Mapping renormalized coupled cluster methods to quantum computers through a compact unitary representation of nonunitary operators
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97 MATHEMATICS AND COMPUTING↗
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Analyzing the errors of DFT approximations for compressed water systems
We report an extensive study of the errors of density functional theory (DFT) approximations for compressed water systems. The approximations studied are based on the widely used PBE and BLYP exchange-correlation functionals, and we characterize their errors before and after correction for 1- and 2-body errors, the corrections being performed using the methods of Gaussian approximation potentials. The errors of the uncorrected and corrected approximations are investigated for two related types of water system: first, the compressed liquid at temperature 420 K and density 1.245 g/cm{sup 3} where the experimental pressure is 15 kilobars; second, thermal samples of compressed water clusters from the trimer to the 27-mer. For the liquid, we report four first-principles molecular dynamics simulations, two generated with the uncorrected PBE and BLYP approximations and a further two with their 1- and 2-body corrected counterparts. The errors of the simulations are characterized by comparing with experimental data for the pressure, with neutron-diffraction data for the three radial distribution functions, and with quantum Monte Carlo (QMC) benchmarks for the energies of sets of configurations of the liquid in periodic boundary conditions. The DFT errors of the configuration samples of compressed water clusters are computed using QMC benchmarks. We find that the 2-body and beyond-2-body errors in the liquid are closely related to similar errors exhibited by the clusters. For both the liquid and the clusters, beyond-2-body errors of DFT make a substantial contribution to the overall errors, so that correction for 1- and 2-body errors does not suffice to give a satisfactory description. For BLYP, a recent representation of 3-body energies due to Medders, Babin, and Paesani [J. Chem. Theory Comput. 9, 1103 (2013)] gives a reasonably good way of correcting for beyond-2-body errors, after which the remaining errors are typically 0.5 mE{sub h} ≃ 15 meV/monomer for the liquid and the clusters.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Improving Estimation of the Koopman Operator with Kolmogorov–Smirnov Indicator Functions
It has become common to perform kinetic analysis using approximate Koopman operators that transform high-dimensional timeseries of observables into ranked dynamical modes. The key to the practical success of the approach is the identification of a set of observables that form a good basis on which to expand the slow relaxation modes. Good observables are, however, difficult to identify a priori and suboptimal choices can lead to significant underestimations of characteristic time scales. Leveraging the representation of slow dynamics in terms of Hidden Markov Models (HMM), we propose a simple and computationally efficient clustering procedure to infer surrogate observables that form a good basis for slow modes. Here, we apply the approach to an analytically solvable model system as well as on three protein systems of different complexities. We consistently demonstrate that the inferred indicator functions can significantly improve the estimation of the leading eigenvalues of Koopman operators and correctly identify key states and transition time scales of stochastic systems, even when good observables are not known a priori.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Resource-Efficient Chemistry on Quantum Computers with the Variational Quantum Eigensolver and The Double Unitary Coupled-Cluster approach
Applications of quantum simulation algorithms to obtain electronic energies of molecules on noisy intermediate-scale quantum (NISQ) devices require careful consideration of resources describing the complex electron correlation effects. In modeling second-quantized problems, the biggest challenge confronted is that the number of qubits scales linearly with the size of molecular basis. This poses a significant limitation on the size of the basis sets and the number of correlated electrons included in quantum simulations of chemical processes. To address this issue and to enable more realistic simulations on NISQ computers, we employ the double unitary coupled-cluster (DUCC) method to effectively downfold correlation effects into the reduced-size orbital space, commonly referred to as the active space. Using downfolding techniques, we demonstrate that properly constructed effective Hamiltonians can capture the effect of the whole orbital space in small-size active spaces. Combining the downfolding pre-processing technique with the Variational Quantum Eigensolver, we solve for the ground-state energy of H2 and Li2 in the cc-pVTZ basis using the DUCC-reduced active spaces. We compare these results to full configuration-interaction and high-level coupled-cluster reference calculations.
quantum computing, variational quantum solver, cou↗
Computational study of the adsorption of bimetallic clusters on alumina substrate
We performed computational investigation of the adsorption of bimetallic Pd 3 M 2 (where M changes from Ag, Au, Co, Cu, Mn, Ni, Pt, and Ru) cluster on the hydroxylated alumina surface. Previously, it was shown that small silver cluster can control the rate of discharge at the cathode of lithium-oxygen battery. The gap near the fermi energy was shown to control the oxygen reduction, an important reaction for LiO 2 formation. Controlling the gap would ultimately control the rate of LiO 2 formation. One can vary the size of the cluster to vary the gap, however, this "knob" provides limited variance in the gap. Alloying, in combination with size variation offer a much wider control of the gap, hence the LiO 2 formation. Using Density Functional Theory (DFT), we determined the most stable geometry of the bimetallic clusters Pd 3 M 2 and calculated the binding energies of these clusters on the alumina substrate which ranges from 0.2 eV to 0.25 eV depending upon the composition of the alloy-cluster, its orientation and the adsorption site. We also find that Pd atoms bind strongly with the substrate oxygen atoms with an average short bond-length of about 2.2 angstrom. We explored how the gap at the Fermi level of the system varies as a function of elemental composition and the calculated gap ranges from 0 meV to 90 meV. Charges distribution using Bader analysis was also performed to probe how charges are transferred between the cluster and the substrate. These preliminary results will open the door for more systematic studies of alloy clusters of different size and stoichiometry for Li-O 2 battery cathode design.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
The Atacama Cosmology Telescope: The LABOCA/ACT survey of clusters at all Redshifts
We present a multi-wavelength analysis of 11 Sunyaev–Zel’dovich effect (SZE)-selected galaxy clusters (10 with new data) from the Atacama Cosmology Telescope (ACT) southern survey. We have obtained new imaging from the Large APEX Bolometer Camera (345 GHz; LABOCA) on the Atacama Pathfinder EXperiment (APEX) telescope, the Australia Telescope Compact Array (2.1 GHz; ATCA), and the Spectral and Photometric Imaging Receiver (250, 350, and 500 μm; SPIRE) on the Herschel Space Observatory.{sup 24} Spatially resolved 345 GHz SZE increments with integrated signal-to-noise ratio (S/N)>5 are found in six clusters. We compute 2.1 GHz number counts as a function of cluster-centric radius and find significant enhancements in the counts of bright sources at projected radii θ<θ{sub 2500c}. By extrapolating in frequency, we predict that the combined signals from 2.1 GHz-selected radio sources and 345 GHz-selected submillimeter galaxies (SMGs) contaminate the 148 GHz SZE decrement signal by ∼5% and the 345 GHz SZE increment by ∼18%. After removing radio source and SMG emission from the SZE signals, we use ACT, LABOCA, and (in some cases) new Herschel SPIRE imaging to place constraints on the clusters’ peculiar velocities. The sample’s average peculiar velocity relative to the cosmic microwave background is 〈v{sub p}〉=153±383 km s{sup −1}.
79 ASTRONOMY AND ASTROPHYSICS↗
Effects of cosmic string velocities and the origin of globular clusters
With the hypothesis that cosmic string loops act as seeds for globular clusters in mind, we study the role that velocities of these strings will play in determining the mass distribution of globular clusters. Loops with high enough velocities will not form compact and roughly spherical objects and can hence not be the seeds for globular clusters. We compute the expected number density and mass function of globular clusters as a function of both the string tension and the peak loop velocity, and compare the results with the observational data on the mass distribution of globular clusters in our Milky Way. We determine the critical peak string loop velocity above which the agreement between the string loop model for the origin of globular clusters (neglecting loop velocities) and observational data is lost.
79 ASTRONOMY AND ASTROPHYSICS↗
An Integrated High-performance Computing and Digital Real-time Simulation Testbed to Benchmark Closed-loop Load Shedding Algorithms in Power Systems
An integrated testbed using digital real-time simulator (DRTS) and a high-performance computing (HPC) cluster is presented here to compare speed and performance of computational schemes to mitigate time-critical issues in electric power systems. The first approach in this testbed validation is taken by running a set of closed-loop load shedding algorithms to compare and contrast two paradigms of arresting cascading failure propagation. Two algorithms involve solving DC and AC power flow model-based optimization problems to compute load shedding at different buses, while a model-based stochastic search using parallel computing provides a viable alternative. The algorithms are implemented in the DRTS-HPC testbed for the IEEE 14-bus benchmark transmission system. As a proof of the concept, simulation results are presented for implementation of closed-loop load-shedding algorithms for cascading failures in the DRTS-HPC testbed
24 POWER TRANSMISSION AND DISTRIBUTION↗
PCS: A Productive Computational Science Platform
As modern supercomputers continue to be increasingly heterogeneous with diverse computational accelerators, software becomes a critical aspect necessary to fully exploit these accelerators while not increasing the effort and time required to make variable scientific discovery in the face of complicated programming environments introduced by these accelerators. This paper presents PCS, a productive computational science platform for cluster-scale heterogeneous computing. The PCS platform by design follows a basic principle that, multiple programming models in a unified programming environment, is essential to facilitate large-scale, accelerator-aware, heterogeneous computing for next- generation scientific applications. We propose a new scheme — Edge Neural Network — for managing regularity in large dataflow graphs using higher-order bulk synchronous operators, providing a foundation for formalizing computational models for cluster-wide dataflow programming of heterogeneous clusters.
36 MATERIALS SCIENCE↗
PCS: A Productive Computational Science Platform
As modern supercomputers continue to be increasingly heterogeneous with diverse computational accelerators, software becomes a critical aspect necessary to fully exploit these accelerators while not increasing the effort and time required to make variable scientific discovery in the face of complicated programming environments introduced by these accelerators. This paper presents PCS, a productive computational science platform for cluster-scale heterogeneous computing. The PCS platform by design follows a basic principle that, multiple programming models in a unified programming environment, is essential to facilitate large-scale, accelerator-aware, heterogeneous computing for next-generation scientific applications. We propose a new scheme — Edge Neural Network — for managing regularity in large dataflow graphs using higher-order bulk synchronous operators, providing a foundation for formalizing computational models for cluster-wide dataflow programming of heterogeneous clusters.
Ojika, David↗
PCS: A Productive Computational Science Platform
As modern supercomputers continue to be increasingly heterogeneous with diverse computational accelerators, software becomes a critical aspect necessary to fully exploit these accelerators while not increasing the effort and time required to make variable scientific discovery in the face of complicated programming environments introduced by these accelerators. This paper presents PCS, a productive computational science platform for cluster-scale heterogeneous computing. The PCS platform by design follows a basic principle that, multiple programming models in a unified programming environment, is essential to facilitate large-scale, accelerator-aware, heterogeneous computing for next- generation scientific applications. We propose a new scheme - Edge Neural Network - for managing regularity in large dataflow graphs using higher-order bulk synchronous operators, providing a foundation for formalizing computational models for cluster-wide dataflow programming of heterogeneous clusters.
36 MATERIALS SCIENCE↗
Superposition of Fragment Excitations for Excited States of Large Clusters with Application to Helium Clusters
We develop a local excited-state method, based on the configuration interaction singles (CIS) wave function, for large atomic and molecular clusters. This method exploits the properties of absolutely localized molecular orbitals (ALMOs), which strictly limits the total number of excitations, and results in formal scaling with the third power of the system size for computing the full spectrum of ALMO-CIS excited states. Here, the derivation of the equations and design of the algorithm are discussed in detail, with particular emphasis on the computational scaling. Clusters containing ~500 atoms were used in evaluating the scaling, which agrees with the theoretical predictions, and the accuracy of the method is evaluated with respect to standard CIS. Finally, a pioneering application to the size dependence of the helium cluster spectrum is also presented for clusters of 25-231 atoms, the largest of which results in the computation of 2310 excited states per sampled cluster geometry.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Exploring Parameter Redundancy in the Unitary Coupled-Cluster Ansätze for Hybrid Variational Quantum Computing
One of the commonly used chemical-inspired approaches in variational quantum computing is the unitary coupled-cluster (UCC) ansatze. Despite being a systematic way of approaching the exact limit, the number of parameters in the standard UCC ansatze exhibits unfavorable scaling with respect to the system size, hindering its practical use on near-term quantum devices. Efforts have been taken to propose some variants of UCC ansatze with better scaling. In this paper we explore the parameter redundancy in the preparation of unitary coupled-cluster singles and doubles (UCCSD) ansatze employing spin-adapted formulation, small amplitude filtration, and entropy-based orbital selection approaches. Numerical results of using our approach on some small molecules have exhibited a significant cost reduction in the number of parameters to be optimized and in the time to convergence compared with conventional UCCSD-VQE simulations. Further, we also discuss the potential application of some machine learning techniques in further exploring the parameter redundancy, providing a possible direction for future studies.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Accelerating science: The usage of commercial clouds in ATLAS Distributed Computing
The ATLAS experiment at CERN is one of the largest scientific machines built to date and will have ever growing computing needs as the Large Hadron Collider collects an increasingly larger volume of data over the next 20 years. ATLAS is conducting R&D projects on Amazon Web Services and Google Cloud as complementary resources for distributed computing, focusing on some of the key features of commercial clouds: lightweight operation, elasticity and availability of multiple chip architectures. The proof of concept phases have concluded with the cloud-native, vendoragnostic integration with the experiment’s data and workload management frameworks. Google Cloud has been used to evaluate elastic batch computing, ramping up ephemeral clusters of up to O(100k) cores to process tasks requiring quick turnaround. Amazon Web Services has been exploited for the successful physics validation of the Athena simulation software on ARM processors. We have also set up an interactive facility for physics analysis allowing endusers to spin up private, on-demand clusters for parallel computing with up to 4 000 cores, or run GPU enabled notebooks and jobs for machine learning applications. The success of the proof of concept phases has led to the extension of the Google Cloud project, where ATLAS will study the total cost of ownership of a production cloud site during 15 months with 10k cores on average, fully integrated with distributed grid computing resources and continue the R&D projects.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Deconvolute individual genomes from metagenome sequences through short read clustering
Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by exploiting statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using synthetic and real-world datasets we demonstrated that this method has the potential to cluster almost all of the short reads from genomes with sufficient sequencing coverage. The improved read clustering in turn leads to improved downstream genome assembly quality.
59 BASIC BIOLOGICAL SCIENCES↗
An Integrated Platform for Collaborative Data Analytics
While collaboration among data scientists is a key to organizational productivity, data analysts face significant barriers to achieving this end, including data sharing, accessing and configuring the required computational environment, and a unified method of sharing knowledge. Each of these barriers to collaboration is related to the fundamental question of knowledge management “how can organizations use knowledge more effectively?”. In this paper, we consider the problem of knowledge management in collaborative data analytics and present ShareAL, an integrated knowledge management platform, as a solution to that problem. The ShareAL platform consists of three core components: a full stack web application, a dashboard for analyzing streaming data and a High Performance Computing (HPC) cluster for performing real time analysis. Prior research has not applied knowledge management to collaborative analytics or developed a platform with the same capabilities as ShareAL. ShareAL overcomes the barriers data scientists face to collaboration by providing intuitive sharing of data and analytics via the web application, a shared computing environment via the HPC cluster and knowledge sharing and collaboration via a real time messaging application.
Oesch, T↗
The Installation of Direct Water-Cooling Systems to Reduce Cooling Energy Requirements for High-Performance Computing Centers
A large cluster of High-Performance Computing (HPC) equipment at the Lawrence Livermore National Laboratory in California was retrofitted with an Asetek cooling system. The Asetek system is a hybrid scheme with water-cooled cold plates on high-heat-producing components in the information technology (IT) equipment, and with the remainder of the heat being removed by conventional air-cooling systems. In order to determine energy savings of the Asetek system, data were gathered and analyzed two ways: using top-down statistical models, and bottom-up engineering models. The cluster, “Cabernet”, rejected its heat into a facilities cooling water loop which in turn rejected the heat into the same chilled water system serving the computer-room air handlers (CRAHs) that provided the air-based cooling for the room. Because the “before” and “after” cases both reject their heat into the chilled water system, the only savings is due to reduction in CRAH fan power. The top-down analysis showed a 4% overall energy savings for the data center (power usage effectiveness (PUE) —the ratio of total data center energy to IT energy— dropped from 1.60 to 1.53, lower is better); the bottom-up analysis showed a 3% overall energy savings (PUE from 1.70 to 1.66) and an 11% savings for the Cabernet system by itself (partial PUE of 1.51). Greater savings, on the order of 15-20%, would be possible if the chilled water system was not used for rejecting the heat from the Asetek system. About 37% of the heat from the Cab system was rejected to the cooling water, lower than at other installations.
Earni, Shankar↗
THERMODYNAMICS AND CHARGING OF INTERSTELLAR IRON NANOPARTICLES
Interstellar iron in the form of metallic iron nanoparticles may constitute a component of the interstellar dust. We compute the stability of iron nanoparticles to sublimation in the interstellar radiation field, finding that iron clusters can persist down to a radius of ≃4.5 Å, and perhaps smaller. We employ laboratory data on small iron clusters to compute the photoelectric yields as a function of grain size and the resulting grain charge distribution in various interstellar environments, finding that iron nanoparticles can acquire negative charges, particularly in regions with high gas temperatures and ionization fractions. If ≳10% of the interstellar iron is in the form of ultrasmall iron clusters, the photoelectric heating rate from dust may be increased by up to tens of percent relative to dust models with only carbonaceous and silicate grains.
79 ASTRONOMY AND ASTROPHYSICS↗