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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Projection pursuit adaptation on polynomial chaos expansions

Here, the present work addresses the issue of accurate stochastic approximations in high-dimensional parametric space using tools from uncertainty quantification (UQ). The basis adaptation method and its accelerated algorithm in polynomial chaos expansions (PCE) were recently proposed to construct low-dimensional approximations adapted to specific quantities of interest (QoI). The present paper addresses one difficulty with these adaptations, namely their reliance on quadrature point sampling, which limits the reusability of potentially expensive samples. Projection pursuit (PP) is a statistical tool to find the “interesting” projections in high-dimensional data and thus bypass the curse-of-dimensionality. In the present work, we combine the fundamental ideas of basis adaptation and projection pursuit regression (PPR) to propose a novel method to simultaneously learn the optimal low-dimensional spaces and PCE representation from given data. While this projection pursuit adaptation (PPA) can be entirely data-driven, the constructed approximation exhibits mean-square convergence to the solution of an underlying governing equation and thus captures the supports and probability distributions associated with the physics constraints. The proposed approach is demonstrated on a borehole problem and a structural dynamics problem, demonstrating the versatility of the method and its ability to discover low-dimensional manifolds with high accuracy with limited data. In addition, the method can learn surrogate models for different quantities of interest while reusing the same data set.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Active Learning in the Era of Big Data

Active learning methods automatically adapt data collection by selecting the most informative samples in order to accelerate machine learning. Because of this, real-world testing and comparing active learning algorithms requires collecting new datasets (adaptively), rather than simply applying algorithms to benchmark datasets, as is the norm in (passive) machine learning research. To facilitate the development, testing and deployment of active learning for real applications, we have built an open-source software system for large-scale active learning research and experimentation. The system, called NEXT, provides a unique platform for realworld, reproducible active learning research. This paper details the challenges of building the system and demonstrates its capabilities with several experiments. The results show how experimentation can help expose strengths and weaknesses of active learning algorithms, in sometimes unexpected and enlightening ways.

Jamieson, Kevin↗
DOE OSTI2014

Rapid determination of 226 Ra in emergency urine samples

A new method has been developed at the Savannah River National Laboratory (SRNL) that can be used for the rapid determination of 226 Ra in emergency urine samples following a radiological incident. If a radiological dispersive device event or a nuclear accident occurs, there will be an urgent need for rapid analyses of radionuclides in urine samples to ensure the safety of the public. Large numbers of urine samples will have to be analyzed very quickly. This new SRNL method was applied to 100 mL urine aliquots, however this method can be applied to smaller or larger sample aliquots as needed. The method was optimized for rapid turnaround times; urine samples may be prepared for counting in <3 h. A rapid calcium phosphate precipitation method was used to pre-concentrate 226 Ra from the urine sample matrix, followed by removal of calcium by cation exchange separation. A stacked elution method using DGA Resin was used to purify the 226 Ra during the cation exchange elution step. This approach combines the cation resin elution step with the simultaneous purification of 226 Ra with DGA Resin, saving time. 133 Ba was used instead of 225 Ra as tracer to allow immediate counting; however, 225 Ra can still be used as an option. The rapid purification of 226 Ra to remove interferences using DGA Resin was compared with a slightly longer Ln Resin approach. A final barium sulfate micro-precipitation step was used with isopropanol present to reduce solubility; producing alpha spectrometry sources with peaks typically <40 keV FWHM (full width half max). This new rapid method is fast, has very high tracer yield (>90 %), and removes interferences effectively. The sample preparation method can also be adapted to ICP-MS measurement of 226 Ra, with rapid removal of isobaric interferences.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Receptance coupling substructure analysis and chatter frequency-informed machine learning for milling stability

This paper describes a milling stability identification approach that simultaneously considers: physics-based models for the tool tip frequency response functions and stability predictions; the binary result from a milling test (automatically labeled as stable or unstable based on frequency content); chatter frequency when an unstable result is obtained; and user risk tolerance. The algorithm applies probabilistic Bayesian machine learning with adaptive, parallelized Markov Chain Monte Carlo sampling to update the probability of stability with each milling test. Furthermore, the result is a robust solution for rapid convergence to optimized milling parameters for maximum metal removal rate using all available information.

42 ENGINEERING↗
DOE OSTI2026

Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials

Silicon carbide (SiC) divacancies are attractive candidates for spin-defect qubits possessing long coherence times and optical addressability. The high activation barriers associated with SiC defect formation and motion pose challenges for their study by first-principles molecular dynamics. In this work, we develop and deploy machine learning interatomic potentials (MLIPs) to accelerate defect dynamics simulations while retaining ab initio accuracy. We employ an active learning strategy comprising symmetry-adapted collective variable discovery and enhanced sampling to compile configurationally diverse training data, calculation of energies and forces using density functional theory (DFT), and training of an E(3)-equivariant MLIP based on the Allegro model. Here, the trained MLIP reproduces DFT-level accuracy in defect transition activation free energy barriers, enables the efficient and stable simulation of multidefect 216-atom supercells, and permits an analysis of the temperature dependence of defect thermodynamic stability and formation/annihilation kinetics to propose an optimal annealing temperature to maximally stabilize VV divacancies.

Computer simulations↗
DOE OSTI2020

Dynamic PRA-Based Estimation of PWR Coping Time Using a Surrogate Model for Accident Tolerant Fuel

In this study, we propose an interpolation-based response surface surrogate methodology to manage a large number of scenarios in dynamic probabilistic risk assessment. It adopts the shape Dynamic Time Warping algorithm to cluster the interpolation neighborhood from time series sample data. The interpolation method was adapted from Taylor Kriging to allow a reduced-order model of the Taylor series. In order to demonstrate its applicability to complex issues in risk assessment for nuclear engineering, an example risk response surface to estimate emergency core cooling system (ECCS) criteria for triplex silicon carbide (SiC) accident-tolerant fuel was constructed. The response surface was exploited to estimate the cumulative failure probability of the fuel cladding structure due to the uncertainties in operator actions and safety systems. The functional failures were assessed based on a combination of individual layer failures computed by coupling Risk Analysis Virtual Environment software with a pressurized water reactor 1000-MW(electric) RELAP5 model and the in-house fuel performance assessment module. Results showed that SiC cladding failure probability spiked less than 1 min after a large-break loss-of- coolant accident whenever the current ECCS criteria for Zircaloy-4 (Zr-4) cladding was used. However, it still provides an increased safety margin of three orders of magnitude compared to Zr-4. This positive margin could be utilized to relax active ECCS requirements by allowing deviations of up to 450 s in its actuation time. The proposed surrogate methodology generated a response surface of SiC cladding failure probability reasonably well, with a significant savings of computation time. This methodology is expected to be useful in the analysis of system response with complex uncertainty sources.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

A novel digital pulse processing architecture for nuclear instrumentation

The field of nuclear instrumentation covers a wide range of applications, including counting, spectrometry, pulse shape discrimination and multi-channel coincidence. These applications are the topic of many researches, new algorithms and implementations are constantly proposed thanks to advances in digital signal processing. However, these improvements are not yet implemented in instrumentation devices. This is especially true for neutron-gamma discrimination applications which traditionally use charge comparison method while literature proposes other algorithms based on frequency domain or wavelet theory which show better performances. Another example is pileups which are generally rejected while pileup correction algorithms also exist. These processes are traditionally performed offline due to two issues. The first is the Poissonian characteristic of the signal, composed of random arrival pulses which requires to current architectures to work in data flow. The second is the real-time requirement, which implies losing pulses when the pulse rate is too high. Despite the possibility of treating the pulses independently from each other, current architectures paralyze the acquisition of the signal during the processing of a pulse. This loss is called dead-time. These two issues have led current architectures to use dedicated solutions based on re-configurable components like Field Programmable Gate Arrays (FPGAs) to overcome the need of performance necessary to deal with dead-time. However, dedicated hardware algorithm implementations on re-configurable technologies are complex and time-consuming. For all these reasons, a programmable Digital pulse Processing (DPP) architecture in a high level language such as Cor C++ which can reduce dead-time would be worthwhile for nuclear instrumentation. This would reduce prototyping and test duration by reducing the level of hardware expertise to implement new algorithms. However, today's programmable solutions do not meet the need of performance to operate online and not allow scaling with the increase in the number of measurement channel. That is why an innovative DPP architecture is proposed in this paper. This architecture is able to overcome dead-time while being programmable and is flexible with the number of measurement channel. Proposed architecture is based on an innovative execution model for pulse processing applications which can be summarized as follow. The signal is not composed of pulses only, consequently, pulses processing does not have to operate on the entire signal. Therefore, the first step of our proposal is pulse extraction by the use of dedicated components named pulse extractors. The triggering step can be achieved after the analog-to-digital conversion without any signal shaping or filtering stages. Pileup detection and accurate pulse time stamping are done at this stage. Any application downstream this step can work on adaptive variable-sized array of samples simplifying pulse processing methods. Then, once the data flow is broken, it is possible to distribute pulses on Functional Units (FUs) which perform processing. As the date of each pulse is known, they can be processed individually out-of-order to provide the results. To manage the pulses distribution, a scheduler and an interconnection network are used. pulses are distributed on the first FU which is not busy without congesting the interconnection network. For this reason, the process duration does not result anymore in dead-time if there are enough FUs. FUs are designed to be standalone and to comprises at least a programmable general purpose processor (ARM, Microblaze) allowing the implementation of complex algorithms without any modification of the hardware. An acquisition chain is composed of a succession of algorithms which lead to organize our FUs as a software macro-pipeline, A simple approach consists in assigning one algorithm per FU. Consequently, the global latency becomes the worst latency of algorithms execution on FU. Moreover, as algorithms are executed locally - i.e. on a FU - this approach limits shared memory requirement. To handle multichannel, we propose FUs sharing, this approach maximize the chance to find a non-busy FU to process an incoming pulse. This is possible since each channel receive random event independently, the pulse extractors associated to them do not necessarily need to access simultaneously to all Computing resources at the same time to distribute their pulses. The major contribution of this paper is the proposition of an execution model and its associated hardware programmable architecture for digital pulse processing that can handle multiple acquisition channels while maintaining the scalability thanks to the use of shared resources. This execution model and associated architecture are validated by simulation of a cycle accurate architecture SystemC model. Proposed architecture shows promising results in terms of scalability while maintaining zero dead-time. This work also permit the sizing of hardware resources requirement required for a predefined set of applications. Future work will focus on the interconnection network and a scheduling policy that can exploit the variable-length of pulses. Then, the hardware implementation of this architecture will be performed and tested for a representative set of application.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Targeted quantification of functional enzyme dynamics in environmental samples for microbially mediated biogeochemical processes: Targeted quantification of functional enzyme dynamics

Microbially mediated biogeochemical processes are catalyzed by enzymes that control the transformation of carbon, nitrogen, and other elements in environment. The dynamic linkage between enzymes and biogeochemical species transformation has, however, rarely been investigated because of the lack of analytical approaches to efficiently and reliably quantify enzymes and their dynamics in soils and sediments. Herein, we developed a signature peptide-based technique for sensitively quantifying dissimilatory and assimilatory enzymes using nitrate-reducing enzymes in a hyporheic zone sediment as an example. Moreover, the measured changes in enzyme concentration were found to correlate with the nitrate reduction rate in a way different from that inferred from biogeochemical models based on biomass or functional genes as surrogates for functional enzymes. This phenomenon has important implications for understanding and modeling the dynamics of microbial community functions and biogeochemical processes in environments. Our results also demonstrate the importance of enzyme quantification for the identification and interrogation of those biogeochemical processes with low metabolite concentrations as a result of faster enzyme-catalyzed consumption of metabolites than their production. The dynamic enzyme behaviors provide a basis for the development of enzyme-based models to describe the relationship between the microbial community and biogeochemical processes.

Li, Minjing↗
DOE OSTI2016

TU-FG-BRB-02: The Impact of Using Dual-Energy CT for Determining Proton Stopping Powers: Comparison Between Theory and Experiments

Purpose: To evaluate the clinical performance of dual-energy CT (DECT) in determining proton stopping power ratios (SPR) and demonstrate advantages over conventional single-energy CT (SECT). Methods: SECT and DECT scans of tissue-equivalent plastics as well as animal meat samples are performed with a Siemens SOMATOM Definition Flash. The methods of Schneider et al. (1996) and Bourque et al. (2014) are used to determine proton SPR on SECT and DECT images, respectively. Waterequivalent path length (WEPL) measurements of plastics and tissue samples are performed with a 195 MeV proton beam. WEPL values are determined experimentally using the depth-dose shift and dose extinction methods. Results: Comparison between CT-based and experimental WEPL is performed for 12 tissue-equivalent plastic as well as 6 meat boxes containing animal liver, kidney, heart, stomach, muscle and bones. For plastic materials, results show a systematic improvement in determining SPR with DECT, with a mean absolute error of 0.4% compared to 1.7% for SECT. For the meat samples, preliminary results show the ability for DECT to determine WEPL with a mean absolute value of 1.1% over all meat boxes. Conclusion: This work demonstrates the potential in using DECT for determining proton SPR with plastic materials in a clinical context. Further work is required to show the benefits of DECT for tissue samples. While experimental uncertainties could be a limiting factor to show the benefits of DECT over SECT for the meat samples, further work is required to adapt the DECT formalism in the context of clinical use, where noise and artifacts play an important role.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2017

Diazotroph Community Characterization via a High-Throughput nifH Amplicon Sequencing and Analysis Pipeline

The dinitrogenase reductase gene ( nifH ) is the most widely established molecular marker for the study of nitrogen-fixing prokaryotes in nature. A large number of PCR primer sets have been developed for nifH amplification, and the effective deployment of these approaches should be guided by a rapid, easy-to-use analysis protocol. Bioinformatic analysis of marker gene sequences also requires considerable expertise. In this study, we advance the state of the art for nifH analysis by evaluating nifH primer set performance, developing an improved amplicon sequencing workflow, and implementing a user-friendly bioinformatics pipeline. Furthermore, the developed amplicon sequencing workflow is a three-stage PCR-based approach that uses established technologies for incorporating sample-specific barcode sequences and sequencing adapters. Based on our primer evaluation, we recommend the Ando primer set be used with a modified annealing temperature of 58°C, as this approach captured the largest diversity of nifH templates, including paralog cluster IV/V sequences. To improve nifH sequence analysis, we developed a computational pipeline which infers taxonomy and optionally filters out paralog sequences. In addition, we employed an empirical model to derive optimal operational taxonomic unit (OTU) cutoffs for the nifH gene at the species, genus, and family levels. A comprehensive workflow script named TaxADivA (TAXonomy Assignment and DIVersity Assessment) is provided to ease processing and analysis of nifH amplicons. Our approach is then validated through characterization of diazotroph communities across environmental gradients in beach sands impacted by the Deepwater Horizon oil spill in the Gulf of Mexico, in a peat moss-dominated wetland, and in various plant compartments of a sugarcane field. IMPORTANCE: Nitrogen availability often limits ecosystem productivity, and nitrogen fixation, exclusive to prokaryotes, comprises a major source of nitrogen input that sustains food webs. The nifH gene, which codes for the iron protein of the nitrogenase enzyme, is the most widely established molecular marker for the study of nitrogen-fixing microorganisms (diazotrophs) in nature. In this study, a flexible sequencing/analysis pipeline, named TaxADivA, was developed for nifH amplicons produced by Illumina paired-end sequencing, and it enables an inference of taxonomy, performs clustering, and produces output in formats that may be used by programs that facilitate data exploration and analysis. Diazotroph diversity and community composition are linked to ecosystem functioning, and our results advance the phylogenetic characterization of diazotroph communities by providing empirically derived nifH similarity cutoffs for species, genus, and family levels. Thus, the utility of our pipeline is validated for diazotroph communities in a variety of ecosystems, including contaminated beach sands, peatland ecosystems, living plant tissues, and rhizosphere soil.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Glacier ice archives nearly 15,000-year-old microbes and phages

Background Glacier ice archives information, including microbiology, that helps reveal paleoclimate histories and predict future climate change. Though glacier-ice microbes are studied using culture or amplicon approaches, more challenging metagenomic approaches, which provide access to functional, genome-resolved information and viruses, are under-utilized, partly due to low biomass and potential contamination. Results We expand existing clean sampling procedures using controlled artificial ice-core experiments and adapted previously established low-biomass metagenomic approaches to study glacier-ice viruses. Controlled sampling experiments drastically reduced mock contaminants including bacteria, viruses, and free DNA to background levels. Amplicon sequencing from eight depths of two Tibetan Plateau ice cores revealed common glacier-ice lineages including Janthinobacterium, Polaromonas, Herminiimonas, Flavobacterium, Sphingomonas, and Methylobacterium as the dominant genera, while microbial communities were significantly different between two ice cores, associating with different climate conditions during deposition. Separately, ~355- and ~14,400-year-old ice were subject to viral enrichment and low-input quantitative sequencing, yielding genomic sequences for 33 vOTUs. These were virtually all unique to this study, representing 28 novel genera and not a single species shared with 225 environmentally diverse viromes. Further, 42.4% of the vOTUs were identifiable temperate, which is significantly higher than that in gut, soil, and marine viromes, and indicates that temperate phages are possibly favored in glacier-ice environments before being frozen. In silico host predictions linked 18 vOTUs to co-occurring abundant bacteria (Methylobacterium, Sphingomonas, and Janthinobacterium), indicating that these phages infected ice-abundant bacterial groups before being archived. Functional genome annotation revealed four virus-encoded auxiliary metabolic genes, particularly two motility genes suggest viruses potentially facilitate nutrient acquisition for their hosts. Finally, given their possible importance to methane cycling in ice, we focused on Methylobacterium viruses by contextualizing our ice-observed viruses against 123 viromes and prophages extracted from 131 Methylobacterium genomes, revealing that the archived viruses might originate from soil or plants. Conclusions Together, these efforts further microbial and viral sampling procedures for glacier ice and provide a first window into viral communities and functions in ancient glacier environments. Such methods and datasets can potentially enable researchers to contextualize new discoveries and begin to incorporate glacier-ice microbes and their viruses relative to past and present climate change in geographically diverse regions globally.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Stan : A Probabilistic Programming Language

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can also be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Assessment of Techniques for Measuring Tropospheric H Sub x O Sub Y: Summary

The objectives were: (1) to provide an assessment of the capability of the existing sensors of HxOy species at levels characteristic of the nonurban troposphere; (2) to identify those techniques adaptable to real-time measurement (sampled or continuous) onboard an aircraft platform; (3) to address the concern for intercalibration and intercomparison of techniques for measuring specific species; and (4) to recommend promising technologies for research and development for measuring important species for which a capability does not now exist or is presently of inadequate sensitivity.

Source record↗
NASA NTRS1989

Adaptive numerical solutions of the Euler equations in 3D using finite elements

The development of an adaptive mesh solution for a flow involving shock interaction on a swept cylinder and an initial solution for a flow past a complex fighter configuration is reported. The finite element solution algorithm, the mesh generation, and the adaptivity of the solution are described. Sample results for the flow past an F-18 configuration at Mach 0.9 and alpha of 3 deg and for shock interaction on a swept cylinder at Mach 8.04 are summarized.

Peraire, J.↗
NASA NTRS2002

Adaptive Identification and Control of Flow-Induced Cavity Oscillations

Progress towards an adaptive self-tuning regulator (STR) for the cavity tone problem is discussed in this paper. Adaptive system identification algorithms were applied to an experimental cavity-flow tested as a prerequisite to control. In addition, a simple digital controller and a piezoelectric bimorph actuator were used to demonstrate multiple tone suppression. The control tests at Mach numbers of 0.275, 0.40, and 0.60 indicated approx. = 7dB tone reductions at multiple frequencies. Several different adaptive system identification algorithms were applied at a single freestream Mach number of 0.275. Adaptive finite-impulse response (FIR) filters of orders up to N = 100 were found to be unsuitable for modeling the cavity flow dynamics. Adaptive infinite-impulse response (IIR) filters of comparable order better captured the system dynamics. Two recursive algorithms, the least-mean square (LMS) and the recursive-least square (RLS), were utilized to update the adaptive filter coefficients. Given the sample-time requirements imposed by the cavity flow dynamics, the computational simplicity of the least mean squares (LMS) algorithm is advantageous for real-time control.

Kegerise, M. A.↗
DOE OSTI2016

Comparison of CO 2 Detection Methods Tested in Shallow Groundwater Monitoring Wells at a Geological Sequestration Site

The geological storage of anthropogenic carbon dioxide (CO 2 ) is one method of reducing the amount of CO 2 released into the atmosphere. Monitoring programs typically determine baseline conditions in surface and near-surface environments before, during, and after CO 2 injection to evaluate if impacts related to injection have occurred. Because CO 2 concentrations in groundwater fluctuate naturally due to complex geochemical and geomicrobiologicalinteractions, a clear understanding of the baseline behavior of CO 2 in groundwater near injection sites is important. Numerous ways of measuring aqueous CO 2 in the field and lab are currently used, but most methods have significant shortcomings (e.g., are tedious, lengthy, have interferences, or have significant lag time before a result is determined). In this study, we examined the effectiveness of two novel CO 2 detection methods and their ability to rapidly detect CO2in shallow groundwater monitoring wells associated with the Illinois Basin –Decatur Project geological sequestration site. The CarboQC beverage carbonation meter was used to measure the concentration of CO 2 in water by monitoring temperature and pressure changes and calculating the PCO 2 from the ideal gas law. Additionally, a non-dispersive infrared (NDIR) CO< sub>2 sensor enclosed in a gas-permeable, water-impermeable membrane measured CO2by determining an equilibrium concentration. Results showed that the CarboQC method provided rapid (< 3 min) and repeatable results under field conditions within a measured concentration range of 15 –125 mg/L CO 2 . The NDIR sensor results correlated well (r 2 = 0.93) with the CarboQC data, but CO 2 equilibration required at least 15 minutes, making the method somewhat less desirable under field conditions. In contrast, NDIR-based sensors have a greater potential for long-term deployment. Both systems are adaptable to in-line groundwater sampling methods. Other specific advantages and disadvantages associated with the two approaches, and anomalies associated with specific samples, are discussed in greater detail in this poster.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Simulation study of energy resolution, position resolution and $\pi^0$-$\gamma$ separation of a sampling electromagnetic calorimeter at high energies

A simulation study of energy resolution, position resolution, and π(0)-γ separation using multivariate methods of a sampling calorimeter is presented. As a realistic example, the geometry of the calorimeter is taken from the design geometry of the Shashlik calorimeter which was considered as a candidate for CMS endcap for the phase II of LHC running. The methods proposed in this paper can be easily adapted to various geometrical layouts of a sampling calorimeter. Energy resolution is studied for different layouts and different absorber-scintillator combinations of the Shashlik detector. It is shown that a boosted decision tree using fine grained information of the calorimeter can perform three times better than a cut-based method for separation of π(0) from γ over a large energy range of 20 GeV–200 GeV.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Adaptive estimation of quantum observables

The accurate estimation of quantum observables is a critical task in science. With progress on the hardware, measuring a quantum system will become increasingly demanding, particularly for variational protocols that require extensive sampling. Here, we introduce a measurement scheme that adaptively modifies the estimator based on previously obtained data. Our algorithm, which we call AEQuO, continuously monitors both the estimated average and the associated error of the considered observable, and determines the next measurement step based on this information. We allow both for overlap and non-bitwise commutation relations in the subsets of Pauli operators that are simultaneously probed, thereby maximizing the amount of gathered information. AEQuO comes in two variants: a greedy bucket-filling algorithm with good performance for small problem instances, and a machine learning-based algorithm with more favorable scaling for larger instances. The measurement configuration determined by these subroutines is further post-processed in order to lower the error on the estimator. We test our protocol on chemistry Hamiltonians, for which AEQuO provides error estimates that improve on all state-of-the-art methods based on various grouping techniques or randomized measurements, thus greatly lowering the toll of measurements in current and future quantum applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗