Recent lbnl_cs_nersc items

Neural Posterior Unfolding

Fri, 26 Jun 2026 00:00:00 +0000

Differential cross section measurements are the currency of scientific exchange in particle and nuclear physics. The key challenge for these analyses is the correction for detector distortions known as deconvolution or unfolding. In the case of binned cross section measurements, there are many tools for regularized matrix inversion where the matrix governs the detector response going from pre- to post-detector observables. In this paper, we show how normalizing flows and neural posterior estimation can be used for unfolding. This approach has many potential advantages, including implicit regularization from the neural networks and fast inference from amortized training. We demonstrate this approach using simple Gaussian examples as well as a simulated jet substructure measurement at the Large Hadron Collider.

Gradient Flow for Parton Distribution Functions: First Application to the Pion

Thu, 25 Jun 2026 00:00:00 +0000

Parton distribution functions (PDFs) are central to precision QCD phenomenology. Their Mellin moments can be computed on the lattice, but direct determinations using local operators, besides ⟨x⟩, face severe challenges from reduced hypercubic symmetry, limiting results to the lowest moments. A recently proposed method resolves these issues using gradient flow. We demonstrate the efficacy of this method by computing ratios of flavor nonsinglet pion PDF moments up to ⟨x^{5}⟩ on four lattice spacings at m_{π}≃411 MeV. The moments and reconstructed PDF agree quantitatively with recent phenomenological extractions.

Moments of parton distribution functions of the pion from lattice QCD using gradient flow

Thu, 25 Jun 2026 00:00:00 +0000

We present a nonperturbative determination of the pion valence parton distribution function (PDF) moment ratios up to , using the gradient flow in lattice quantum chromodynamics (QCD). As a testing ground, we employ SU(3) isosymmetric gauge configurations generated by the OpenLat initiative with a pseudoscalar mass of . Our analysis uses four lattice spacings and a nonperturbatively improved action, enabling full control over the continuum extrapolation, and the limit of vanishing flow time, . The flowed ratios exhibit O( ) scaling across the ensembles, and the continuum-extrapolated results, matched to the scheme at using next-to-next-to-leading order matching coefficients, show only mild residual flow-time dependence. The resulting ratios, computed with a relatively small number of configurations, are consistent with phenomenological expectations for the pion’s valence distribution, with statistical uncertainties that are competitive with modern global fits. These findings...

A multimodal large language model for materials science

Thu, 25 Jun 2026 00:00:00 +0000

Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics and beyond. Integrating material structure data with language-based information through multimodal large language models (LLMs) offers great potential to support these efforts by enhancing human–artificial intelligence interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multimodal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat uses a bridging module to effectively align a pretrained universal machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat greatly improves performance in material property prediction and human–artificial intelligence...

Signs of nonmonotonic finite-volume corrections to gA

Wed, 17 Jun 2026 00:00:00 +0000

We study finite-volume (FV) corrections to determinations of via lattice quantum chromodynamics (QCD) using analytic results and numerical analysis. We observe that heavy Baryon chiral perturbation theory does not provide an unambiguous prediction for the sign of the FV correction, which is not surprising when one also considers large- constraints on the axial couplings. We further show that nonmonotonic FV corrections are naturally allowed when one considers either including explicit -resonance degrees of freedom or one works to higher orders in the chiral expansion. We investigate the potential impact of these FV corrections with a precision study of using models of FV corrections that are monotonic and nonmonotonic. Using lattice QCD data that is approximately at the 1% level of precision, we do not see significant evidence of nonmonotonic corrections. Looking forward to the next phase of lattice QCD calculations, we estimate that calculations that are between the 0.1%...

ERRATUM: Two-neutrino double electron capture of 124Xe in the first LUX-ZEPLIN exposure (2024 J. Phys. G: Nucl. Part. Phys. 52 015103)

Wed, 17 Jun 2026 00:00:00 +0000

Due to an error in production, the error margins of many values were incorrectly published. Table 1 details the locations of the errors and their corrections.

Erratum to: Search for exclusive Higgs and Z boson decays to ϕγ and ργ with the ATLAS detector

Tue, 16 Jun 2026 00:00:00 +0000

One correction is noted for the paper. The calculation of an angle used to account for meson polarisation in the signal decays was not correct.

Artificial intelligence methods for protein structure and interaction prediction: Recent advances and challenges

Wed, 27 May 2026 00:00:00 +0000

Recent advances in artificial intelligence have introduced novel methods for high-accuracy prediction of protein tertiary structures, protein complex structures, and interactions between proteins and other biomolecules, such as small molecules and nucleic acids. Such advancements are accelerating biomedical research and the development of new protein design and bioengineering methods among many other important biotechnology applications. In this review, we outline the recent advances in protein-centric biomolecular structure and interaction prediction, highlight some major challenges in the field, and discuss potential directions to address them.

StochasticGW-GPU: Rapid Quasi-Particle Energies for Molecules beyond 10,000 Atoms

Thu, 14 May 2026 00:00:00 +0000

StochasticGW is a code for computing accurate quasi-particle (QP) energies of molecules and material systems in the GW approximation. StochasticGW utilizes the stochastic Resolution of the Identity (sROI) technique to enable a massively parallel implementation with computational costs that scale semilinearly with system size, allowing the method to access systems with tens of thousands of electrons. We introduce a new implementation, StochasticGW-GPU, for which the main bottleneck steps have been ported to GPUs and give substantial performance improvements over previous versions of the code. We showcase the new code by computing band gaps of hydrogenated silicon clusters (Si_xH_y) containing up to 10,001 atoms and 35,144 electrons, and we obtain individual QP energies with a statistical precision of better than ±0.03 eV with times-to-solution of less than 1 h.

Quantifying Interconnect Energy Efficiency on Perlmutter: pJ/bit Measurements of NVLink, PCIe, Slingshot NICs, and Rosetta Switches

Wed, 13 May 2026 00:00:00 +0000

In the exascale era, comprehensive energy accounting is critical for sustainable HPC. Standard monitoring captures CPU and GPU power, but the energy footprint of interconnects, including switches, NICs, and PCIe/NVLinks, remains hidden due to limited hardware sensors. We address this with a software-centric methodology using targeted microbenchmarks on the Perlmutter (HPE Cray EX) system at NERSC. By correlating controlled stress on specific network components with job- and rack-level power telemetry, we isolate each component’s energy consumption, quantifying dynamic energy per bit (pJ/bit) for active communication while separating constant-power overhead. Integrating network bandwidth measurements from vendor tools such as NVIDIA DCGM and CrayPat enables fine-grained, application-level estimates of network energy use, unattainable with standard monitoring. This approach establishes a generalizable framework for evaluating network energy, providing actionable insights for designing...

Data Release 1 of the Dark Energy Spectroscopic Instrument

Tue, 5 May 2026 00:00:00 +0000

In 2021 May the Dark Energy Spectroscopic Instrument (DESI) collaboration began a 5 yr spectroscopic redshift survey to produce a detailed map of the evolving three-dimensional structure of the Universe between z = 0 and z ≈ 4. DESI’s principal scientific objectives are to place precise constraints on the equation of state of dark energy, the gravitationally driven growth of large-scale structure, and the sum of the neutrino masses, and to explore the observational signatures of primordial inflation. We present DESI DR1, which consists of all data acquired during the first 13 months of the DESI main survey, as well as a uniform reprocessing of the DESI Survey Validation data, which were previously made public in the DESI Early Data Release. The DR1 main survey includes high-confidence redshifts for 18.7M objects, of which 13.1M are spectroscopically classified as galaxies, 1.6M as quasars, and 4M as stars, making DR1 the largest sample of extragalactic redshifts ever assembled....

Unsupervised discovery of extreme weather events using universal representations of emergent organization

Tue, 28 Apr 2026 00:00:00 +0000

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones-local causal states-capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional...

Foundation model framework for all tasks involving jet physics

Wed, 15 Apr 2026 00:00:00 +0000

Foundation models use large datasets to build an effective representation of data that can be deployed on diverse downstream tasks. Previous research developed the mniearn foundation model for jet physics, using unique properties of particle physics, and showed that it could significantly advance discovery potential across collider experiments. This paper introduces a major upgrade, resulting in the mniearned framework. This framework has three new elements: (1) updates to the model architecture and training, (2) using over jets used for training, and (3) providing well-documented software for accessing all datasets and models. We demonstrate mniearned with three representative tasks: top-quark jet tagging with the community elphes-based benchmark dataset, b tagging with ATLAS full simulation, and anomaly detection with CMS experimental data. In each case, mniearned is the state of the art, further expanding the discovery potential of past, current, and future...

Di-nucleons do not form bound states at heavy pion mass

Tue, 14 Apr 2026 00:00:00 +0000

We perform a high-statistics lattice QCD calculation of the low-energy two-nucleon scattering amplitudes. To address discrepancies in the literature, the calculation is performed at a heavy pion mass in the limit that the light quark masses are equal to the physical strange quark mass, . Using a state-of-the-art momentum space method, we rule out the presence of a bound di-nucleon in both the isospin 0 (deuteron) and 1 (di-neutron) channels, in contrast with many previous results that made use of compact hexaquark creation operators. To diagnose the discrepancy, we add such hexaquark interpolating operators to our basis and find that they do not affect the determination of the two-nucleon finite-volume spectrum, and thus they do not couple to deeply bound di-nucleons that are missed by the momentum-space operators. Furthermore, we perform a high-statistics calculation of the HAL QCD potential on the same gauge ensembles and find qualitative agreement with our main results. We...

Efficient Measurement-Driven Eigenenergy Estimation with Classical Shadows

Tue, 31 Mar 2026 00:00:00 +0000

Quantum algorithms exploiting real-time evolution under a target Hamiltonian have demonstrated remarkable efficiency in extracting key spectral information. However, the broader potential of these methods, particularly beyond ground-state calculations, is underexplored. In this work, we introduce the framework of multiobservable dynamic mode decomposition (MODMD), which combines the observable dynamic mode decomposition (DMD), a measurement-driven eigensolver tailored for near-term implementation, with classical shadow tomography. MODMD leverages random scrambling in the classical shadow technique to construct, with exponentially reduced resource requirements, a signal subspace that encodes rich spectral information. Notably, we replace typical Hadamard-test circuits with a protocol designed to predict low-rank observables, thereby broadening the use of classical shadow tomography for predicting many low-rank observables. We establish theoretical guarantees on the spectral approximation...

Nonlinear thermodynamic computing out of equilibrium

Wed, 25 Mar 2026 00:00:00 +0000

We present the design for a thermodynamic computer that can perform arbitrary nonlinear calculations in or out of equilibrium. Simple thermodynamic circuits, fluctuating degrees of freedom in contact with a thermal bath and confined by a quartic potential, display an activity that is a nonlinear function of their input. Such circuits can therefore be regarded as thermodynamic neurons, and can serve as the building blocks of networked structures that act as thermodynamic neural networks, universal function approximators whose operation is powered by thermal fluctuations. We simulate a digital model of a thermodynamic neural network, and show that its parameters can be adjusted by genetic algorithm to perform nonlinear calculations at specified observation times, regardless of whether the system has attained thermal equilibrium. This work expands the field of thermodynamic computing beyond the regime of thermal equilibrium, enabling fully nonlinear computations, analogous to those...

Spatial and temporal evaluations of the liquid argon purity in ProtoDUNE-SP

Mon, 16 Mar 2026 00:00:00 +0000

Liquid argon time projection chambers (LArTPCs) rely on highly pure argon to ensure that ionization electrons produced by charged particles reach readout arrays. ProtoDUNE Single-Phase (ProtoDUNE-SP) was an approximately 700-ton liquid argon detector intended to prototype the Deep Underground Neutrino Experiment (DUNE) Far Detector Horizontal Drift module. It contains two drift volumes bisected by the cathode plane assembly, which is biased to create an almost uniform electric field in both volumes. The DUNE Far Detector modules must have robust cryogenic systems capable of filtering argon and supplying the TPC with clean liquid. This paper will explore comparisons of the argon purity measured by the purity monitors with those measured using muons in the TPC from October 2018 to November 2018. A new method is introduced to measure the liquid argon purity in the TPC using muons crossing both drift volumes of ProtoDUNE-SP. For extended periods on the timescale of weeks, the drift...

Diagonal state designs with reconfigurable real-time circuits

Wed, 11 Mar 2026 00:00:00 +0000

Unitary designs are widely used in quantum computation, but in many practical settings it suffices to construct a diagonal state design generated with unitary gates diagonal in the computational basis. In this work, we introduce a simple and efficient diagonal state 3-design based on real-time evolutions under 2-local Hamiltonians. Our construction is inspired by the classical Girard-Hutchinson trace estimator in that it involves the stochastic preparation of many random-phase states. Though the exact Girard-Hutchinson states are not tractably implementable on a quantum computer, we can construct states that match the statistical moments of the Girard-Hutchinson states with real-time evolution. Importantly, our random states are all generated using the same Hamiltonians for real-time evolution, with the randomness arising solely from stochastic variations in the durations of the evolutions. In this sense, the circuit is fully reconfigurable and thus suited for near-term realizations...

QCaMP: A 4-Week Summer Camp Introducing High School Students to Quantum Information Science and Technology

Wed, 11 Mar 2026 00:00:00 +0000

The 2024 Quantum Computing, Math and Physics Camp (QCaMP) for Students was a 4-week long summer camp aimed at introducing high school students to quantum concepts and careers, including applications spanning quantum computing, sensing, and communication. The program ran for 7 hours/day, Monday-Friday, July 1-26, and included hands-on modules and activities, professional development, and projectbased learning. Here we provide details on the camp curriculum and outcomes based on pre and post knowledge and attitudes assessments.

QCLAB: A Matlab Toolbox for Quantum Computing

Wed, 11 Mar 2026 00:00:00 +0000

We introduce QCLAB, an object-oriented MATLAB toolbox for constructing, representing, and simulating quantum circuits. Designed with an emphasis on numerical stability, efficiency, and performance, QCLAB provides a reliable platform for prototyping and testing quantum algorithms. For advanced performance needs, QCLAB++ serves as a complementary C++ package optimized for GPU-accelerated quantum circuit simulations. Together, QCLAB and QCLAB++ form a comprehensive toolkit, balancing the simplicity of MATLAB scripting with the computational power of GPU acceleration. This paper serves as an introduction to the package and its features along with a hands-on tutorial that invites researchers to explore its capabilities right away.

A Practical Framework for Assessing the Performance of Observable Estimation in Quantum Simulation

Wed, 11 Mar 2026 00:00:00 +0000

Simulating dynamics of physical systems is a key application of quantum computing, with potential impact in fields such as condensed matter physics and quantum chemistry. However, current quantum algorithms for Hamiltonian simulation yield results that are inadequate for real use cases and suffer from lengthy execution times when implemented on near-term quantum hardware. In this work, we introduce a framework for evaluating the performance of quantum simulation algorithms, focusing on the computation of observables, such as energy expectation values. Our framework provides end-to-end demonstrations of algorithmic optimizations that utilize Pauli term groups based on $k$-commutativity, generate customized Clifford measurement circuits, and implement weighted shot distribution strategies across these groups. These demonstrations span multiple quantum execution environments, allowing us to identify critical factors influencing runtime and solution accuracy. We integrate enhancements...

KBase: Open-source platform for collaborative biological data analysis and publication

Tue, 10 Mar 2026 00:00:00 +0000

The U.S. Department of Energy’s Systems Biology Knowledgebase (KBase; www.kbase.us ) is an open, collaborative platform that integrates data, models, and analysis tools to accelerate discovery in microbiology, plant biology, and environmental systems. Recently, KBase expanded as a comprehensive, multi-omics ecosystem. KBase enables representation of scientific samples, long-read sequence analysis, protein structure integration, and scalable modeling of microbial communities across diverse environments. KBase also generates digital notebooks as citable, executable research objects that link data, methods, and interpretation. KBase also supports a global education community focused on training the next generation of scientists to use high-performance computational tools. Together, these advances position KBase as a central hub for open, reproducible systems biology. In turn, this enables us to integrate many of the emerging advances in data federation, semantic interoperability,...

Erratum: DESI 2024 V: Full-Shape Galaxy Clustering from Galaxies and Quasars

Tue, 10 Mar 2026 00:00:00 +0000

Erratum: DESI 2024 V: Full-Shape Galaxy Clustering from Galaxies and Quasars

DESI 2024 V: Full-Shape galaxy clustering from galaxies and quasars

Tue, 3 Mar 2026 00:00:00 +0000

We present the measurements and cosmological implications of the galaxy two-point clustering using over 4.7 million unique galaxy and quasar redshifts in the range 0.1 < z < 2.1 divided into six redshift bins over a ∼ 7,500 square degree footprint, from the first year of observations with the Dark Energy Spectroscopic Instrument (DESI Data Release 1). By fitting the full power spectrum, we extend previous DESI DR1 baryon acoustic oscillation (BAO) measurements to include redshift-space distortions and signals from the matter-radiation equality scale. For the first time, this Full-Shape analysis is blinded at the catalogue-level to avoid confirmation bias and the systematic errors are accounted for at the two-point clustering level, which automatically propagates them into any cosmological parameter. When analysing the data in terms of compressed model-agnostic variables, we obtain a combined precision of 4.7% on the amplitude of the redshift space distortion (RSD) signal...

DESI 2024 VII: cosmological constraints from the full-shape modeling of clustering measurements

Tue, 3 Mar 2026 00:00:00 +0000

We present cosmological results from the measurement of clustering of galaxy, quasar and Lyman-α forest tracers from the first year of observations with the Dark Energy Spectroscopic Instrument (DESI Data Release 1). We adopt the full-shape (FS) modeling of the power spectrum, including the effects of redshift-space distortions, in an analysis which has been thoroughly validated in a series of supporting papers as summarised in [1]. We combine the full-shape information with DESI's DR1 constraints from the baryon acoustic oscillations (BAO) of these tracers. In the flat ΛCDM cosmological model, DESI (FS+BAO), combined with a baryon density prior from Big Bang Nucleosynthesis and a weak prior on the scalar spectral index, determines matter density to Ωm = 0.2962 ± 0.0095, and the amplitude of mass fluctuations to σ 8 = 0.842 ± 0.034. The addition of the cosmic microwave background (CMB) data tightens these constraints to Ωm = 0.3056 ± 0.0049 and σ 8 = 0.8121 ± 0.0053, while further...

Assessing the potential of deep learning for protein–ligand docking

Wed, 4 Feb 2026 00:00:00 +0000

The effects of ligand binding on protein structures and their in vivo functions carry numerous implications for modern biomedical research and biotechnology development efforts such as drug discovery. Although several deep learning (DL) methods and benchmarks designed for protein–ligand docking have recently been introduced, so far no previous works have systematically studied the behaviour of the latest docking and structure prediction methods within the broadly applicable context of: (1) using predicted (apo) protein structures for docking (for example, for applicability to new proteins); (2) binding multiple (cofactor) ligands concurrently to a given target protein (for example, for enzyme design); and (3) having no previous knowledge of binding pockets (for example, for generalization to unknown pockets). To enable a deeper understanding of the real-world utility of docking methods, we introduce PoseBench, a comprehensive benchmark for broadly applicable protein–ligand docking....

Benchmark-driven Models for Energy Analysis and Attribution of GPU-Accelerated Supercomputing

Tue, 3 Feb 2026 00:00:00 +0000

As advances in energy-efficiency become the primary limiter to increases in power-constrained supercomputing and machine learning performance, it is imperative developers, architects, and practitioners understand how modern GPUs consume energy when running HPC and ML applications. Rather than opaque coarse-grained metrics, in this paper, we develop an extensible, microbenchmark-parameterized energy model capable of attributing application energy not only by functional unit (FPU, tensor core, integer ALU) and memory level (L1, L2, HBM), but can also differentiate control energy from datapath energy. We examine trends in energy per operation among four generations of GPUs and validate our results using supercomputing and ML/AI procurement workloads. Our insights and extrapolations can be used to drive the future of CMOS and memory technologies, computer architecture research, algorithmic innovation, optimizations for power-constrained and mobile environments, and data center operations.

Classical optimization with imaginary-time block encoding on quantum computers: The MaxCut problem

Mon, 26 Jan 2026 00:00:00 +0000

Optimization problems in finance, physics, and computer science are typically very hard to tackle in classical computing; quantum computing could help speed up computations and provide efficient methods for tackling large problems. Typically, to treat a problem with a quantum computer, the optimal solution is cast as the ground state of a diagonal Hamiltonian. We develop a method, called imaginary-time evolution block encoding (ITE-BE), based on a recent imaginary-time algorithm, which requires no variational parameter optimization, as all parameters can be derived analytically from the target Hamiltonian. We also demonstrate that our method can be successfully combined with other quantum algorithms such as the quantum approximate optimization algorithm (QAOA). For illustration, here we study the MaxCut problem. We find that the QAOA ansatz increases the postselection success of ITE-BE, and shallow QAOA circuits, when boosted with ITE-BE, achieve better performance than deeper...

The XLZD Design Book: towards the next-generation liquid xenon observatory for dark matter and neutrino physics

Thu, 22 Jan 2026 00:00:00 +0000

This report describes the experimental strategy and technologies for XLZD, the next-generation xenon observatory sensitive to dark matter and neutrino physics. In the baseline design, the detector will have an active liquid xenon target of 60 tonnes, which could be increased to 80 tonnes if the market conditions for xenon are favorable. It is based on the mature liquid xenon time projection chamber technology used in current-generation experiments, LZ and XENONnT. The report discusses the baseline design and opportunities for further optimization of the individual detector components. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3σ$$\sigma $$ evidence potential for WIMP-nucleon cross sections as low as 3×10-49cm2$$3\times 10^{-49}\mathrm \,cm^2$$ (at 40 GeV/c2$$^2$$ WIMP mass). The observatory will also have leading sensitivity to a wide range...

US20: Edits for Local Prefix Operation Intrinsics

Fri, 16 Jan 2026 00:00:00 +0000

This paper contains Fortran 202Y specification edits for Fortran 202Y work item US20: Local Prefix Operation Intrinsics. It passed by unanimous consent at the Jan 2026 meeting #238 of the INCITS/US Fortran Programming Language Standards Technical Committee.

Assessing the potential of deep learning for protein–ligand docking

Tue, 13 Jan 2026 00:00:00 +0000

Seamless end-to-end containerized HPC environments

Tue, 13 Jan 2026 00:00:00 +0000

High-performance computing environments face increasing challenges from diverse scientific workflows, imposing conflicting demands for stability, customization, and reproducibility that traditional monolithic software stacks cannot accommodate. We present a comprehensive approach to seamless end-to-end containerized HPC environments which decomposes the technical challenge into five manageable areas: specification and construction of environments, session provisioning, scheduler integration, system integration, and security. We develop and evaluate prototypes across these five technical areas, demonstrating practical feasibility through Spack-based environment construction with CI/CD pipelines, transparent session access via PAM and Kubernetes, and flexible job execution using Slurm’s native container support. Through this work, we demonstrate that comprehensive containerization of HPC environments can be achieved using open standards, providing enhanced reproducibility and flexibility...

Mic-hackathon 2024: hackathon on machine learning for electron and scanning probe microscopy

Fri, 9 Jan 2026 00:00:00 +0000

Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision...

FAIR Universe 2024: Higgs ML Uncertainty Challenge

Mon, 5 Jan 2026 00:00:00 +0000

The HiggsML Uncertainty Challenge is a machine learning competition aimed at improving uncertainty-aware AI techniques in high-energy physics. Part of the FAIR Universe initiative, focuses on estimating the Higgs boson signal strength while accounting for systematic uncertainties affecting collider experiments. Unlike traditional classification tasks, participants must construct confidence intervals that properly cover systematic distortions. The HiggsML Uncertainty Challenge establishes a benchmark for uncertainty-aware AI, with applications in high-energy physics and beyond. The competition is hosted on Codabench, an open AI benchmarking platform, and uses highperformance computing resources at NERSC Perlmutter for scalable and reproducible model evaluation. The dataset and evaluation framework will remain publicly available for continued research.

Estimating Eigenenergies from Quantum Dynamics: A Unified Noise-Resilient Measurement-Driven Approach

Wed, 10 Dec 2025 00:00:00 +0000

Ground state energy estimation in physical, chemical, and materials sciences is one of the most promising applications of quantum computing. In this work, we introduce a new hybrid approach that finds the eigenenergies by collecting real-time measurements and post-processing them using the machinery of dynamic mode decomposition (DMD). From the perspective of quantum dynamics, we establish that our approach can be formally understood as a stable variational method on the function space of observables available from a quantum many-body system. We also provide strong theoretical and numerical evidence that our method converges rapidly even in the presence of a large degree of perturbative noise, and show that the method bears an isomorphism to robust matrix factorization methods developed independently across various scientific communities. Our numerical benchmarks on spin and molecular systems demonstrate an accelerated convergence and a favorable resource reduction over state-of-the-art...

Measurement reduction for expectation values via fine-grained commutativity

Tue, 2 Dec 2025 00:00:00 +0000

We introduce a notion of commutativity between operators on a tensor product space, nominally Pauli strings on qubits, that interpolates between qubitwise commutativity and (full) commutativity. We apply this notion, which we call k-commutativity, to measuring expectation values of observables in quantum circuits and show a reduction in the number measurements at the cost of increased circuit depth. We discuss the asymptotic measurement complexity of k-commutativity for several families of n-qubit Hamiltonians, showing examples with O(1), O(n), and O(n) scaling.

Accelerating Advanced Light Source Science Through Multi-Facility HPC Workflows

Tue, 2 Dec 2025 00:00:00 +0000

Synchrotron light sources support a wide array of techniques to investigate materials, often producing complex, high-volume data that challenge traditional workflows. At the Advanced Light Source (ALS), we developed infrastructure to move microtomography data over ESnet to ALCF and NERSC, where CPU- and GPU-based algorithms generate 3D reconstructed volumes of experimental samples. We employ two data movement and reconstruction models: real-time processing as data streams directly to NERSC compute nodes, and automated file transfer to NERSC and ALCF file systems. The streaming pipeline provides users with feedback in under ten seconds, while the file-based workflow produces high-quality re-constructions suitable for deeper analysis in 20–30 minutes. This infrastructure enables users to utilize HPC resources without direct access to backend systems. We plan to extend this architecture to more endstations, supporting our beamline scientists and users.

US04/DIN1: Edits for Asynchronous and Team Collective Subroutines

Mon, 17 Nov 2025 00:00:00 +0000

This paper contains Fortran 202Y specification edits for work items US-04 and DIN1, Asynchronous and Team Collective Subroutines. It passed by unanimous consent at the Nov 2025 meeting #237 of the INCITS/US Fortran Programming Language Standards Technical Committee.

Edits for US20 Collective Subroutines for Prefix Reductions

Mon, 17 Nov 2025 00:00:00 +0000

This paper contains Fortran 202Y specification edits for Fortran 202Y work item US-20, Collective subroutines for prefix operations. It passed by unanimous consent at the Oct 2025 meeting #237 of the INCITS/US Fortran Programming Language Standards Technical Committee.

Implementing Network-level QoS at HPC Datacenters to Enable Distributed Scientific Workflows

Mon, 10 Nov 2025 00:00:00 +0000

Implementing Network-level QoS at HPC Datacenters to Enable Distributed Scientific Workflows

US04: Specifications for Asynchronous Collective Subroutines

Sun, 9 Nov 2025 00:00:00 +0000

This paper contains formal specifications for Fortran 202Y work item US-04, Asynchronous Collective Subroutines. It passed by unanimous consent at the Oct 2025 meeting #237 of the INCITS/US Fortran Programming Language Standards Technical Committee.

US20: Specs and Syntax for Local Prefix Reduce Intrinsics

Sun, 9 Nov 2025 00:00:00 +0000

This paper contains formal specifications and syntax for Fortran 202Y work item US-20, intrinsic subroutines for prefix reduce operations. It passed by unanimous consent at the Oct 2025 meeting #237 of the INCITS/US Fortran Programming Language Standards Technical Committee.

Specifications and Syntax for Local Prefix Operation Intrinsics (sum)

Sun, 9 Nov 2025 00:00:00 +0000

This paper contains formal specifications and syntax for Fortran 202Y work item US-20, intrinsic subroutines for prefix sum operations. It passed by unanimous consent at the Oct 2025 meeting #237 of the INCITS/US Fortran Programming Language Standards Technical Committee.

US04: Requirements for Asynchronous Collective Subroutines

Sun, 9 Nov 2025 00:00:00 +0000

This paper contains formal requirements for Fortran 202Y work item US-04, Asynchronous Collective Subroutines. It passed by unanimous consent at the Oct 2025 meeting #237 of the INCITS/US Fortran Programming Language Standards Technical Committee.

US04: Syntax for Asynchronous Collective Subroutines

Sun, 9 Nov 2025 00:00:00 +0000

This paper contains formal syntax for Fortran 202Y work item US-04, Asynchronous Collective Subroutines. It passed by unanimous consent at the Oct 2025 meeting #237 of the INCITS/US Fortran Programming Language Standards Technical Committee.

Specifications and Syntax for US20 Collective Subroutines

Sun, 9 Nov 2025 00:00:00 +0000

This paper contains formal specifications and syntax for Fortran 202Y work item US-20, Collective subroutines for prefix operations. It passed by unanimous consent at the Oct 2025 meeting #237 of the INCITS/US Fortran Programming Language Standards Technical Committee.

Superfacility: The Convergence of Data, Compute, Networking, Analytics and Software

Mon, 22 Sep 2025 00:00:00 +0000

Superfacility: The Convergence of Data, Compute, Networking, Analytics and Software

Extreme I/O on HPC for HEP using the Burst Buffer at NERSC

Mon, 22 Sep 2025 00:00:00 +0000

In recent years there has been increasing use of HPC facilities for HEP experiments. This has initially focussed on less I/O intensive workloads such as generator-level or detector simulation. We now demonstrate the efficient running of I/O-heavy analysis workloads on HPC facilities at NERSC, for the ATLAS and ALICE LHC collaborations as well as astronomical image analysis for DESI and BOSS.

Cross-Facility Workflows: Case Studies with Active Experiments

Mon, 22 Sep 2025 00:00:00 +0000

Experimental and observational science pipelines are increasingly turning to supercomputing resources to handle their large-scale data analysis. Many of these pipelines serve experiments that are running 24/7, and must shutdown or find alternatives for their real-time data analysis during outages. Workflows from experimental and observational facilities are usually architected with a specific network and computing facility in mind, and are very difficult to switch between compute resources. What's more, the assumptions built into the architecture of most high-performance computing (HPC) centers makes moving workflows to new locations more complicated. By carefully targeting well-understood cosmology and genomics pipelines, we have researched the capabilities needed to run these workflows at multiple computing sites. In this process, we have identified several of the pain points and key future research topics for automated workflow migration, and have made substantial progress...

Quantum Computing Technology Roadmaps and Capability Assessment for Scientific Computing - An analysis of use cases from the NERSC workload

Thu, 11 Sep 2025 00:00:00 +0000

The National Energy Research Scientific Computing Center (NERSC), as the high-performance computing (HPC) facility for the Department of Energy’s Office of Science, recognizes the essential role of quantum computing in its future mission. In this report, we analyze the NERSC workload and identify materials science, quantum chemistry, and high-energy physics as the science domains and application areas that stand to benefit most from quantum computers. These domains jointly make up over 50% of the current NERSC production workload, which is illustrative of the impact quantum computing could have on NERSC’s mission going forward. We perform an extensive literature review and determine the quantum resources required to solve classically intractable problems within these science domains. This review also shows that the quantum resources required have consistently decreased over time due to algorithmic improvements and a deeper understanding of the problems. At the same time, public...

A Novel Multi-level Integrated Roofline Model Approach for Performance Characterization

Wed, 10 Sep 2025 00:00:00 +0000

With energy-efficient architectures, including accelerators and many-core processors, gaining traction, application developers face the challenge of optimizing their applications for multiple hardware features including many-core parallelism, wide processing vector-units and on-chip high-bandwidth memory. In this paper, we discuss the development and utilization of a new application performance tool based on an extension of the classical roofline-model for simultaneously profiling multiple levels in the cache-memory hierarchy. This tool presents a powerful visual aid for the developer and can be used to frame the many-dimensional optimization problem in a tractable way. We show case studies of real scientific applications that have gained insights from the Integrated Roofline Model.

A Methodology for Evaluating Tightly-integrated and Disaggregated Accelerated Architectures

Wed, 10 Sep 2025 00:00:00 +0000

Tighter integration of computational resources can foster superior application performance by mitigating communication bottlenecks. Unfortunately, not every application can use every compute or accelerator all the time. As a result, co-locating resources often leads to under-utilization of resources. To mitigate this challenge, architects have proposed disaggregation and ad hoc pooling of computational resources. In the next five years, HPC system architects will be presented with a spectrum of accelerated solutions ranging from tightly coupled, single package APUs to a sea of disaggregated GPUs interconnected by a global network. In this paper, we detail NEthing, our methodology and tool for evaluating the potential performance implications of such diverse architectural paradigms. We demonstrate our methodology on today's and projected 2026 technologies for three distinct workloads: a compute-intensive kernel, a tightly-coupled HPC simulation, and an ensemble of loosely-coupled...

Hierarchical Roofline analysis for GPUs: Accelerating performance optimization for the NERSC‐9 Perlmutter system

Wed, 10 Sep 2025 00:00:00 +0000

Summary The Roofline performance model provides an intuitive and insightful approach to identifying performance bottlenecks and guiding performance optimization. In preparation for the next‐generation supercomputer Perlmutter at NERSC, this paper presents a methodology to construct a hierarchical Roofline on NVIDIA GPUs and extends it to support reduced precision and Tensor Cores. The hierarchical Roofline incorporates L1, L2, device memory, and system memory bandwidths into one single figure, and it offers more profound insights into performance analysis than the traditional DRAM‐only Roofline. We use our Roofline methodology to analyze three proxy applications: GPP from BerkeleyGW, HPGMG from AMReX, and conv2d from TensorFlow. In doing so, we demonstrate the ability of our methodology to readily understand various aspects of performance and performance bottlenecks on NVIDIA GPUs and motivate code optimizations.

Experiences Porting the SU3_Bench Microbenchmark to the Intel Arria 10 and Xilinx Alveo U280 FPGAs

Wed, 10 Sep 2025 00:00:00 +0000

In this study we investigate the implications of porting a common computational kernel used in high performance computing, which has been optimized for efficient execution on general purpose graphics processing units (GPUs), to a field programmable gate array (FPGA). In particular, we use a benchmark based on a matrix-matrix multiply kernel commonly used in lattice quantum chromodynamics applications. The microbenchmark is based on the OpenCL programming language. We evaluate the performance, and portability, aspects associated for two FPGAs, the Intel Arria 10 and the Xilinx Alveo U280. The purpose of the study is not to compare the two FPGAs, but to evaluate their respective OpenCL toolchains and to evaluate the level of effort needed to port a GPU optimized code to a FPGA, and the effectiveness of the respective toolchains. We did find the toolchains to be relatively easy to use, and it was possible to get correctness with little effort, but there was significant effort needed...

Performance Variability on Xeon Phi

Wed, 10 Sep 2025 00:00:00 +0000

An understanding of sources of performance variability is important for high performance application developers and users. In this paper we discuss non-I/O sources of application performance variability on Cori, a Cray XC40 at NERSC with 9600+ Xeon Phi nodes connecting to an Aries high speed network with a Dragonfly topology. Our survey covers variability due to on-node effects from MCDRAM configured as cache and clock frequency scaling as well as off-node effects due to the network. For each source of variability we quantify the variability through micro-benchmarks and mini-applications, discuss potential mitigation strategies and analyze the impact on applications.

Architectural Requirements for Deep Learning Workloads in HPC Environments

Wed, 10 Sep 2025 00:00:00 +0000

Scientific machine learning (SciML) promises to have a transformational impact on scientific exploration, by combining state-of-The-Art AI methods with the latest generation of supercomputers. However, to efficiently leverage ML techniques on high-performance computing (HPC) systems, it is critical to understand the performance characteristics of the underlying algorithms on modern computational systems. In this work, we present a new methodology for developing a detailed performance understanding of ML benchmarks. To demonstrate our approach we investigate two emerging SciML benchmark applications from cosmology and climate, ComsoFlow and DeepCAM, as well as ResNet-50, a well-known image classification model. We develop and validate performance models that explore the key architectural artifacts, including memory requirements, data reuse, and performance efficiency across both single-and multiple-GPU computations. Our methodology also focuses on the complexity of data-movement...

Prediction of vacancy defect diffusion paths in high entropy alloys via machine learning on molecular dynamics data

Fri, 5 Sep 2025 00:00:00 +0000

Identifying the diffusion path of point defects is a critical step in understanding their evolution and the mechanisms of related phenomena. Defect diffusion occurs at small length and time scales, with impacts on material properties that may continue to evolve over ns to μs, ms, and the continuum scale (s, min, etc., and cm, m, etc.). The time scale accessible to molecular dynamics (MD) simulations is limited by small step sizes, typically in the fs range. Thus, surrogate models of MD simulations through machine learning (ML)-based algorithms are of great interest, especially for complex systems such as high entropy alloys (HEAs). In this work, dynamics governing vacancy migration in HEA were approximated with graph convolutional network (GCN) models as ansatzes for kinetic Monte Carlo (KMC) rate catalogs. Network design considered that diffusion in crystalline solids generally depends on interactions between defects and their immediate neighbor atoms. Graphs represented the...

Dark Matter Search Results from 4.2 Tonne-Years of Exposure of the LUX-ZEPLIN (LZ) Experiment

Tue, 26 Aug 2025 00:00:00 +0000

We report results of a search for nuclear recoils induced by weakly interacting massive particle (WIMP) dark matter using the LUX-ZEPLIN (LZ) two-phase xenon time projection chamber. This analysis uses a total exposure of 4.2±0.1 tonne-years from 280 live days of LZ operation, of which 3.3±0.1 tonne-years and 220 live days are new. A technique to actively tag background electronic recoils from ^{214}Pb β decays is featured for the first time. Enhanced electron-ion recombination is observed in two-neutrino double electron capture decays of ^{124}Xe, representing a noteworthy new background. After removal of artificial signal-like events injected into the dataset to mitigate analyzer bias, we find no evidence for an excess over expected backgrounds. World-leading constraints are placed on spin-independent (SI) and spin-dependent WIMP-nucleon cross sections for masses ≥9 GeV/c^{2}. The strongest SI exclusion set is 2.2×10^{-48} cm^{2} at the 90% confidence level and the best SI...

New Constraints on Cosmic Ray-Boosted Dark Matter from the LUX-ZEPLIN Experiment

Tue, 26 Aug 2025 00:00:00 +0000

While dual-phase xenon time projection chambers have driven the sensitivity toward weakly interacting massive particles at the GeV/c^{2} to TeV/c^{2} mass scale, the scope for sub-GeV/c^{2} dark matter particles is hindered by a limited nuclear recoil energy detection threshold. One approach to probe for lighter candidates is to consider cases where they have been boosted by collisions with cosmic rays in the Milky Way, such that the additional kinetic energy lifts their induced signatures above the nominal threshold. In this Letter, we report first results of a search for cosmic ray-boosted dark matter (CRDM) with a combined 4.2 metric ton/yr exposure from the LUX-ZEPLIN experiment. We observe no excess above the expected backgrounds and establish world-leading constraints on the spin-independent CRDM-nucleon cross section as small as 3.9×10^{-33} cm^{2} at 90% confidence level for sub-GeV/c^{2} masses.

First Constraint on Atmospheric Millicharged Particles with the LUX-ZEPLIN Experiment

Fri, 15 Aug 2025 00:00:00 +0000

We report on a search for millicharged particles (mCPs) produced in cosmic ray atmospheric interactions using data collected during the first science run of the LUX-ZEPLIN experiment. The mCPs produced by two processes-meson decay and proton bremsstrahlung-are considered in this study. This search utilized a novel signature unique to liquid xenon (LXe) time projection chambers, allowing sensitivity to mCPs with masses ranging from 10 to 1000 MeV/c^{2} and fractional charges between 0.001 and 0.02 of the electron charge (e). With an exposure of 60 live days and a 5.5 metric ton fiducial mass, we observed no significant excess over background. This represents the first experimental search for atmospheric mCPs and the first search for mCPs using an underground LXe experiment.

Supernova pointing capabilities of DUNE

Fri, 15 Aug 2025 00:00:00 +0000

The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electron-neutrino charged-current absorption on and elastic scattering of neutrinos on electrons. Procedures to reconstruct individual interactions, including a newly developed technique called “brems flipping,” as well as the burst direction from an ensemble of interactions are described. Performance of the burst direction reconstruction is evaluated for supernovae happening at a distance of 10 kpc for a specific supernova burst flux model. The pointing resolution is found to be 3.4 degrees at 68% coverage for a perfect interaction-channel classification and a fiducial mass of 40 kton,...

Using the Metropolis algorithm to explore the loss surface of a recurrent neural network

Fri, 1 Aug 2025 00:00:00 +0000

In the limit of small trial moves the Metropolis Monte Carlo algorithm is equivalent to gradient descent on the energy function in the presence of Gaussian white noise. This observation was originally used to demonstrate a correspondence between Metropolis Monte Carlo moves of model molecules and overdamped Langevin dynamics, but it also applies in the context of training a neural network: making small random changes to the weights of a neural network, accepted with the Metropolis probability, with the loss function playing the role of energy, has the same effect as training by explicit gradient descent in the presence of Gaussian white noise. We explore this correspondence in the context of a simple recurrent neural network. We also explore regimes in which this correspondence breaks down, where the gradient of the loss function becomes very large or small. In these regimes the Metropolis algorithm can still effect training, and so can be used as a probe of the loss function...

Spin-informed universal graph neural networks for simulating magnetic ordering

Wed, 30 Jul 2025 00:00:00 +0000

The screening and discovery of magnetic materials are hindered by the computational cost of first-principles density-functional theory (DFT) calculations required to find the ground state magnetic ordering. Although universal machine-learning interatomic potentials (uMLIPs), also known as atomistic foundation models, offer high-fidelity models of many atomistic systems with significant speedup, they currently lack the inputs required for predicting magnetic ordering. In this work, we present a data-efficient, spin-informed graph neural network framework that incorporates spin degrees of freedom as inputs and preserves physical symmetries, extending the functionality of uMLIPs to simulate magnetic orderings. This framework speeds up DFT calculations through better initial guesses for magnetic moments, determines the ground-state ordering of bulk materials and even generalizes to magnetic ordering in surfaces. Furthermore, we implement a closed-loop anomaly detection approach that...

The track-length extension fitting algorithm for energy measurement of interacting particles in liquid argon TPCs and its performance with ProtoDUNE-SP data

Fri, 25 Jul 2025 00:00:00 +0000

This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe the impact of the dE/dx model on...

Requirements for US20 collective subroutines for prefix operations

Thu, 24 Jul 2025 00:00:00 +0000

This paper contains formal requirements for Fortran 202Y work item US-20, collective subroutines for prefix operations. It passed by unanimous consent at the Jun 2025 meeting #236 of the INCITS/US Fortran Programming Language Standards Technical Committee.

Requirements for US20: Local Prefix Operation Intrinsics

Thu, 24 Jul 2025 00:00:00 +0000

This paper contains formal requirements for Fortran 202Y work item US-20, Local Prefix Operation Intrinsics. It passed by unanimous consent at the Jun 2025 meeting #236 of the INCITS/US Fortran Programming Language Standards Technical Committee.

Au147(SPh)30(PPh3)12: A Geometrically Closed, but Electronically Open Triple‐Shell Icosahedral Gold Cluster and its Geometrically Open Counterpart

Mon, 21 Jul 2025 00:00:00 +0000

The aesthetic platonic solids have been known since ancient times, and the structure of all five platonic solids is also found in chemical compounds. While gold sub-nanometer clusters and gold nanoparticles with an icosahedral structure have been known for a long time to exist, a multi-shell icosahedral gold cluster at the intermediate size between 13 and thousands of atoms has been elusive. Here we present the synthesis and crystallographic characterization of the first triple-shell icosahedral metal cluster, Au₁₄₇(SPh)₃₀(PPh₃)₁₂ 1. The gold core in 1 is stabilized by phosphines and thiolates, but surprisingly no staple motifs are formed. A second cluster, Au₁₄₆(SPh)₃₀(PPh₃)₁₂ 2, cocrystallizes and is identified as having a closed electronic shell but can be considered as a geometrically open pendant of 1. The unique clusters are characterized experimentally by EDX, UV/vis, DLS, and EPR and...

DESI 2024 II: sample definitions, characteristics, and two-point clustering statistics

Tue, 15 Jul 2025 00:00:00 +0000

Abstract: We present the samples of galaxies and quasars used for DESI 2024 cosmological analyses, drawn from the DESI Data Release 1 (DR1). We describe the construction of large-scale structure (LSS) catalogs from these samples, which include matched sets of synthetic reference `randoms' and weights that account for variations in the observed density of the samples due to experimental design and varying instrument performance. We detail how we correct for variations in observational completeness, the input `target' densities due to imaging systematics, and the ability to confidently measure redshifts from DESI spectra. We then summarize how remaining uncertainties in the corrections can be translated to systematic uncertainties for particular analyses. We describe the weights added to maximize the signal-to-noise of DESI DR1 2-point clustering measurements. We detail measurement pipelines applied to the LSS catalogs that obtain 2-point clustering measurements...

Accelerating discoveries at DIII-D with the Integrated Research Infrastructure

Wed, 2 Jul 2025 00:00:00 +0000

DIII-D research is being accelerated by leveraging high performance computing (HPC) and data resources available through the National Energy Research Scientific Computing Center (NERSC) Superfacility initiative. As part of this initiative, a high-resolution, fully automated, whole discharge kinetic equilibrium reconstruction workflow was developed that runs at the NERSC for most DIII-D shots in under 20 min. This has eliminated a long-standing research barrier and opened the door to more sophisticated analyses, including plasma transport and stability. These capabilities would benefit from being automated and executed within the larger Department of Energy Advanced Scientific Computing Research program’s Integrated Research Infrastructure (IRI) framework. The goal of IRI is to empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation. For transport, we...

Expediting Higher Fidelity Plasma State Reconstructions for the DIII-D National Fusion Facility Using Leadership Class Computing Resources

Wed, 2 Jul 2025 00:00:00 +0000

The OMFIT CAKE workflow for plasma state reconstruction has been automated on the DIII-D National Fusion Facility to run on a combination of computational resources at DIII-D and the National Energy Research Computing (NERSC) Center, utilizing the emerging DOE Integrated Research Infrastructure (IRI). The reconstruction of the plasma state is vital for understanding what occurred in the DIII-D machine during the pulse. This understanding allows informed decisions to be made on how to change the configuration for the next pulse. The initial reconstruction workflow was performed on DIII-D resources for a benchmark case in 62 minutes. The wall-clock time for the benchmark case was reduced to 11 minutes by running on the Perlmutter system at NERSC, which opens the possibility to influence decisions between DIII-D pulses during an experiment. The reconstruction results can be used as inputs for other modeling analyses; the determination of the classification of the microturbulence...

Exact block encoding of imaginary time evolution with universal quantum neural networks