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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Machine learning for continuous quantum error correction on superconducting qubits

Abstract Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla qubits. We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identify bit-flip errors from continuous noisy syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Error field measurements with rotating RMP fields for DIII-D H-mode

3D magnetic sensors are employed to identify the amplitude and toroidal phase of error fields (EF) by analyzing the torque balance for magnetic islands entrained by rotating resonant magnetic perturbations (RMPs) in DIII-D H-mode plasmas. This technique of torque balance allows for efficient error field identification, offering a valuable tool for scenario-specific and optimized error field compensation (EFC) and requires only magnetic diagnostics. The torque balance used in this work includes the contribution from electromagnetic torque due to error fields, wall response, RMP fields, and viscous. Results show that viscous torque plays a crucial role, particularly during locked modes and H-mode plasmas, ensuring accurate data fits with lower residuals. The torque balance technique reveals that the L- and H-mode plasmas have distinct EF configurations, and consistent with a model-based EF analysis including MHD response in IPEC and the SURFMN EF simulation. This technique shows great robustness in measuring the intrinsic EF amplitude regardless of its amplitude or toroidal phase. Repeated discharges with EFC disparities exhibit consistent results of intrinsic error field within a reasonable range near the “standard” error field compensation. Additionally, the use of a rotating n = 1 resonant magnetic perturbation offers the advantage of reducing disruption risks by entraining saturated magnetic islands. These findings are instrumental for optimizing EF correction in fusion devices, thereby enhancing tearing mode suppression and overall plasma stability.

3D magnetic sensors↗
DOE OSTI2023

Unified analysis of finite-size error for periodic Hartree-Fock and second order Møller-Plesset perturbation theory

Despite decades of practice, finite-size errors in many widely used electronic structure theories for periodic systems remain poorly understood. For periodic systems using a general Monkhorst-Pack grid, there has been no comprehensive and rigorous analysis of the finite-size error in the Hartree-Fock theory (HF) and the second order Møller-Plesset perturbation theory (MP2), which are the simplest wavefunction based method, and the simplest post-Hartree-Fock method, respectively. Such calculations can be viewed as a multi-dimensional integral discretized with certain trapezoidal rules. Due to the Coulomb singularity, the integrand has many points of discontinuity in general, and standard error analysis based on the Euler-Maclaurin formula gives overly pessimistic results. The lack of analytic understanding of finite-size errors also impedes the development of effective finite-size correction schemes. We propose a unified analysis to obtain sharp convergence rates of finite-size errors for the periodic HF and MP2 theories. Our main technical advancement is a generalization of the result of Lyness [Math. Comp. 30 (1976), pp. 1–23] for obtaining sharp convergence rates of the trapezoidal rule for a class of non-smooth integrands. Our result is applicable to three-dimensional bulk systems as well as low dimensional systems (such as nanowires and 2D materials). Our unified analysis also allows us to prove the effectiveness of the Madelung-constant correction to the Fock exchange energy, and the effectiveness of a recently proposed staggered mesh method for periodic MP2 calculations (see X. Xing, X. Li, and L. Lin [J. Chem. Theory Comput. 17 (2021), pp. 4733–4745]). In conclusion, our analysis connects the effectiveness of the staggered mesh method with integrands with removable singularities, and suggests a new staggered mesh method for reducing finite-size errors of periodic HF calculations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Perturbative Stability and Error-Correction Thresholds of Quantum Codes

Topologically ordered phases are stable to local perturbations, and topological quantum error-correcting codes enjoy thresholds to local errors. We connect the two notions of stability by constructing classical statistical mechanics models for decoding general Calderbank-Shor-Steane codes and classical linear codes. Our construction encodes correction success probabilities under uncorrelated bit-flip and phase-flip errors, and simultaneously describes a generalized ℤ 2 lattice-gauge theory with quenched disorder. We observe that the clean limit of the latter is precisely the discretized imaginary-time path integral of the corresponding quantum code Hamiltonian when the errors are turned into a perturbative 𝑋 or 𝑍 magnetic field. Motivated by error-correction considerations, we define general order parameters for all such generalized ℤ 2 lattice-gauge theories, and show that they are generally lower bounded by success probabilities of error correction. For CSS codes satisfying the low-density parity-check condition and with a sufficiently large code distance, we prove the existence of a low-temperature ordered phase of the corresponding lattice-gauge theories, particularly for those lacking Euclidean spatial locality and/or when there is a nonzero code rate. We further argue that these results provide evidence for stable phases in the corresponding perturbed quantum Hamiltonians, obtained in the limit of continuous imaginary time. To do so, we distinguish space- and timelike defects in the lattice-gauge theory. A high free-energy cost of spacelike defects corresponds to a successful “memory experiment” and suppresses the energy splitting among the ground states, while a high free-energy cost of timelike defects corresponds to a successful “stability experiment” and points to a nonzero gap to local excitations.

quantum error correction↗
DOE OSTI2022

ARETE: Accurate Error Assessment via Machine Learning-Guided Dynamic-Timing Analysis

Nanometer circuits are increasingly prone to timing errors, escalating the need for fault injection frameworks to accurately evaluate their impact on applications. Here in this paper, we propose ARETE, a novel cross-layer, fault-injection framework that combines dynamic-binary instrumentation with machine learning-guided dynamic-timing analysis. ARETE enables accurate fault-injection into any application by estimating the location of the injecting errors via dynamic-timing analysis. To accelerate fault-injection, we develop a novel, data-aware, machine learning-based mechanism that dynamically pre-selects the error-prone instructions and limits the application of the costly dynamic-timing analysis only to them. To evaluate ARETE's accuracy, our fully automated toolflow is configured to support fault-injection based on detailed post-layout gate-level simulations as well as via existing workload-agnostic error models. Our results for various workloads, including an autonomous-driving library, show that the location and time of injected errors performed by ARETE, is 89.9% consistent with fault-injection based on full gate-level simulation. On average, ARETE executes 84.6x faster than gate-level simulation and at a cost of 3.4% loss in the program output quality estimation. When compared to the existing statistical fault-injection tools that are based on workload-agnostic error models, ARETE improves the accuracy of fault-injection rate and output quality estimation by 143.9% and 40.4% on average, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Putting error bars on density functional theory dataset

This dataset contains submission files and raw output files from high-throughput DFT simulations to analyze the systemic errors in lattice constant, bulk moduli and formation energy predictions for a range of binary and ternary oxides using four exchange correlation functionals (LDA, PBE, PBEsol and vdW-DF-C09). This data was then used as the basis for employing materials informatics methods to predict the expected errors in the lattice constants of the studied compounds. Predicted errors were also used to better the DFT-predicted lattice parameters. Our results emphasize the link between the computed errors and the electron density and hybridization errors of a functional. In essence, these results provide “error bars” for choosing a functional for the creation of high-accuracy, high-throughput datasets as well as avenues for the development of XC functionals with enhanced performance, thereby enabling the accelerated discovery and design of new materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Analytic error analysis of cross section interpolation methods in nodal diffusion codes - I : Theory

This paper discusses two cross section interpolation methods commonly found in popular nodal codes; the partial derivatives and multiple tables models. The motivation for choosing a model, and thus a case matrix structure, is a trade off between accuracy and computational cost. Due to decades of experience, there are default structures that are sufficient for current light water reactor analysis. However, this is not necessarily the case for advanced reactor designs. Therefore, it is advantageous to understand the sources of error in cross section interpolation models so that the quality of a case matrix may be improved. A mathematical framework for these models is presented in this work that provides a more rigorous connection between the nuclear engineering field's cross section interpolation methods and the broader mathematical field of function approximation. The two cross section models examined in this paper were found to utilize Lagrange interpolation and are a subset of Lagrange tensor products. Classical results of Lagrange polynomial error analysis were then applied to the partial derivative and multiple tables models to derive expressions for the total point-wise error. The analytical results classify the total error into two parts: the model form error and interpolation error. Finally, based on our observations, a better foundation for improving the quality of a case matrix is proposed. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities

Scientific applications continue to grow and produce extremely large amounts of data, which require efficient compression algorithms for long-term storage. Compression errors in scientific applications can have a deleterious impact on downstream processing. Thus, it is crucial to preserve all the “known” Quantities of Interest (QoI) during compression. To address this issue, most existing approaches guarantee the reconstruction error of the original data or primary data (PD), but cannot directly control the problem of preserving the QoI. In this work, we propose a physics-informed compression technique that is composed of two parts: (i) reduction of the PD with bounded errors and (ii) preservation of the QoI. In the first step, we combine tensor decompositions, autoencoders, product quantizers, and error-bounded lossy compressors to bound the reconstruction error at high levels of compression. In the second step, we use constraint satisfaction post-processing followed by quantization to preserve the QoI. To illustrate the challenges of reducing the reconstruction errors of the PD and QoI, we focus on simulation data generated by a large-scale fusion code, XGC, which can produce tens of petabytes in a single day. The results show that our approach can achieve a high compression amount while accurately preserving the QoI within scientifically acceptable bounds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Stochastic Error Cancellation in Analog Quantum Simulation

Analog quantum simulation is a promising path towards solving classically intractable problems in many-body physics on near-term quantum devices. However, the presence of noise limits the size of the system and the length of time that can be simulated. In our work, we consider an error model in which the actual Hamiltonian of the simulator differs from the target Hamiltonian we want to simulate by small local perturbations, which are assumed to be random and unbiased. We analyze the error accumulated in observables in this setting and show that, due to stochastic error cancellation, with high probability the error scales as the square root of the number of qubits instead of linearly. We explore the concentration phenomenon of this error as well as its implications for local observables in the thermodynamic limit. Moreover, we show that stochastic error cancellation also manifests in the fidelity between the target state at the end of time-evolution and the actual state we obtain in the presence of noise. This indicates that, to reach a certain fidelity, more noise can be tolerated than implied by the worst-case bound if the noise comes from many statistically independent sources.

Analog quantum simulation↗
DOE OSTI2022

Quantifying Errors in Effective Cluster Interactions of Lattice Gas Cluster Expansions

The promise of lattice gas (LG) cluster expansions (CEs) is that they can describe a given system property to any level of accuracy since the orthogonal “cluster basis functions” span the complete space of available configurations. Such an approach can be constructed to an arbitrarily large surface of a finite number of distinct adsorption sites. Unfortunately, this is only true for the case of an ideal, fixed lattice decorated with components at precise lattice points (the lattice “sites”) with no distortions or relaxations subsequently allowed. Since most systems, and surfaces specifically, do not conform to such an ideal set of constraints, errors in LG CEs must be expected or CE convergence severely hampered. Beyond this, numerical errors in the provided data can complicate the proper construction of a truly predictive and/or physically significant CE. In this work, we show here how reliance on typical statistical tools like confidence intervals cannot be expected to provide an accurate representation of the uncertainty of effective cluster interactions (ECIs) in the CE due to the nature of the target ab initio data and the nature of CEs themselves. We develop a method for estimating these errors that does not rely on statistical assumptions about the model or data. We then use these ECI errors to quantify fundamental consequences on the uncertainty of ECIs in CEs built from O/Fe(100) data whose surface and adsorbates have been allowed to relax in a typical manner and from O/Fe(100) data whose surface and adsorbates are fixed in ideal lattice positions. We also quantify the effect of using a different density functional theory exchange–correlation functional, using these ECI errors to assess the significance in any deviations. In both cases, our method is shown to have remarkable utility in the quantification of errors in the ECIs of CEs. While we stick to the lattice gas convention in this work, the method is equally applicable to the Ising convention or, in principle, any linear model of sufficient complexity.

08 HYDROGEN↗
DOE OSTI2021

New Insights Into Error Decomposition for Precipitation Products

Abstract It is very important to quantify errors of precipitation estimation products. However, the existing methods do not describe all error components and are therefore not comprehensive enough. In this study, we propose a four‐component error decomposition method (4CED) that decomposes the total errors of precipitation products into four independent parts: hit positive bias, hit negative bias, false bias, and missed bias. And we use it to evaluate the performance of the three latest satellite precipitation products in the eastern monsoon region of China. Our study reveals 4CED has apparent improvements compared with the previous method. Results also provide new insights for tracking error sources and quantifying the error magnitudes of precipitation products. Moreover, the proposed 4CED can be extended to different spatial and temporal scales. Our new method will not only contribute to product upgrades, but also provide guidance for potential applications.

58 GEOSCIENCES↗
DOE OSTI2023

Real-time quantum error correction beyond break-even

The ambition of harnessing the quantum for computation is at odds with the fundamental phenomenon of decoherence. The purpose of quantum error correction (QEC) is to counteract the natural tendency of a complex system to decohere. This cooperative process, which requires participation of multiple quantum and classical components, creates a special type of dissipation that removes the entropy caused by the errors faster than the rate at which these errors corrupt the stored quantum information. Previous experimental attempts to engineer such a process faced an excessive generation of errors that overwhelmed the error-correcting capability of the process itself. Whether it is practically possible to utilize QEC for extending quantum coherence thus remains an open question. We answer it by demonstrating a fully stabilized and error-corrected logical qubit whose quantum coherence is significantly longer than that of all the imperfect quantum components involved in the QEC process, beating the best of them with a coherence gain of G=2.27±0.07. Here we achieve this performance by combining innovations in several domains including the fabrication of superconducting quantum circuits and model-free reinforcement learning.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Self-interaction error overbinds water clusters but cancels in structural energy differences

Here, we gauge the importance of self-interaction errors in density functional approximations (DFAs) for the case of water clusters. To this end, we used the Fermi–Löwdin orbital self-interaction correction method (FLOSIC) to calculate the binding energy of clusters of up to eight water molecules. Three representative DFAs of the local, generalized gradient, and metageneralized gradient families [i.e., local density approximation (LDA), Perdew–Burke–Ernzerhof (PBE), and strongly constrained and appropriately normed (SCAN)] were used. We find that the overbinding of the water clusters in these approximations is not a density-driven error. We show that, while removing self-interaction error does not alter the energetic ordering of the different water isomers with respect to the uncorrected DFAs, the resulting binding energies are corrected toward accurate reference values from higher-level calculations. In particular, self-interaction–corrected SCAN not only retains the correct energetic ordering for water hexamers but also reduces the mean error in the hexamer binding energies to less than 14 meV/ H 2 O from about 42 meV/ H 2 O for SCAN. By decomposing the total binding energy into many-body components, we find that large errors in the two-body interaction in SCAN are significantly reduced by self-interaction corrections. Higher-order many-body errors are small in both SCAN and self-interaction–corrected SCAN. These findings suggest that orbital-by-orbital removal of self-interaction combined with a proper DFA can lead to improved descriptions of water complexes.

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

Understanding and Estimating Error Propagation in Neural Networks for Scientific Data Analysis

Neural networks are increasingly integrated into scientific discovery, where input data reduction and model quantization play a key role in accelerating inference. However, understanding and mitigating the impact of these techniques on output error is critical for ensuring reliable results, particularly in tasks demanding high numerical precision. This paper introduces a comprehensive framework for optimizing neural network inference in scientific computing by combining data reduction and weight quantization while maintaining error-controlled outcomes. We develop theoretical analyses to bound error propagation under these reductions and propose a framework that balances computational performance with error constraints. Evaluation on real-world learning-based combustion simulations and satellite image classification demonstrates that our derived error bounds accurately predict observed errors while enabling significant computational speedup under our framework. This work highlights the potential for further leveraging advancements in modern lossy compression algorithms and hardware accelerators that support lower-precision formats.

He, Weiming [New Jersey Institute of Technology]↗
DOE OSTI2025

Q-Cluster: Quantum Error Mitigation Through Noise-Aware Unsupervised Learning

Quantum error mitigation (QEM) is critical in reducing the impact of noise in the pre-fault-tolerant era, and is expected to complement error correction in fault-tolerant quantum computing (FTQC). In this work, we propose a novel QEM approach, Q-Cluster, that uses unsupervised learning (clustering) to reshape the measured bit-string distribution. Our approach starts with a simplified bit-flip noise model. It first performs clustering on noisy measurement results, i.e., bit-strings, based on the Hamming distance. The centroid of each cluster is calculated using a qubit-wise majority vote. Next, the noisy distribution is adjusted with the clustering outcomes and the bitflip error rates using Bayesian inference. Our simulation results show that Q-Cluster can mitigate high noise rates (up to 40% per qubit) with the simple bit-flip noise model. However, real quantum computers do not fit such a simple noise model. To address the problem, we (a) apply Pauli twirling to tailor the complex noise channels to Pauli errors, and (b) employ a machine learning model, ExtraTrees regressor, to estimate an effective bit-flip error rate using a feature vector consisting of machine calibration data (gate & measurement error rates), circuit features (number of qubits, numbers of different types of gates, etc.) and the shape of the noisy distribution (entropy). Our experimental results show that our proposed Q-Cluster scheme improves the fidelity by a factor of 1.46x, on average, compared to the unmitigated output distribution, for a set of low-entropy benchmarks on five different IBM quantum machines. Our approach outperforms the state-of-art QEM approaches RZNE [28], M3 [24], Hammer [35], and QBEEP [33] by 1.26x,1.29x,1.47x, and 2.65 x, respectively.

42 ENGINEERING↗
DOE OSTI2023

TopoSZ: Preserving Topology in Error-Bounded Lossy Compression

Existing error-bounded lossy compression techniques control the pointwise error during compression to guarantee the integrity of the decompressed data. However, they typically do not explicitly preserve the topological features in data. When performing post hoc analysis with decompressed data using topological methods, preserving topology in the compression process to obtain topologically consistent and correct scientific insights is desirable. In this paper, we introduce TopoSZ, an error-bounded lossy compression method that preserves the topological features in 2D and 3D scalar fields. Specifically, we aim to preserve the types and locations of local extrema as well as the level set relations among critical points captured by contour trees in the decompressed data. The main idea is to derive topological constraints from contour-tree-induced segmentation from the data domain, and incorporate such constraints with a customized error-controlled quantization strategy from the SZ compressor (version 1.4). In conclusion, our method allows users to control the pointwise error and the loss of topological features during the compression process with a global error bound and a persistence threshold.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Single Grid Error Estimation for Neutron Transport Solvers

The method of nearby problems (MNP) is a solution verification technique that does not require the use of multiple spatial grids. To estimate spatial discretization error without requiring a high-fidelity spatial grid, an analytical curve fit is interpolated from the numerical solution. The residual between the curve fit solution and numerical solution is calculated and added as an additional source term to the governing equation. The nearby solution is estimated using the updated source term and boundary conditions to remain consistent with the curve fit interpolation. The nearby solution can be compared to the curve fit solution as a discretization error estimation while using a single spatial grid. Without the use of higher fidelity spatial grids, the MNP is able to approximate the spatial discretization error, a facet of solution verification. The application of the method of nearby problems is presented for one- and two-dimensional neutron transport problems for both fixed source and criticality problems on the spatial variable. The fixed source results demonstrate the effectiveness of nearby problems for spatial error identification using the discrete ordinates method. Criticality results are shown to identify area of high spatial error for the C5G7 problem as well as for the discrete ordinates solver. A novel approach of combining the capabilities of Monte Carlo with the discrete ordinates nearby problems is presented for one- and two-dimensional fixed source problems. In conclusion, the MNP demonstrates its effectiveness at identifying spatial error on a single structured grid with a wide variety of neutron transport problems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Mid-circuit correction of correlated phase errors using an array of spectator qubits

Scaling up invariably error-prone quantum processors is a formidable challenge. Although quantum error correction ultimately promises fault-tolerant operation, the required qubit overhead and error thresholds are daunting. In a complementary proposal, colocated, auxiliary “spectator” qubits act as in situ probes of noise and enable real-time, coherent corrections of data qubit errors. We used an array of cesium spectator qubits to correct correlated phase errors on an array of rubidium data qubits. By combining in-sequence readout, data processing, and feedforward operations, these correlated errors were suppressed within the execution of the quantum circuit. The protocol is broadly applicable to quantum information platforms and establishes key tools for scaling neutral-atom quantum processors: mid-circuit readout of atom arrays, real-time processing and feedforward, and coherent mid-circuit reloading of atomic qubits.

Science & Technology - Other Topics↗