8,896 research outputs found
Magic-State Functional Units: Mapping and Scheduling Multi-Level Distillation Circuits for Fault-Tolerant Quantum Architectures
Quantum computers have recently made great strides and are on a long-term
path towards useful fault-tolerant computation. A dominant overhead in
fault-tolerant quantum computation is the production of high-fidelity encoded
qubits, called magic states, which enable reliable error-corrected computation.
We present the first detailed designs of hardware functional units that
implement space-time optimized magic-state factories for surface code
error-corrected machines. Interactions among distant qubits require surface
code braids (physical pathways on chip) which must be routed. Magic-state
factories are circuits comprised of a complex set of braids that is more
difficult to route than quantum circuits considered in previous work [1]. This
paper explores the impact of scheduling techniques, such as gate reordering and
qubit renaming, and we propose two novel mapping techniques: braid repulsion
and dipole moment braid rotation. We combine these techniques with graph
partitioning and community detection algorithms, and further introduce a
stitching algorithm for mapping subgraphs onto a physical machine. Our results
show a factor of 5.64 reduction in space-time volume compared to the best-known
previous designs for magic-state factories.Comment: 13 pages, 10 figure
Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist
Apache Spark is a popular system aimed at the analysis of large data sets,
but recent studies have shown that certain computations---in particular, many
linear algebra computations that are the basis for solving common machine
learning problems---are significantly slower in Spark than when done using
libraries written in a high-performance computing framework such as the
Message-Passing Interface (MPI).
To remedy this, we introduce Alchemist, a system designed to call MPI-based
libraries from Apache Spark. Using Alchemist with Spark helps accelerate linear
algebra, machine learning, and related computations, while still retaining the
benefits of working within the Spark environment. We discuss the motivation
behind the development of Alchemist, and we provide a brief overview of its
design and implementation.
We also compare the performances of pure Spark implementations with those of
Spark implementations that leverage MPI-based codes via Alchemist. To do so, we
use data science case studies: a large-scale application of the conjugate
gradient method to solve very large linear systems arising in a speech
classification problem, where we see an improvement of an order of magnitude;
and the truncated singular value decomposition (SVD) of a 400GB
three-dimensional ocean temperature data set, where we see a speedup of up to
7.9x. We also illustrate that the truncated SVD computation is easily scalable
to terabyte-sized data by applying it to data sets of sizes up to 17.6TB.Comment: Accepted for publication in Proceedings of the 24th ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining, London, UK,
201
Design issues for the Generic Stream Encapsulation (GSE) of IP datagrams over DVB-S2
The DVB-S2 standard has brought an unprecedented degree of novelty and flexibility in the way IP datagrams or other network level packets can be transmitted over DVB satellite links, with the introduction of an IP-friendly link layer - he continuous Generic Streams - and the adaptive combination of advanced error coding, modulation and spectrum management techniques. Recently approved by the DVB, the Generic Stream Encapsulation (GSE) used for carrying IP datagrams over DVBS2 implements solutions stemmed from a design rationale quite different from the one behind IP encapsulation schemes over its predecessor DVB-S. This paper highlights GSE's original design choices under the perspective of DVB-S2's innovative features and possibilities
Accurate ionic forces and geometry optimization in linear-scaling density-functional theory with local orbitals
Linear scaling methods for density-functional theory (DFT) simulations are formulated in terms of localized orbitals in real space, rather than the delocalized eigenstates of conventional approaches. In local-orbital methods, relative to conventional DFT, desirable properties can be lost to some extent, such as the translational invariance of the total energy of a system with respect to small displacements and the smoothness of the potential-energy surface. This has repercussions for calculating accurate ionic forces and geometries. In this work we present results from onetep, our linear scaling method based on localized orbitals in real space. The use of psinc functions for the underlying basis set and on-the-fly optimization of the localized orbitals results in smooth potential-energy surfaces that are consistent with ionic forces calculated using the Hellmann-Feynman theorem. This enables accurate geometry optimization to be performed. Results for surface reconstructions in silicon are presented, along with three example systems demonstrating the performance of a quasi-Newton geometry optimization algorithm: an organic zwitterion, a point defect in an ionic crystal, and a semiconductor nanostructure.<br/
Distributed Space Time Coding for Wireless Two-way Relaying
We consider the wireless two-way relay channel, in which two-way data
transfer takes place between the end nodes with the help of a relay. For the
Denoise-And-Forward (DNF) protocol, it was shown by Koike-Akino et. al. that
adaptively changing the network coding map used at the relay greatly reduces
the impact of Multiple Access interference at the relay. The harmful effect of
the deep channel fade conditions can be effectively mitigated by proper choice
of these network coding maps at the relay. Alternatively, in this paper we
propose a Distributed Space Time Coding (DSTC) scheme, which effectively
removes most of the deep fade channel conditions at the transmitting nodes
itself without any CSIT and without any need to adaptively change the network
coding map used at the relay. It is shown that the deep fades occur when the
channel fade coefficient vector falls in a finite number of vector subspaces of
, which are referred to as the singular fade subspaces. DSTC
design criterion referred to as the \textit{singularity minimization criterion}
under which the number of such vector subspaces are minimized is obtained.
Also, a criterion to maximize the coding gain of the DSTC is obtained. Explicit
low decoding complexity DSTC designs which satisfy the singularity minimization
criterion and maximize the coding gain for QAM and PSK signal sets are
provided. Simulation results show that at high Signal to Noise Ratio, the DSTC
scheme provides large gains when compared to the conventional Exclusive OR
network code and performs slightly better than the adaptive network coding
scheme proposed by Koike-Akino et. al.Comment: 27 pages, 4 figures, A mistake in the proof of Proposition 3 given in
Appendix B correcte
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