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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantum computing hardware for HEP algorithms and sensing

Quantum information science harnesses the principles of quantum mechanics to realize computational algorithms with complexities vastly intractable by current computer platforms. Typical applications range from quantum chemistry to optimization problems and also include simulations for high energy physics. The recent maturing of quantum hardware has triggered preliminary explorations by several institutions (including Fermilab) of quantum hardware capable of demonstrating quantum advantage in multiple domains, from quantum computing to communications, to sensing. The Superconducting Quantum Materials and Systems (SQMS) Center, led by Fermilab, is dedicated to providing breakthroughs in quantum computing and sensing, mediating quantum engineering and HEP based material science. The main goal of the Center is to deploy quantum systems with superior performance tailored to the algorithms used in high energy physics. In this Snowmass paper, we discuss the two most promising superconducting quantum architectures for HEP algorithms, i.e. three-level systems (qutrits) supported by transmon devices coupled to planar devices and multi-level systems (qudits with arbitrary N energy levels) supported by superconducting 3D cavities. For each architecture, we demonstrate exemplary HEP algorithms and identify the current challenges, ongoing work and future opportunities. Furthermore, we discuss the prospects and complexities of interconnecting the different architectures and individual computational nodes. Finally, we review several different strategies of error protection and correction and discuss their potential to improve the performance of the two architectures. This whitepaper seeks to reach out to the HEP community and drive progress in both HEP research and QIS hardware.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Hybrid Particle Swarm-Interpolation Algorithm for Improved Critical Experiment Design

The typical goal of designing a critical experiment is twofold: a system that is both critical and optimized for some other value. This value could be an energy-integrated sensitivity, percent fissions in a certain energy range, or some other value that can be calculated by a transport code. By simulating different combinations of reflector, moderator, and fuel thicknesses a designer can identify such a desirable configuration. The domain of all possible combinations of these thicknesses is referred to as the experiment search space. As more dimensions are added, the search space increases in size exponentially. For a three-dimensional case, which includes three thickness values between zero and ten centimeters to the nearest tenth of a millimeter, a total of 1,000 3 , configurations exists. Rather than check each configuration individually, which would be extremely computationally expensive, it has been shown to be more efficient to use a conventional particle swarm optimization (PSO) algorithm to identify critical and optimal configurations. This work presents the theory and implementation of a novel hybrid PSO interpolation algorithm to perform these optimizations faster than a conventional PSO algorithm. To demonstrate this, an example optimization will be carried out by the conventional and hybrid PSO algorithms and their performances will be compared.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Survey of Time Shift Detection Algorithms for Measured PV Data

In this research, three variations of time shift detection algorithms were tested for their ability to detect time shift issues (including daylight savings time and random time shifts) in measured PV data sets. Two algorithms from the Python PVAnalytics package were assessed, and one algorithm from the Solar-Data-Tools package was assessed. Each algorithm's ability to accurately detect and measure time shifts was assessed.

automated preprocessing↗
DOE OSTI2022

An in-line burn-up algorithm: proposal and preliminary analysis

A new burn-up algorithm is proposed for coupling neutronics and isotopic depletion. The algorithm is analogous to those proposed for enforcing xenon equilibrium and converging neutronics and thermal-hydraulics. During the corrector step of standard predictor-corrector methods, the corrector nuclide density is updated as neutronics converges. This is expected to improve stability while reducing computational expense compared to other implicit algorithms. The stability of the algorithm is investigated by Fourier analysis for a simple case whose results have been successfully compared with numerical experiments.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Noise-induced transition in optimal solutions of variational quantum algorithms

Variational quantum algorithms are promising candidates for delivering practical quantum advantage on noisy intermediate-scale quantum (NISQ) hardware. However, optimizing the noisy cost functions associated with these algorithms is challenging for system sizes relevant to quantum advantage. In this work, we investigate the effect of noise on optimization by studying a variational quantum eigensolver (VQE) algorithm calculating the ground state of a spin chain model, and we observe an abrupt transition induced by noise to the optimal solutions. We will present numerical simulations, a demonstration using an IBM quantum processor unit (QPU), and a theoretical analysis indicating the origin of this transition. Our findings suggest that careful analysis is crucial to avoid misinterpreting the noise-induced features as genuine algorithm results.

Li, Andy C.Y.↗
DOE OSTI2024

An Efficient Numerical Algorithm for Solving Coupled Time-Dependent Ginzburg-Landau Equation for Superconductivity and Elasticity

A decoupled finite element algorithm is developed for simulating the vortex dynamics on an elastic superconductor which couples the time-dependent Ginzburg- Landau equation with the complex-valued superconducting order parameter and the vector-valued magnetic potential, and the elasticity equation. We present an iterative algorithm for the decoupled system arising from the time and spatial discretization using a combination of preconditioner, algebraic multigrid method (AMG) and preconditioned conjugate gradient method (PCG). The iterative algorithm allows us to perform large-scale three-dimensional simulations of mesoscale pattern formation during superconducting phase transitions with arbitrary elastic boundary conditions. Here, the performance and efficiency of the algorithm are numerically verified by several benchmark problems, exhibiting up to two orders of magnitude improvement depending on the scale of discrete system compared to the exact solver.

Efficiency↗
DOE OSTI2024

Development of online pulse-finding algorithms for the Mu2e Stopping Target Monitor and an evaluation of the signal to background ratio for the Mu2e stopped-muon rate determination

This thesis presents a determination of the acceptance of the Mu2e Stopping Target Monitor (STM) detector for both signal and background events. The rate of X-rays of a specific energy in the STM is used to determine the rate of stopped muons in the aluminium stopping target, which is used as the normalisation for all Mu2e measurements, particularly the branching ratio for a charged lepton flavour violating (cLFV) process. The acceptance for the 347\,keV $2p-1s$ aluminium signal X-ray is determined to be $(8.03 \pm 0.05) \times 10^{-10}$\,cm$^{-2}$ and the acceptance for the background photons (at 347\,keV) to be $(3.19 \pm 0.21) \times 10^{-8}$\,cm$^{-2}$. This results in a signal-to-background ratio of approximately two which is significantly higher than had previously been estimated. These signal-to-background rates are used to determine the time required to achieve a statistical uncertainty of 10\% on the number of 347\,keV X-rays as a function of the detector resolution and back ground rate. For a resolution of 2\,keV, and the background rate determined in this thesis, approximately one minute of data is required. The three corrections needed to be applied to the measured 347\,keV rate to determine the rate of stopped muons are also determined and potential sources of bias arising from the background shape, background fit range and energy calibration are investigated and found to be insignificant. The raw energy of the particles in the STM detector comes from a 16-bit ADC digitising at $\sim 300$\,MHz. It is not possible to store all the raw digitisations and online algorithms have been developed to rapidly select pulses in the detector and reconstruct their energy. The performance of these algorithms has been characterised with simulated data as a function of detector rate and resolution and optimum operating points for the algorithms have been defined. The integrity of the algorithms has also been demonstrated and characterised on test-beam data at high r ates and with radioactive sources at low rates.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2011

Model-Based Clustering of Regression Time Series Data via APECM -- An AECM Algorithm Sung to an Even Faster Beat

We propose a model-based approach for clustering time series regression data in an unsupervised machine learning framework to identify groups under the assumption that each mixture component follows a Gaussian autoregressive regression model of order p. Given the number of groups, the traditional maximum likelihood approach of estimating the parameters using the expectation-maximization (EM) algorithm can be employed, although it is computationally demanding. The somewhat fast tune to the EM folk song provided by the Alternating Expectation Conditional Maximization (AECM) algorithm can alleviate the problem to some extent. In this article, we develop an alternative partial expectation conditional maximization algorithm (APECM) that uses an additional data augmentation storage step to efficiently implement AECM for finite mixture models. Results on our simulation experiments show improved performance in both fewer numbers of iterations and computation time. The methodology is applied to the problem of clustering mutual funds data on the basis of their average annual per cent returns and in the presence of economic indicators.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Slow manifolds of classical Pauli particle enable structure-preserving geometric algorithms for guiding center dynamics

Since variational symplectic integrators for the guiding center was proposed, structure-preserving geometric algorithms have become an active research field in plasma physics. We found that the slow manifolds of the classical Pauli particle enable a family of structure-preserving geometric algorithms for guiding center dynamics with long-term stability and accuracy. This discovery overcomes the difficulty associated with the unstable parasitic modes for variational symplectic integrators when applied to the degenerate guiding center Lagrangian. It is a pleasant surprise that Pauli's Hamiltonian for electrons, which predated the Dirac equation and marks the beginning of particle physics, reappears in classical physics as an effective algorithm for solving an important plasma physics problem. This technique is applicable to other degenerate Lagrangians reduced from regular Lagrangians.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A scalable algorithm for the optimization of neural network architectures

In this work, we propose a new scalable method to optimize the architecture of an artificial neural network. The proposed algorithm, called Greedy Search for Neural Network Architecture, aims to determine a neural network with minimal number of layers that is at least as performant as neural networks of the same structure identified by other hyperparameter search algorithms in terms of accuracy and computational cost. Numerical results performed on benchmark datasets show that, for these datasets, our method outperforms state-of-the-art hyperparameter optimization algorithms in terms of attainable predictive performance by the selected neural network architecture, and time-to-solution for the hyperparameter optimization to complete.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Fock-Space Schrieffer–Wolff Transformation: Classically-Assisted Rank-Reduced Quantum Phase Estimation Algorithm

We present an extension of many-body downfolding methods to reduce the resources required in the quantum phase estimation (QPE) algorithm. In this paper, we focus on the Schrieffer–Wolff (SW) transformation of the electronic Hamiltonians for molecular systems that provides significant simplifications of quantum circuits for simulations of quantum dynamics. We demonstrate that by employing Fock-space variants of the SW transformation (or rank-reducing similarity transformations (RRST)) one can significantly increase the locality of the qubit-mapped similarity-transformed Hamiltonians. The practical utilization of the SW-RRST formalism is associated with a series of approximations discussed in the manuscript. In particular, amplitudes that define RRST can be evaluated using conventional computers and then encoded on quantum computers. The SW-RRST QPE quantum algorithms can also be viewed as an extension of the standard state-specific coupled-cluster downfolding methods to provide a robust alternative to the traditional QPE algorithms to identify the ground and excited states for systems with various numbers of electrons using the same Fock-space representations of the downfolded Hamiltonian. The RRST formalism serves as a design principle for developing new classes of approximate schemes that reduce the complexity of quantum circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2010

The Development of a Parameterized Scatter Removal Algorithm for NMIS Imaging

Neutron radiography is a powerful investigative technique for analyzing the interior structure of an object. If the true attenuation of neutrons at each position in the object can be measured, the cross-sections can be computed using the exponential attenuation formula. However, scattered neutrons will contribute to the measured neutron flux and reduce the measured attenuation. The Nuclear Materials Identification System (NMIS) uses a time-tagged and electronically collimated DT neutron source to reduce this effect; however, some of the scattering component remains. This work used Monte Carlo simulations to calculate the scattering component for a variety of scenarios. The scattering for each scenario was fit using a Gaussian distribution to derive a point scatter function. These point scatter functions were used to develop a parameterized scatter removal algorithm that can calculate and remove the scattering component for a wide range of scenarios based on operator inputs. The process used to calculate the scatter functions and develop the scatter removal algorithm is discussed in detail. Examples of simulated and experimental measurements are used to test and validate the algorithm.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

A low-communication, parallel algorithm for solving PDEs based on range decomposition

This work proposes a new, low-communication algorithm for solving PDEs on massively parallel computers. The range decomposition (RD) algorithm exposes coarse-grain parallelism by applying nested iteration and adaptive mesh refinement locally before performing a global communication step. Just a few such steps are observed to be sufficient to obtain accuracy within a small multiple of discretization error. The target applications are petascale and exascale machines, where hierarchical parallelism is required and traditional parallel numerical PDE communication patterns are costly because of message latency. The RD algorithm uses a partition of unity to equally distribute the error, and thus, the work. The computational advantages of this approach are that the decomposed problems can be solved in parallel without any communication until the partitioned solutions are summed. This offers potential advantages in the paradigm of expensive communication but very cheap computation. This paper introduces the method and explains the details of the communication step. Two performance models are developed, showing that the latency cost associated with a traditional parallel implementation of nested iteration is proportional to log(P) 2 , whereas the RD method reduces the communication latency to log(P), while maintaining similar bandwidth costs. Numerical results for two problems, Laplace and advection diffusion, demonstrate the enhanced performance, and a heuristic argument explains why the method converges quickly.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Spontaneous Intramuscular Hematomas of the Abdomen and Pelvis: A New Multilevel Algorithm to Direct Transarterial Embolization and Patient Management

PurposeTo report our experience using a multilevel patient management algorithm to direct transarterial embolization (TAE) in managing spontaneous intramuscular hematoma (SIMH).Materials and MethodsFrom May 2006 to January 2014, twenty-seven patients with SIMH had been referred for TAE to our Radiology department. Clinical status and coagulation characteristics of the patients are analyzed. An algorithm integrating CT findings is suggested to manage SIMH. Patients were classified into three groups: Type I, SIMH with no active bleeding (AB); Type II, SIMH with AB and no muscular fascia rupture (MFR); and Type III, SIMH with MFR and AB. Type II is furthermore subcategorized as IIa, IIb and IIc. Types IIb, IIc and III were considered for TAE. The method of embolization as well as the material been used are described. Continuous variables are presented as mean ± SD. Categorical variables are reported as percentages. Technical success, clinical success, complications and 30-day mortality (d30 M) were analyzed.ResultsTwo patients (7.5%) had Type IIb, four (15%) Type IIc and 21 (77.5%) presented Type III. The detailed CT and CTA findings, embolization procedure and materials used are described. Technical success was 96% with a complication rate of 4%. Clinical success was 88%. The bleeding-related thirty-day mortality was 15% (all with Type III).ConclusionTAE is a safe and efficient technique to control bleeding that should be considered in selected SIMH as soon as possible. The proposed algorithm integrating CT features provides a comprehensive chart to select patients for TAE.Level of Evidence4.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

A genetic meta-algorithm-assisted inversion approach: hydrogeological study for the determination of volumetric rock properties and matrix and fluid parameters in unsaturated formations

An evolutionary inversion approach is suggested for the interpretation of nuclear and resistivity logs measured by direct-push tools in shallow unsaturated sediments. The efficiency of formation evaluation is improved by estimating simultaneously (1) the petrophysical properties that vary rapidly along a drill hole with depth and (2) the zone parameters that can be treated as constant, in one inversion procedure. In the workflow, the fractional volumes of water, air, matrix and clay are estimated in adjacent depths by linearized inversion, whereas the clay and matrix properties are updated using a float-encoded genetic meta-algorithm. The proposed inversion method provides an objective estimate of the zone parameters that appear in the tool response equations applied to solve the forward problem, which can significantly increase the reliability of the petrophysical model as opposed to setting these parameters arbitrarily. The global optimization meta-algorithm not only assures the best fit between the measured and calculated data but also gives a reliable solution, practically independent of the initial model, as laboratory data are unnecessary in the inversion procedure. The feasibility test uses engineering geophysical sounding logs observed in an unsaturated loessy-sandy formation in Hungary. The multi-borehole extension of the inversion technique is developed to determine the petrophysical properties and their estimation errors along a profile of drill holes. The genetic meta-algorithmic inversion method is recommended for hydrogeophysical logging applications of various kinds to automatically extract the volumetric ratios of rock and fluid constituents as well as the most important zone parameters in a reliable inversion procedure.

58 GEOSCIENCES↗
DOE OSTI2016

Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs

We present a method for computing lower bounds in the progressive hedging algorithm (PHA) for two-stage and multi-stage stochastic mixed-integer programs. Computing lower bounds in the PHA allows one to assess the quality of the solutions generated by the algorithm contemporaneously. The lower bounds can be computed in any iteration of the algorithm by using dual prices that are calculated during execution of the standard PHA. In conclusion, we report computational results on stochastic unit commitment and stochastic server location problem instances, and explore the relationship between key PHA parameters and the quality of the resulting lower bounds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Verification of an Algorithm for Processing Data from Multifrequency Lidar Sensing of Aerosols

We consider an algorithm for processing lidar signals, which for a lidar path with constant qualitative aerosol characteristics (particle size distribution in each aerosol mode, complex refractive index of the particles) lets us obtain a concentration profile for the fine aerosol component without using additional a priori information. The algorithm was verified based on lidar data from the Institute of Physics (National Academy of Sciences of Belarus). The vertical volume concentration profile for fine particles obtained by our algorithm is quite consistent with the results of its independent retrieval, based on integrated processing of lidar and sun photometer data.

42 ENGINEERING↗
DOE OSTI2020

Decomposition Algorithms for Solving NP-hard Problems on a Quantum Annealer

NP-hard problems such as the maximum clique or minimum vertex cover problems, two of Karp’s 21 NP-hard problems, have several applications in computational chemistry, biochemistry and computer network security. Adiabatic quantum annealers can search for the optimum value of such NP-hard optimization problems, given the problem can be embedded on their hardware. However, this is often not possible due to certain limitations of the hardware connectivity structure of the annealer. This paper studies a general framework for a decomposition algorithm for NP-hard graph problems aiming to identify an optimal set of vertices. Our generic algorithm allows us to recursively divide an instance until the generated subproblems can be embedded on the quantum annealer hardware and subsequently solved. Furthermore, the framework is applied to the maximum clique and minimum vertex cover problems, and we propose several pruning and reduction techniques to speed up the recursive decomposition. The performance of both algorithms is assessed in a detailed simulation study.

97 MATHEMATICS AND COMPUTING↗