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A Taxonomy of Small Markovian Errors

PRX Quantum

Blume-Kohout, Robin; Da Silva, Marcus P.; Nielsen, Erik N.; Proctor, Timothy J.; Rudinger, Kenneth M.; Sarovar, Mohan; Young, Kevin

Errors in quantum logic gates are usually modeled by quantum process matrices (CPTP maps). But process matrices can be opaque and unwieldy. We show how to transform the process matrix of a gate into an error generator that represents the same information more usefully. We construct a basis of simple and physically intuitive elementary error generators, classify them, and show how to represent the error generator of any gate as a mixture of elementary error generators with various rates. Finally, we show how to build a large variety of reduced models for gate errors by combining elementary error generators and/or entire subsectors of generator space. We conclude with a few examples of reduced models, including one with just 9N2 parameters that describes almost all commonly predicted errors on an N-qubit processor.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus

QSCOUT Progress Report, June 2022 [Quantum Scientific Computing Open User Testbed]

Clark, Susan M.; Norris, Haley; Landahl, Andrew J.; Yale, Christopher G.; Lobser, Daniel; Van Der Wall, Jay W.; Revelle, Melissa

Quantum information processing has reached an inflection point, transitioning from proof-of-principle scientific experiments to small, noisy quantum processors. To accelerate this process and eventually move to fault-tolerant quantum computing, it is necessary to provide the scientific community with access to whitebox testbed systems. The Quantum Scientific Computing Open User Testbed (QSCOUT) provides scientists unique access to an innovative system to help advance quantum computing science.

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TYPE Other Report YEAR 2022
DOIOSTI

“Deep reinforcement learning for engineering design through topology optimization of elementally discretized design domains”

Materials and Design

Garland, Anthony; Brown, Nathan K.; Fadel, Georges M.; Li, Gang

Advances in machine learning algorithms and increased computational efficiencies give engineers new capabilities and tools to apply to engineering design. Machine learning models can approximate complex functions and, therefore, can be useful for various tasks in the engineering design workflow. This paper investigates using reinforcement learning (RL), a subset of machine learning that teaches an agent to complete a task through accumulating experiences in an interactive environment, to automate the designing of 2D discretized topologies. RL agents use past experiences to learn sequential sets of actions to best achieve some objective. In the proposed environment, an RL agent can make sequential decisions to design a topology by removing elements to best satisfy compliance minimization objectives. After each action, the agent receives feedback by evaluating how well the current topology satisfies the design objectives. After training, the agent was tasked with designing optimal topologies under various load cases. The agent's proposed designs had similar or better compliance minimization performance to those produced by traditional gradient-based topology optimization methods. These results show that a deep RL agent can learn generalized design strategies to satisfy multi-objective design tasks and, therefore, shows promise as a tool for arbitrarily complex design problems across many domains.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus

Invariant surface elastic properties in FCC metals and their correlation to bulk properties revealed by machine learning methods

Journal of the Mechanics and Physics of Solids

Chen, Xiaolei; Dingreville, Remi P.M.; Richeton, Thiebaud; Berbenni, Stephane

We present a combination of machine-learned models that predicts the surface elastic properties of general free surfaces in face-centered cubic (FCC) metals. These models are built by combining a semi-analytical method based on atomistic simulations to calculate surface properties with the artificial neural network (ANN) method or the boosted regression tree (BRT) method. The latter is also used to link bulk properties and surface orientation to surface properties. The surface elastic properties are represented by their invariants considering plane elasticity within a polar method. The resulting models are shown to accurately predict the surface elastic properties of seven pure FCC metals (Cu, Ni, Ag, Au, Al, Pd, Pt). The BRT model reveals the correlations between bulk and corresponding surface properties in terms of invariants, which can be used to guide the design of complex nano-sized particles, wires and films. Finally, by expressing the surface excess energy density as a function of surface elastic invariants, fast predictions of surface energy as a function of in-plane deformations can be made from these model constructs.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus

"Did you eat a MOLEcule today?" An Interactive Demonstration of Molecular Weight with Real-Time Breath Analysis Using Mass Spectrometry for All Ages

Journal of the American Society for Mass Spectrometry

Kustas, Jessica; Redline, Erica M.; Mowry, Curtis D.

Although mass spectrometry is a widely used analytical tool, age-appropriate, interactive outreach activities for laboratory visitors, especially children, are lacking. The presented interactive demonstration, "Did you eat a MOLEcule today?", introduces all ages to molecular weight concepts and mass spectrometry in a research laboratory, while connecting the concepts to real-world applications. Through real-time breath analysis, participants explore the concepts of molecular weight, electrostatic field manipulation of charged molecules, and analyte identification by mass analysis. This module is rapid and highly adaptable for outreach activities but also includes age- or classroom-appropriate variations to decrease or increase difficulty levels. The presented interactive demonstration has repeatedly been implemented, with over 2300 participants during six annual "Take Our Daughters & Sons to Work Day" and two corporate "Family Day" outreach activities, successfully engaging, exciting, and educating both kids and parents.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus

Verification of MOOSE/Bison's Heat Conduction Solver Using Combined Spatiotemporal Convergence Analysis

Journal of Verification, Validation and Uncertainty Quantification

Toptan, Aysenur; Porter, Nathan W.; Hales, Jason D.; Jiang, Wen; Spencer, Benjamin W.; Novascone, Stephen R.

Bison is a computational physics code that uses the finite element method to model the thermo-mechanical response of nuclear fuel. Since Bison is used to inform highconsequence decisions, it is important that its computational results are reliable and predictive. One important step in assessing the reliability and predictive capabilities of a simulation tool is the verification process, which quantifies numerical errors in a discrete solution relative to the exact solution of the mathematical model. One step in the verification process-called code verification-ensures that the implemented numerical algorithm is a faithful representation of the underlying mathematical model, including partial differential or integral equations, initial and boundary conditions, and auxiliary relationships. In this paper, the code verification process is applied to spatiotemporal heat conduction problems in Bison. Simultaneous refinement of the discretization in space and time is employed to reveal any potential mistakes in the numerical algorithms for the interactions between the spatial and temporal components of the solution. For each verification problem, the correct spatial and temporal order of accuracy is demonstrated for both first- and second-order accurate finite elements and a variety of time-integration schemes. These results provide strong evidence that the Bison numerical algorithm for solving spatiotemporal problems reliably represents the underlying mathematical model in MOOSE. The selected test problems can also be used in other simulation tools that numerically solve for conduction or diffusion.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus
Results 8551–8600 of 101,000
Results 8551–8600 of 101,000
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