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At least 811 records · Page 45

Digitizing gauge fields: Lattice Monte Carlo results for future quantum computers

We report that in the near-future noisy intermediate-scale quantum (NISQ) era of quantum computing technology, applications of quantum computing will be limited to calculations of very modest scales in terms of the number of qubits used. The need to represent numeric quantities using limited resources leads to digitization errors which must be taken into account. As a first step towards quantum simulations of realistic high-energy physics problems, we explore classically the effects of digitizing elements of the $\mathrm{SU}(2)$ gauge group to a finite set. We consider several methods for digitizing the group, finding the best performance from an action-preserving projection onto a mesh. Working in (3+1) dimensions, we find that using $\sim 7$ (qu)bits to represent each $\mathrm{SU}(2)$ gauge link induces a digitization error on the order of $10\%$ in short-distance observables and $2\%$ in long-distance observables. Promisingly, our results indicate that each $\mathrm{SU}(2)$ gauge link can be represented by $\mathcal{O}(10)$ (qu)bits, from which we estimate that a $16^3$ $\mathrm{SU}(2)$ lattice could be simulated with no more than $\mathcal{O}(10^5)$ (qu)bits. Lastly, our results on digitization are also of interest as a form of lossy compression that could be used in high-performance classical computing to alleviate communications bottlenecks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

SU-E-T-314: The Application of Cloud Computing in Pencil Beam Scanning Proton Therapy Monte Carlo Simulation

Purpose: Monte Carlo simulation plays an important role for proton Pencil Beam Scanning (PBS) technique. However, MC simulation demands high computing power and is limited to few large proton centers that can afford a computer cluster. We study the feasibility of utilizing cloud computing in the MC simulation of PBS beams. Methods: A GATE/GEANT4 based MC simulation software was installed on a commercial cloud computing virtual machine (Linux 64-bits, Amazon EC2). Single spot Integral Depth Dose (IDD) curves and in-air transverse profiles were used to tune the source parameters to simulate an IBA machine. With the use of StarCluster software developed at MIT, a Linux cluster with 2–100 nodes can be conveniently launched in the cloud. A proton PBS plan was then exported to the cloud where the MC simulation was run. Results: The simulated PBS plan has a field size of 10×10cm{sup 2}, 20cm range, 10cm modulation, and contains over 10,000 beam spots. EC2 instance type m1.medium was selected considering the CPU/memory requirement and 40 instances were used to form a Linux cluster. To minimize cost, master node was created with on-demand instance and worker nodes were created with spot-instance. The hourly cost for the 40-node cluster was $0.63 and the projected cost for a 100-node cluster was $1.41. Ten million events were simulated to plot PDD and profile, with each job containing 500k events. The simulation completed within 1 hour and an overall statistical uncertainty of < 2% was achieved. Good agreement between MC simulation and measurement was observed. Conclusion: Cloud computing is a cost-effective and easy to maintain platform to run proton PBS MC simulation. When proton MC packages such as GATE and TOPAS are combined with cloud computing, it will greatly facilitate the pursuing of PBS MC studies, especially for newly established proton centers or individual researchers.

07 ISOTOPE AND RADIATION SOURCES↗

From Vibrational Spectroscopy to Force Fields and Structures of Saccharides: New Computational Algorithms and Applications

This work was undertaken with the main objective to investigate basic reactions that take place in relatively simple saccharides (mono-saccharides and cellobiose - the building block of cellulose) , in isolation and in cluster with few water molecules or with (gas-phase) clusters of few waters and ionic compounds (salt, isolated ions like H + or OH - ). Within the context of this work, different potentials were investigated; among them, were the PM3 semi empirical potential, DFT/BLYP and a new hybrid potential constructed from MP2 for the harmonic part and from adjusted Hartree-Fock anharmonic interactions (VSCF-PT2). These potentials were evaluated by comparison with experimental data from published sources and from several collaborating groups. The findings show excellent agreement between experiments and predictions with the hybrid VSCF-PT2 potential and very good agreement with predictions obtained from dynamics with dispersion corrected DFT/BLYP potential. Investigation of hydration of cellobiose, was another topic of interest. Guided by a hydration motif demonstrated by our experimental collaborators (team of Prof J.P. Simons), we demonstrated large energetic and structural differences between the two species of cellobiose: cis and trans. The later, which is dominant in solid and liquid phases, is higher in energy in the gas-phase and compared to pure water, it does not disturb as much the network of H bonds. In contrast, the cis species exhibits asymmetric hydration in cluster with up to 25 waters, indicating that it has surfactant properties. Another highlight of this research effort was the successful first time spectrometric and spectroscopic study of a gas-phase protonated sugar derivative (alpha-D-Galactopyranoside) and its interpretation by Ab Initio molecular dynamics (AIMD) simulations. The findings demonstrate the formation of a motif in which a proton bridges between two Oxygen atoms (belonging to OH groups) at the sugar; The vibrational bands involving the shared proton were strongly shifted to lower frequencies ( by about ~ 500 cm -1 for the symmetric mode, in this case). A similar motif was also observed recently by us in protonated cellobiose, indicating that this might be a common mechanism for interaction of a proton with sugars, perhaps similar to the proton wires observed in proteins. The simulations with protonated sugars also shed light on different mechanisms of interaction of a sugar with a proton, including formation of a carboxonium ion, mutarotation events, ring puckering and in the disaccharide cellobiose, the breaking of the glycosidic bond (in both forms of cis and trans). One final highlight to note in this summary, is the finding that in the monosaccharide beta-D-Galactose a hydroxyl ion abstracts a proton (forming water) in a barrierless process at room temperature, but the water remains bound to the sugar backbone, though it migrates around it; actual degradation occurs at ~500 K when water leaves the sugar. However, the study also shows that the water abstraction reaction can be reversed in the presence of 2 additional water molecules complexed with the sugar.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Detecting and Testing for Structural Error in Computer Models with Application to the Community Atmospheric Model

Bayesian calibration/inversion methods for uncertain input parameters to simulation codes have generated an increasing amount of interest in the climate community in the recent years. This approach uses multiple disparate observational datasets related to the output of the simulator of interest to yield a probabilistic constraint on the uncertain inputs (i.e. probability distribution). A major challenge in applying Bayesian calibration methods to simulation codes is related to the possible presence of a large structural error in the output fields of interest; that is, the presence of discrepancy between the observed and the simulated output that is neither explained by the uncertainty in the inputs nor the observations. We present here methods to test for and visualize structural error in an ensemble of input-perturbed simulations. The methods are based on bootstrap (resmpling) test statistics and well-known metrics to quantify the difference between two probability distributions. The proposed methods are demonstrated using selected precipitation output fields of the Community Atmospheric Model (CAM), which are compared to observations provided by the Global Precipitation Climatology Project (GPCP). The methods are found to be useful in identifying regions and seasons with a large structural error. The proposed structural error detection methodology can be used to screen multiple observational datasets of interest for structural error, making it possible to retain only the datasets with a small structural error for use in Bayesian calibration. In addition, information about the amount of structural error can be valuable to model developers.

58 GEOSCIENCES↗

Potential Applications of Quantum Computing at Los Alamos National Laboratory, v0.3.0

Since the scientific revolution in the 16th and 17th centuries, the process of scientific discovery has followed an iterative feedback process of observation, hypothesis development and testing with physical experiments, which is widely referred to as the scientific method. This process remained largely unchanged until the middle of the 20th century, when the emergence of digital computers empowered scientist to build and inspect detailed simulations of physical phenomena. Over the last century, computational tools have transformed modern approaches to scientific discovery by enabling fast and affordable hypothesis testing before physical experiments are conducted, shown in Figure 1-1. Some notable examples include: global climate forecasts to understand how the environment may change over decades [130]; modeling the behavior of plasma to design fusion reactors [59]; and understanding the behavior of molecules in biological processes [161, 223].

36 MATERIALS SCIENCE↗