8,561 research outputs found
Hierarchical Parallelisation of Functional Renormalisation Group Calculations -- hp-fRG
The functional renormalisation group (fRG) has evolved into a versatile tool
in condensed matter theory for studying important aspects of correlated
electron systems. Practical applications of the method often involve a high
numerical effort, motivating the question in how far High Performance Computing
(HPC) can leverage the approach. In this work we report on a multi-level
parallelisation of the underlying computational machinery and show that this
can speed up the code by several orders of magnitude. This in turn can extend
the applicability of the method to otherwise inaccessible cases. We exploit
three levels of parallelisation: Distributed computing by means of Message
Passing (MPI), shared-memory computing using OpenMP, and vectorisation by means
of SIMD units (single-instruction-multiple-data). Results are provided for two
distinct High Performance Computing (HPC) platforms, namely the IBM-based
BlueGene/Q system JUQUEEN and an Intel Sandy-Bridge-based development cluster.
We discuss how certain issues and obstacles were overcome in the course of
adapting the code. Most importantly, we conclude that this vast improvement can
actually be accomplished by introducing only moderate changes to the code, such
that this strategy may serve as a guideline for other researcher to likewise
improve the efficiency of their codes
Modeling Quantum Optical Components, Pulses and Fiber Channels Using OMNeT++
Quantum Key Distribution (QKD) is an innovative technology which exploits the
laws of quantum mechanics to generate and distribute unconditionally secure
cryptographic keys. While QKD offers the promise of unconditionally secure key
distribution, real world systems are built from non-ideal components which
necessitates the need to model and understand the impact these non-idealities
have on system performance and security. OMNeT++ has been used as a basis to
develop a simulation framework to support this endeavor. This framework,
referred to as "qkdX" extends OMNeT++'s module and message abstractions to
efficiently model optical components, optical pulses, operating protocols and
processes. This paper presents the design of this framework including how
OMNeT++'s abstractions have been utilized to model quantum optical components,
optical pulses, fiber and free space channels. Furthermore, from our toolbox of
created components, we present various notional and real QKD systems, which
have been studied and analyzed.Comment: Published in: A. F\"orster, C. Minkenberg, G. R. Herrera, M. Kirsche
(Eds.), Proc. of the 2nd OMNeT++ Community Summit, IBM Research - Zurich,
Switzerland, September 3-4, 201
Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond
In this and a set of companion whitepapers, the USQCD Collaboration lays out
a program of science and computing for lattice gauge theory. These whitepapers
describe how calculation using lattice QCD (and other gauge theories) can aid
the interpretation of ongoing and upcoming experiments in particle and nuclear
physics, as well as inspire new ones.Comment: 44 pages. 1 of USQCD whitepapers
Resource Optimized Quantum Architectures for Surface Code Implementations of Magic-State Distillation
Quantum computers capable of solving classically intractable problems are
under construction, and intermediate-scale devices are approaching completion.
Current efforts to design large-scale devices require allocating immense
resources to error correction, with the majority dedicated to the production of
high-fidelity ancillary states known as magic-states. Leading techniques focus
on dedicating a large, contiguous region of the processor as a single
"magic-state distillation factory" responsible for meeting the magic-state
demands of applications. In this work we design and analyze a set of optimized
factory architectural layouts that divide a single factory into spatially
distributed factories located throughout the processor. We find that
distributed factory architectures minimize the space-time volume overhead
imposed by distillation. Additionally, we find that the number of distributed
components in each optimal configuration is sensitive to application
characteristics and underlying physical device error rates. More specifically,
we find that the rate at which T-gates are demanded by an application has a
significant impact on the optimal distillation architecture. We develop an
optimization procedure that discovers the optimal number of factory
distillation rounds and number of output magic states per factory, as well as
an overall system architecture that interacts with the factories. This yields
between a 10x and 20x resource reduction compared to commonly accepted single
factory designs. Performance is analyzed across representative application
classes such as quantum simulation and quantum chemistry.Comment: 16 pages, 14 figure
QMCPACK: Advances in the development, efficiency, and application of auxiliary field and real-space variational and diffusion Quantum Monte Carlo
We review recent advances in the capabilities of the open source ab initio
Quantum Monte Carlo (QMC) package QMCPACK and the workflow tool Nexus used for
greater efficiency and reproducibility. The auxiliary field QMC (AFQMC)
implementation has been greatly expanded to include k-point symmetries,
tensor-hypercontraction, and accelerated graphical processing unit (GPU)
support. These scaling and memory reductions greatly increase the number of
orbitals that can practically be included in AFQMC calculations, increasing
accuracy. Advances in real space methods include techniques for accurate
computation of band gaps and for systematically improving the nodal surface of
ground state wavefunctions. Results of these calculations can be used to
validate application of more approximate electronic structure methods including
GW and density functional based techniques. To provide an improved foundation
for these calculations we utilize a new set of correlation-consistent effective
core potentials (pseudopotentials) that are more accurate than previous sets;
these can also be applied in quantum-chemical and other many-body applications,
not only QMC. These advances increase the efficiency, accuracy, and range of
properties that can be studied in both molecules and materials with QMC and
QMCPACK
Optimized Surface Code Communication in Superconducting Quantum Computers
Quantum computing (QC) is at the cusp of a revolution. Machines with 100
quantum bits (qubits) are anticipated to be operational by 2020
[googlemachine,gambetta2015building], and several-hundred-qubit machines are
around the corner. Machines of this scale have the capacity to demonstrate
quantum supremacy, the tipping point where QC is faster than the fastest
classical alternative for a particular problem. Because error correction
techniques will be central to QC and will be the most expensive component of
quantum computation, choosing the lowest-overhead error correction scheme is
critical to overall QC success. This paper evaluates two established quantum
error correction codes---planar and double-defect surface codes---using a set
of compilation, scheduling and network simulation tools. In considering
scalable methods for optimizing both codes, we do so in the context of a full
microarchitectural and compiler analysis. Contrary to previous predictions, we
find that the simpler planar codes are sometimes more favorable for
implementation on superconducting quantum computers, especially under
conditions of high communication congestion.Comment: 14 pages, 9 figures, The 50th Annual IEEE/ACM International Symposium
on Microarchitectur
Analyzing Learned Molecular Representations for Property Prediction
Advancements in neural machinery have led to a wide range of algorithmic
solutions for molecular property prediction. Two classes of models in
particular have yielded promising results: neural networks applied to computed
molecular fingerprints or expert-crafted descriptors, and graph convolutional
neural networks that construct a learned molecular representation by operating
on the graph structure of the molecule. However, recent literature has yet to
clearly determine which of these two methods is superior when generalizing to
new chemical space. Furthermore, prior research has rarely examined these new
models in industry research settings in comparison to existing employed models.
In this paper, we benchmark models extensively on 19 public and 16 proprietary
industrial datasets spanning a wide variety of chemical endpoints. In addition,
we introduce a graph convolutional model that consistently matches or
outperforms models using fixed molecular descriptors as well as previous graph
neural architectures on both public and proprietary datasets. Our empirical
findings indicate that while approaches based on these representations have yet
to reach the level of experimental reproducibility, our proposed model
nevertheless offers significant improvements over models currently used in
industrial workflows
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