined to be $\left( 37.4 \pm 1.3 \right) \times 10^{-3}$. Both results are in good agreement with current world averages.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Sampling Size Optimization for Bioburden Density Estimation in Planetary Protection

Planetary protection (PP) is a discipline that focuses on minimizing the biological contamination of spacecraft to ensure compliance with international policy. Precise estimation of bioburden - the total number of microbes in or on spacecraft hardware – and the bioburden density are of utmost importance for PP. Such estimation is the way concordance with requirements is demonstrated, and it is critical for quantifying the potential risk of inadvertently contaminating other planetary bodies. Although a suite of molecular techniques have been used to thoroughly characterize and profile the microbiome of various cleanroom environments and spacecraft, the gold standard remains the physical enumeration of microbes via culturing of samples directly taken from spacecraft and associated surfaces. However, due to technical, budgetary, and programmatic constraints, only a manageable portion (around 10%) of the entire spacecraft surface is directly sampled with cotton swabs or wipes. To generate the bioburden current best estimate (CBE) for components not directly verifiable, the accepted approach is to apply a NASA-defined bioburden estimate based on the components’ manufacturing or assembly environment. This approach utilizes a prespecified bioburden density estimation that applies a maximum value across the total surface area of the specified component. For hardware components that underwent similar assembly processes, an implied bioburden is adopted for all components, based on a direct verification of a representative component within the same lot. Once all components have a CBE, the bioburden estimates are generated. In previous publication [ 1], we have shown that statistical risks quantifying the accuracy of the estimates for sampled, prespecified, and implied components can be derived and ranked. For mean squared error (MSE) function, the risks are available analytically and hence a cost function can be obtained to optimize the risks with respect to the sampling area and sampling cost. Since the sampling area and sampling cost are two complimentary variables, their sum will have a well-defined minimum. This paper presents the multivariate optimization of the integrated risk of an empirical Bayes estimator to determine the optimal sampling schedule for a given number of components. It is assumed that given a number of components, N, the bioburden density for each component can either be sampled, implied, or prespecified. The multivariate optimization searches through different options to sample, imply or prespecify the bioburden density for a component, and account for the component’s surface area and cost of sampling. The idea of the optimization is based on the observation that the statistical risk of using an estimator is a monotonically decreasing function of the sampled area. The larger the sampled area, the lower the risk of using the estimator as the estimator becomes more and more accurate as the sampling area increases. On the other hand, the cost of sampling is monotonically increasing as the sampled surface grows. This makes the risk and total cost of sampling complimentary variables which can be counterbalanced to achieve an optimal overall value with respect to the sampled surface. In this paper, the integrated risk has been used to quantify the accuracy of the estimator. This risk has been selected because it depends on neither the true value of the parameter nor on the collected data. The cost of each sample was also available to obtain the total cost of sampling of N components. The paper will present the results based on computer-simulated data as well as the data collected during the InSight mission. The computer-simulated data have N components with randomly generated total areas and each component assigned to one of the three categories according to the method of estimating of bioburden density: sampled, implied, or prespecified. The cost of sampling is also available. The cost of sampling is estimated based on a cost model provided by the planetary protection group at JPL. For this paper, the overall cost was assumed to be a linear function of exposure. The optimization process finds the allocation of the components to the three categories that minimizes the tradeoff between integrated risk and total cost. For the InSight data, a set of components is selected representing all three categories, and optimization is performed to determine if the performed allocation was optimal or if a better allocation could have been obtained. To the best of our knowledge, this work is the first attempt not only perform an accurate estimation of bioburden density but also do it in an optimal way.

97 - MATHEMATICS AND COMPUTING↗

HARMONY: Large-Scale Architecture Search for Efficient Hybrid Language Models

As large language models scale to trillions of parameters, their computational and memory requirements present critical challenges for efficient training and deployment. While Mixture of Experts (MoE) architectures enable efficient scaling through sparse parameter activation, and state-space models like Mamba offer linear-time complexity, principled methods for combining these paradigms remain undeveloped. We introduce HARMONY (Hybrid Architecture Research for Mamba, Optimized with Neural efficiencY), a multi-objective evolutionary neural architecture search framework for discovering efficient hybrid language models that integrate Transformer attention mechanisms, Mixture-of-Experts routing, and Mamba state-space components. Through large-scale distributed search using 16,384 MI250X GPUs on the Frontier supercomputer, HARMONY explores a comprehensive design space encompassing six attention variants (MHA, MQA, GQA, MLA, SWA, and Mamba-2), variable MoE configurations with both routed and shared experts, and extensive Mamba hyperparameters. Our framework discovers heterogeneous architectures that balance training performance with computational efficiency through multi-objective optimization incorporating latency penalties and fitness-based selection. Analysis of discovered architectures reveals that optimal hybrid designs favor heterogeneous component mixing rather than homogeneous patterns, with Mamba-2 and Multi-Head Latent Attention (MLA) emerging as preferred mechanisms. Discovered architectures demonstrate superior training efficiency: our best configuration achieves a final perplexity of 1.0874 with 2.38B parameters while processing 4,320 tokens/second, outperforming significantly larger manually designed models. Full-scale evaluation shows HARMONY's top architectures achieve better loss trajectories than equivalently-sized models using state-of-the-art configurations including Mixtral, Jamba, and Samba. Additionally, we demonstrate 91% weak scaling efficiency when training discovered 36B-parameter models across 1,024 GPUs. HARMONY is released as an open framework with comprehensive tools for building and training hybrid models using expert-data-pipeline parallelism, democratizing access to automated architecture design for next-generation language models.

Herron, Emily [ORNL] (ORCID:0000000273008172)↗

ESMs Latent Space Exploration for Uncertainty Quantification and Spatiotemporal Downscaling

This final report for DOE Award DE-SC0023044 presents advances in two key areas of climate modeling: (1) representative climate model selection and (2) Earth System Model (ESM) downscaling using hybrid AI methods. The first section introduces a reordered, three-stage workflow to select representative GCM runs that more effectively balance historical skill with ensemble spread, validated across Texas, Bihar, and New York. The second section introduces two novel super-resolution frameworks, ViSIR and ViFOR, that integrate Vision Transformers with sinusoidal and Fourier-based implicit neural representations. These models achieve state-of-the-art reconstruction accuracy for ESM variables including surface temperature and heat fluxes. The report includes detailed methodology, benchmarks, and results, demonstrating significant gains in uncertainty quantification, spatial fidelity, and scalability for climate-impact studies.

54 ENVIRONMENTAL SCIENCES↗

Local Chain Dynamics in Sequence-Controlled Polymers as a Tunable Handle for Rare Earth Sequestration

Chain dynamics govern the intricate behaviors of proteins, underpinning functions such as catalysis, recognition, and stimulus response, and are an increasingly appreciated aspect of structure–function relationships. Analogously, manipulating chain dynamics and structure in abiotic polymers via sequence control is an exciting, yet underexplored, strategy for improving material functions. In this work, we report a systematic study relating the sequence of polymeric sequestrants to their structure and dynamics, as well as to their binding affinity and selectivity for model substrates, rare earth elements (REEs). A series of sequence-controlled polymers with metal chelating, solubilizing, and structure forming monomers was synthesized via multiblock polymerization, yielding compositionally identical polymers with spectroscopically resolved domains and distinct morphologies. Using a combination of small-angle X-ray scattering and 19 F NMR relaxometry measurements, we connected differences in polymer structure and dynamics to polymer sequence variables such as the patchiness (density) of the structure forming monomer and the location of the chelating monomer. Furthermore, we found that, relative to calcium, all polymers in the series collapse more and have slower dynamics when binding REEs (lanthanum and lutetium) , though the extent of these effects were sequence-dependent and localized to specific domains within the polymer. Notably, sequence-controlled polymers that exhibited the largest conformational and dynamic changes upon binding REEs also bound REEs with the greatest affinity and modest selectivity. Collectively, these results correlate monomer patterning with dynamics, morphology, and REE binding performance en route to the development of efficient and selective macromolecular chelators.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Evaluating Moisture Control of Variable-Capacity Heat Pumps in Mechanically Ventilated, Low-Load Homes in Climate Zone 2A

The well-sealed, highly insulated building enclosures constructed by today's home building industry coupled with efficient lighting and appliances are achieving significantly reduced heating and cooling loads. These low-load homes can present a challenge when selecting appropriate space-conditioning equipment. Conventional, fixed-capacity heating and cooling equipment is often oversized for small homes, causing increased first costs and operating costs. Even if fixed-capacity equipment can be properly specified for peak loads, it remains oversized for use during much of the year. During these part-load cooling hours, oversized equipment meets the target dry-bulb temperatures very quickly, often without sufficient opportunity for moisture control. The problem becomes more acute for high-performance houses in humid climates when meeting ASHRAE Standard 62.2 recommendations for wholehouse mechanical ventilation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Dependence of Sum Frequency Generation (SFG) Spectral Features on the Mesoscale Arrangement of SFG-Active Crystalline Domains Interspersed in SFG-Inactive Matrix: A Case Study with Cellulose in Uniaxially Aligned Control Samples and Alkali-Treated Secondary Cell Walls of Plants

Vibrational sum frequency generation (SFG) spectroscopy can selectively detect not only molecules at two-dimensional (2D) interfaces but also noncentrosymmetric domains interspersed in amorphous three-dimensional (3D) matrixes. However, the SFG analysis of 3D systems is more complicated than 2D systems because more variables are involved. One such variable is the distance between SFG-active domains in SFG-inactive matrixes. In this study, we fabricated control samples in which SFG-active cellulose crystals were uniaxially aligned in an amorphous matrix. Assuming uniform separation distances between cellulose crystals, the relative intensities of alkyl (CH) and hydroxyl (OH) SFG peaks of cellulose could be related to the intercrystallite distance. The experimentally measured CH/OH intensity ratio as a function of the intercrystallite distance could be explained reasonably well with a model constructed using the theoretically calculated hyperpolarizabilities of cellulose and the symmetry cancellation principle of dipoles antiparallel to each other. In conclusion, this comparison revealed physical insights into the intercrystallite distance dependence of the CH/OH SFG intensity ratio of cellulose, which can be used to interpret the SFG spectral features of plant cell walls in terms of mesoscale packing of cellulose microfibrils.

09 BIOMASS FUELS↗

Defects Enable Dark Exciton Photoluminescence in Single-Walled Carbon Nanotubes

Variable temperature photoluminescence excitation spectroscopy of three (n,m) species of single-walled carbon nanotubes revealed that at resonant S 22 excitation, in addition to allowed excitonic optical transitions, several sidebands that should be forbidden based on selection rules were observed and appeared to have a strong temperature dependence. In particular, we found that a sideband located approximately 130 meV away from the bright S 11 exciton peak relating to the K-momentum dark exciton state, called X 1 , decreased in intensity five-fold as the samples were cooled. Direct optical excitation of this dark state is nominally forbidden, thus calling into question how the state is populated, and why it is so prominent in the photoluminescence spectrum. Interestingly, the ratio of the integrated photoluminescence intensities of X 1 to S 11 scales with a Boltzmann factor unrelated to the phonon that is thought to be responsible for depopulating the K-momentum dark exciton state: an in-plane transverse optical phonon, A 1 ’. Furthermore, photoluminescence spectra from individual (7,5) nanotubes show that only a small fraction exhibit the X 1 feature, with varying oscillator strength, thus suggesting that intrinsic processes such as phonon scattering are not responsible for populating the dark state. Alternatively, we suggest that populating the K-momentum dark exciton state requires scattering from defects, which is consistent with the increased magnitude of the X 1 feature for samples with increased sample purification and processing. Thus, the presence of an X 1 peak in photoluminescence is an extremely sensitive spectroscopic indicator of defects on single-walled carbon nanotubes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The structure of plastocyanin tunes the midpoint potential by restricting axial ligation of the reduced copper ion

Blue copper proteins are models for illustrating how proteins tune metal properties. Nevertheless, the mechanisms by which the protein controls the metal site remain to be fully elucidated. A hindrance is that the closed shell Cu(I) site is inaccessible to most spectroscopic analyses. Carbon deuterium (C-D) bonds used as vibrational probes afford nonperturbative, selective characterization of the key cysteine and methionine copper ligands in both redox states. The structural integrity of Nostoc plastocyanin was perturbed by disrupting potential hydrogen bonds between loops of the cupredoxin fold via mutagenesis (S9A, N33A, N34A), variably raising the midpoint potential. The C-D vibrations show little change to suggest substantial alteration to the Cu(II) coordination in the oxidized state or in the Cu(I) interaction with the cysteine ligand. They rather indicate, along with visible and NMR spectroscopy, that the methionine ligand distinctly interacts more strongly with the Cu(I) ion, in line with the increases in midpoint potential. Here we show that the protein structure determines the redox properties by restricting the interaction between the methionine ligand and Cu(I) in the reduced state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Fundamental effective temperature measurements for eclipsing binary stars – IV. Selection of new benchmark stars and first results for HD 22064

I describe the selection and initial characterization of 20 eclipsing binary stars that are suitable for calibration and testing of stellar models and data analysis algorithms used by the PLATO mission and spectroscopic surveys. The binary stars selected are F-/G-type dwarf stars with M-type dwarf companions that contribute less than 2 per cent of the flux at optical wavelengths. The light curves typically show well-defined total eclipses with very little variability between the eclipses. I have used near-infrared spectra obtained by the APOGEE survey to measure the spectroscopic orbit for both stars in HD 22064. Combined with an analysis of the TESS light curve, I derive the following masses and radii: M 1 = 1.35 ± 0.03M ⊙ , M 2 = 0.58 ± 0.01M ⊙ , R 1 = 1.554 ± 0.014R ⊙ , R 2 = 0.595 ± 0.008R ⊙ . Using R 1 and the parallax from Gaia EDR3, I find that the primary star’s angular diameter is θ = 0.1035 ± 0.0009 mas. The apparent bolometric flux of the primary star is ${\mathcal {F}}_{{\oplus },0} = (7.51\pm 0.09)\times 10^{-9}$ erg cm -2 s -1 . Hence, this F2V star has an effective temperature $T_{\rm eff,1} = 6763{\rm \, K} \pm 39{\rm \, K}$. HD 22064 is an ideal benchmark star that can be used for ‘end-to-end’ tests of the stellar parameters measured by large-scale spectroscopic surveys or stellar parameters derived from asteroseismology with PLATO. The techniques described here for HD 22064 can be applied to the other eclipsing binaries in the sample in order to create an all-sky network of such benchmark stars.

79 ASTRONOMY AND ASTROPHYSICS↗

First Double-Differential Cross Section Measurement of Neutral-Current 𝜋 0 Production in Neutrino-Argon Scattering in the MicroBooNE Detector

We report the first double-differential cross section measurement of neutral-current neutral pion (NC⁢𝜋 0 ) production in neutrino-argon scattering, as well as single-differential measurements of the same channel in terms of final states with and without protons. The kinematic variables of interest for these measurements are the 𝜋 0 momentum and the 𝜋 0 scattering angle with respect to the neutrino beam. A total of 4971 candidate NC⁢𝜋 0 events fully contained within the MicroBooNE detector are selected using data collected at a mean neutrino energy of ∼0.8 GeV from 6.4 ×10 20 protons on target from the Booster Neutrino Beam at the Fermi National Accelerator Laboratory. After extensive data-driven model validation to ensure unbiased unfolding, the Wiener-singular-value-decomposition method is used to extract nominal flux-averaged cross sections. The results are compared to predictions from commonly used neutrino event generators, which tend to overpredict the measured NC⁢𝜋 0 cross section, especially in the 0.2–0.5 GeV/c 𝜋 0 momentum range and at forward scattering angles. Events with at least one proton present in the final state are also underestimated. This data will help improve the modeling of NC⁢𝜋 0 production, which represents a major background in measurements of charge-parity violation in the neutrino sector and in searches for new physics beyond the standard model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Qubit Assignment Using Time Reversal

As quantum computers with large numbers of qubits become increasingly available, experiments executed on a given device may not utilize all available qubits. In this case, the outcome of executing a quantum program will depend on the ability to efficiently select a subset of high-performing physical qubits. For any given quantum program and device there are many ways to assign physical qubits for execution of the program, and assignments will differ in performance due to the variability in quality across qubits and entangling operations on a single device. Evaluating the performance of each assignment using fidelity estimation introduces significant experimental overhead and will be infeasible for many applications, while relying on standard device benchmarks provides incomplete information about the performance of any specific program. Furthermore, the number of possible assignments grows combinatorially in the number of qubits on the device and in the program, motivating the use of heuristic optimization techniques. We demonstrate a practical solution to the problem of qubit assignment by using simulated annealing with a cost function based on the Loschmidt echo, a diagnostic that measures the reversibility of a quantum process. We provide theoretical justification for this choice of cost function by demonstrating that the optimal qubit assignment coincides with the optimal qubit assignment based on state fidelity in the weak error limit, and we provide experimental justification using diagnostics performed on Google’s superconducting qubit devices. We then establish the performance of simulated annealing for qubit assignment using classical simulations of noisy devices as well as optimization experiments performed on a quantum processor. Our results demonstrate that the use of Loschmidt echoes and simulated annealing provides a scalable and flexible approach to optimizing qubit assignment on near-term hardware.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗