Potential Applications of Quantum Computers at Los Alamos National Laboratory
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36 MATERIALS SCIENCE↗
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AMReX: Block-structured adaptive mesh refinement for multiphysics applications
Block-structured adaptive mesh refinement (AMR) provides the basis for the temporal and spatial discretization strategy for a number of Exascale Computing Project applications in the areas of accelerator design, additive manufacturing, astrophysics, combustion, cosmology, multiphase flow, and wind plant modeling. AMReX is a software framework that provides a unified infrastructure with the functionality needed for these and other AMR applications to be able to effectively and efficiently utilize machines from laptops to exascale architectures. AMR reduces the computational cost and memory footprint compared to a uniform mesh while preserving accurate descriptions of different physical processes in complex multiphysics algorithms. AMReX supports algorithms that solve systems of partial differential equations in simple or complex geometries and those that use particles and/or particle–mesh operations to represent component physical processes. In this article, we will discuss the core elements of the AMReX framework such as data containers and iterators as well as several specialized operations to meet the needs of the application projects. In addition, we will highlight the strategy that the AMReX team is pursuing to achieve highly performant code across a range of accelerator-based architectures for a variety of different applications.
Zhang, Weiqun↗
Three-dimensional electron temperature measurement of inertial confinement fusion hotspots using x-ray emission tomography
Here we present a novel approach to reconstruct three-dimensional (3D) electron temperature distributions of inertially confined fusion plasma hotspots at the National Ignition Facility. Using very limited number of two-dimensional (2D) x-ray imaging lines of sight, we perform 3D reconstructions of x-ray emission distributions from different x-ray energy channels ranging from 20 to 30 keV. 2D time-integrated x-ray images are processed using the algebraic reconstruction technique to reconstruct a 3D hotspot x-ray emission distribution that is self-consistent with the input images. 3D electron temperatures are computed using the energy channel ratios. We demonstrate the high accuracy and applicability of this method with different complex hotspot geometries in both synthetic and experimental results.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
mystic : software for autonomous discovery and design under uncertainty
Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.
42 ENGINEERING↗
ML-based Data Assimilation and History Matching: Application to the IBDP CCS Project
It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficiency. However, the computational cost of existing data assimilation methods can be prohibitively expensive due to the complex physics with multi-component non-isothermal simulation and high dimensionality of large-scale reservoir models. We address this challenge by proposing an accelerated deep learning-based workflow for model calibration and prediction of CO2 plume evolution in the reservoir.The power and efficacy of our workflow is demonstrated by application to the Illinois Basin-Decatur Project (IBDP), a large-scale CO2 storage test in saline aquifer. The data assimilation process is implemented rapidly by the proposed workflow with given field measurements including distributed pressure and temperature sensing (DTS) data at an injection and a monitoring well. CO2 plume evolution is predicted by running the simulations of the calibrated reservoir models.
Nagao, Masahiro↗
Multi-scale Coupled Processes Modeling of Fractures as Porous, Interfacial and Granular Systems from Rock Images with the Numerical Manifold Method
Abstract The greatest challenges of rigorously modeling coupled hydro-mechanical processes in fractured rocks at different scales are associated with computational geometry. In addition, selections of continuous or discontinuous models, physical laws, and coupling priorities at different scales based on different geometric features determine the applicability of a numerical model for a certain type of problem. In this study, we present our multi-scale modeling capabilities that have been developed based on the numerical manifold method for analyzing coupled hydro-mechanical processes in fractured rocks. Based on their geometric features, the fractures are modeled as continua—finite-thickness porous zones, and discontinua—discontinuous interfaces and microscale asperities and granular systems. Different governing equations, physical laws, coupling priorities, and approaches for addressing fracture intersections and shearing are then applied to describe these. We applied these models to simulate coupled processes in fractured rocks using realistic geometry obtained from rock images at different scales. We first calculated shearing of a single fracture with different models and demonstrated the impacts of asperities on shearing. We then applied the continuous and discontinuous models to simulate a network of rough fractures, demonstrating that contact dynamics contribute significantly to the geometric, multi-physical evolution of systems where rough fractures are not mineral filled. For a discrete fracture network, our coupled processes modeling demonstrates that shearing of the discrete fractures can have a major impact on stress and pore pressure distribution. Lastly, we applied the discontinuous granular model to simulate evolution of a complex granular system with a deformation band, demonstrating that the deformation band can dominate contact dynamics, the structural and the stress evolution of the granular system.
58 GEOSCIENCES↗
Search for Majorana neutrinos exploiting millikelvin cryogenics with CUORE
The possibility that neutrinos may be their own antiparticles, unique among the known fundamental particles, arises from the symmetric theory of fermions proposed by Ettore Majorana in 1937. Given the profound consequences of such Majorana neutrinos, among which is a potential explanation for the matter–antimatter asymmetry of the universe via leptogenesis, the Majorana nature of neutrinos commands intense experimental scrutiny globally; one of the primary experimental probes is neutrinoless double beta (0νββ) decay. Here we show results from the search for 0νββ decay of 130 Te, using the latest advanced cryogenic calorimeters with the CUORE experiment3. CUORE, operating just 10 millikelvin above absolute zero, has pushed the state of the art on three frontiers: the sheer mass held at such ultralow temperatures, operational longevity, and the low levels of ionizing radiation emanating from the cryogenic infrastructure. We find no evidence for 0νββ decay and set a lower bound of the process half-life as 2.2 × 10 25 years at a 90 percent credibility interval. We discuss potential applications of the advances made with CUORE to other fields such as direct dark matter, neutrino and nuclear physics searches and large-scale quantum computing, which can benefit from sustained operation of large payloads in a low-radioactivity, ultralow-temperature cryogenic environment.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Electrically tuned hyperfine spectrum in neutral Tb( II )(Cp iPr5 ) 2 single-molecule magnet
Molecular spin qubits with long spin coherence time as well as non-invasive operation methods on such qubits are in high demand. It was shown that both molecular electronic and nuclear spin levels can be used as qubits. In solid state systems with dopants, an electric field was shown to effectively change the spacing between the nuclear spin qubit levels when the electron spin density is high at the nucleus of the dopant. Inspired by such solid-state systems, we propose that divalent lanthanide (Ln) complexes with an unusual electronic configuration of Ln 2+ have a strong interaction between the Ln nuclear spin and the electronic degrees of freedom, which renders electrical tuning of the interaction. Furthermore, as an example, we study electronic structure and hyperfine interaction of the 159 Tb nucleus in a neutral Tb(II)(Cp iPr5 ) 2 single-molecule magnet (SMM), which exhibits unusually long magnetization relaxation time, using the complete active space self-consistent field (CASSCF) method with spin–orbit interaction included within the restricted active space state interaction (RASSI). Our calculations show that the low-energy states arise from 4f 8 (6s,5d z 2 ) 1 , 4f 8 (5d x 2 -y 2 ) 1 , and 4f 8 (5d xy ) 1 configurations. We compute the hyperfine interaction parameters and the electronic–nuclear spectrum within our multiconfigurational approach. We find that the hyperfine interaction is about one order of magnitude greater than that for Tb(III)Pc 2 SMMs. This stems from the strong Fermi contact interaction between the Tb nuclear spin and the electron spin density at the nucleus that originates from the occupation of the (6s,5d) orbitals. We also uncover that the response of the Fermi contact term to electric field results in electrical tuning of the electronic–nuclear level separations. This hyperfine Stark effect may be useful for applications of molecular nuclear spins for quantum computing.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Investigating magnetic van der Waals materials using data-driven approaches
In this work, we investigate magnetic monolayers of the form A i A ii B 4 X 8 based on the well-known intrinsic topological magnetic van der Waals (vdW) material MnBi 2 Te 4 (MBT) using first-principles calculations and machine learning techniques. We select an initial subset of structures to calculate the thermodynamic properties, electronic properties, such as the band gap, and magnetic properties, such as the magnetic moment and magnetic order using density functional theory (DFT). Data analytics approaches are used to gain insight into the microscopic origin of materials’ properties. The dependence of materials’ properties on chemical composition is also explored. For example, we find that the formation energy and magnetic moment depend largely on A and B sites whereas the band gap depends on all three sites. Finally, we employ machine learning tools to accelerate the search for novel vdW magnets in the MBT family with optimized properties. Finally, this study creates avenues for rapidly predicting novel materials with desirable properties that could enable applications in spintronics, optoelectronics, and quantum computing.
36 MATERIALS SCIENCE↗
Metaplectic geometrical optics for modeling caustics in uniform and non-uniform media
As an approximate theory that is highly regarded for its computational efficiency, geometrical optics (GO) is widely used for modeling waves in various areas of physics. However, GO fails at caustics, which significantly limits its applicability. A new framework, called metaplectic geometrical optics (MGO), has recently been developed that allows caustics of certain types to be modeled accurately within the GO framework. Here, we extend MGO to the most general case. Further, to illustrate our new theory, we also apply it to several sample problems, including calculations of two-dimensional wavefields near fold and cusp caustics. In contrast with traditional GO solutions, the corresponding MGO solutions are finite everywhere and approximate the true wavefield well near these caustics.
79 ASTRONOMY AND ASTROPHYSICS↗
Infinite neural network quantum states: entanglement and training dynamics
We study infinite limits of neural network quantum states (∞-NNQS), which exhibit representation power through ensemble statistics, and also tractable gradient descent dynamics. Ensemble averages of entanglement entropies are expressed in terms of neural network correlators, and architectures that exhibit volume-law entanglement are presented. The analytic calculations of entanglement entropy bound are tractable because the ensemble statistics are simplified in the Gaussian process limit. A general framework is developed for studying the gradient descent dynamics of neural network quantum states (NNQS), using a quantum state neural tangent kernel (QS-NTK). For ∞-NNQS the training dynamics is simplified, since the QS-NTK becomes deterministic and constant. An analytic solution is derived for quantum state supervised learning, which allows an ∞-NNQS to recover any target wavefunction. Numerical experiments on finite and infinite NNQS in the transverse field Ising model and Fermi Hubbard model demonstrate excellent agreement with theory. ∞-NNQS opens up new opportunities for studying entanglement and training dynamics in other physics applications, such as in finding ground states.
quantum state supervised learning↗
Exploiting universal nonlocal dispersion in optically active materials for spectro-polarimetric computational imaging
Recent years have seen significant advancements in exploring novel light-matter interactions such as hyperbolic dispersion within natural crystals. However, current studies have predominantly concentrated on local optical response of materials characterized by a dielectric tensor without spatial dispersion. Here, we investigate the nonlocal response in optically-active crystals with screw symmetries, revealing their lossless, super-dispersive properties compared to traditional optical response functions. We leverage this universal nonlocal dispersion, i.e. the dispersion of optical rotatory power, to explore a novel spectral de-multiplexing scheme compared to conventional gratings, prisms and metasurfaces. We design and demonstrate an ‘Nonlocal-Cam’ - a camera that exploits nonlocal dispersion through sampling of polarized spectral states and the application of computational spectral reconstruction algorithms. The Nonlocal-Cam captures information in both laboratory and outdoor field experiments which is unavailable to traditional intensity cameras - the spectral texture of polarization. Merging the fields of nonlocal electrodynamics and computational imaging, our work paves the way for exploiting nonlocal optics of optically active materials in a variety of applications, from biological microscopy to physics-driven machine vision and remote sensing.
Wang, Xueji [Purdue Univ., West Lafayette, IN (Uni↗
Thermal Spectrum Molten Salt-Fueled Reactor Reference Plant Model
This report details a fully coupled neutronics thermal hydraulics reference plant model for a thermal spectrum molten salt-fueled reactor. The Multiphysics model is developed on the Nuclear Regulatory Commission's Comprehensive Reactor Analysis Bundle available on the Idaho National Laboratory high-performance computer, which natively and seamlessly couples Griffin and Sam codes of the Multiphysics Object-Oriented Simulation Environment (MOOSE) based applications. Griffin provides the reactor physics capabilities, k-eigenvalue, delayed neutron precursor concentrations, and transient solutions for stationary and flowing fuels. Sam provides the solution of mass, momentum, and energy conservation equations of the whole system with a set of system components for one-dimensional single-phase flow. The neutronics feedback model relies primarily on fuel and moderator temperatures, fuel salt density, and delayed neutron precursors distributions. This report presents our results for the coupled steady state core and an unprotected loss of flow event. Although this model is prototypical regarding capabilities in the Comprehensive Reactor Analysis Bundle, its results are consistent with experimental data of the pump transient tests that were performed at zero power of the Molten Salt Reactor Experiment (MSRE).
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
AI-Enhanced Co-Design for Next-Generation Microelectronics: Innovating Innovation (Workshop Report)
The Artificial Intelligence Enhanced Co-Design for Next Generation Microelectronics virtual workshop was held April 4-5, 2023, and attended by subject matter experts from universities, industry, and national laboratories. This was the third in a series of workshops to motivate the research community to identify and address major challenges facing microelectronics research and production. The 2023 workshop focused on a set of topics from materials to computing algorithms, and included discussions on relevant federal legislation and such as the Creating Helpful Incentives to Produce Semiconductors and Science Act (CHIPS Act) which was signed into law in the summer of 2022. Talks at the workshop included edge computing in radiation environments, new materials for neuromorphic computing, advanced packaging for microelectronics, and new AI techniques. We also received project updates from several of the Department of Energy (DOE) microelectronics co-design projects funded in the fall of 2021, and from three of the Energy Frontier Research Centers (EFRCs) that had been funded in the fall of 2022. The workshop also conducted a set of breakout discussions around the five principal research directions (PRDs) from the 2018 Department of Energy workshop report: 1) define innovative material, device, and architecture requirements driven by applications, algorithms, and software; 2) revolutionize memory and data storage; 3) re-imagine information flow unconstrained by interconnects; 4) redefine computing by leveraging unexploited physical phenomena; 5) reinvent the electricity grid through new materials, devices, and architectures. We tasked each breakout group to consider one primary PRD (and other PRDs as relevant topics arose during discussions) and to address questions such as whether the research community has embraced co-design as a methodology and whether new developments at any level of innovation from materials to programming models requires the research community to reevaluate the PRDs developed back in 2018.
97 MATHEMATICS AND COMPUTING↗
Grassmannian Shape Representations for Aerodynamic Applications: Preprint
Airfoil shape design is a classical problem in engineering, science, and manufacturing. Our motivation is to combine principled physics-based considerations for the shape design problem with modern computational techniques informed by a data-driven approach. Traditional analyses of airfoil shapes emphasize a flow-based sensitivity to deformations which can be represented generally by affine transformations (rotation, scaling, shearing, shifting). We present a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data, (ii) improved low-dimensional parameter domain for inferential statistics, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes.
blade representation↗
Efficient Reinforcement Learning for Real-Time Hardware-Based Energy System Experiments: Preprint
In the context of urgent climate challenges and the pressing need for rapid technology development, Reinforcement Learning (RL) stands as a compelling data-driven method for controlling real-world physical systems. However, RL implementation often entails time-consuming and computationally intensive data collection and training processes, rendering them inefficient for real-time applications that lack non-real-time models. To address these limitations, real-time emulation techniques have emerged as valuable tools for the lab-scale rapid prototyping of intricate energy systems. While emulated systems offer a bridge between simulation and reality, they too face constraints, hindering comprehensive characterization, testing, and development. In this research, we construct a surrogate model using limited data from simulated systems, enabling an efficient and effective training process for a Double Deep Q-Network (DDQN) agent for future deployment. Our approach is illustrated through a hydropower application, demonstrating the practical impact of our approach on climate-related technology development.
deep Q-learning↗
Physics-informed machine learning with optimization-based guarantees: Applications to AC power flow
Not Available
97 MATHEMATICS AND COMPUTING↗
Unstructured global to coastal wave modeling for the Energy Exascale Earth System Model using WAVEWATCH III version 6.07
Wind-wave processes have generally been excluded from coupled Earth system models due to the high computational expense of spectral wave models, which resolve a frequency and direction spectrum of waves across space and time. Existing uniform-resolution wave modeling approaches used in Earth system models cannot appropriately represent wave climates from global to coastal ocean scales, largely because of tradeoffs between coastal resolution and computational costs. To resolve this challenge, we introduce a global unstructured mesh capability for the WAVEWATCH III (WW3) model that is suitable for coupling within the US Department of Energy's Energy Exascale Earth System Model (E3SM). The new unstructured WW3 global wave modeling approach can provide the accuracy of higher global resolutions in coastal areas at the relative cost of lower uniform global resolutions. This new capability enables simulation of waves at physically relevant scales as needed for coastal applications.
58 GEOSCIENCES↗