183,903 research outputs found
Quantum picturalism for topological cluster-state computing
Topological quantum computing is a way of allowing precise quantum
computations to run on noisy and imperfect hardware. One implementation uses
surface codes created by forming defects in a highly-entangled cluster state.
Such a method of computing is a leading candidate for large-scale quantum
computing. However, there has been a lack of sufficiently powerful high-level
languages to describe computing in this form without resorting to single-qubit
operations, which quickly become prohibitively complex as the system size
increases. In this paper we apply the category-theoretic work of Abramsky and
Coecke to the topological cluster-state model of quantum computing to give a
high-level graphical language that enables direct translation between quantum
processes and physical patterns of measurement in a computer - a "compiler
language". We give the equivalence between the graphical and topological
information flows, and show the applicable rewrite algebra for this computing
model. We show that this gives us a native graphical language for the design
and analysis of topological quantum algorithms, and finish by discussing the
possibilities for automating this process on a large scale.Comment: 18 pages, 21 figures. Published in New J. Phys. special issue on
topological quantum computin
Investigation of cluster and cluster queuing system
Cluster became main platform as parallel and distributed computing structure
for high performance computing. Following the development of high
performance computer architecture more and more different branches of natural
science benefit fromhuge and efficient computational power. For instance
bio-informatics, climate science, computational physics, computational chemistry,
marine science, etc. Efficient and reliable computing powermay not only
expending demand of existing high performance computing users but also attracting
more and more different users. Efficiency and performance are main
factors on high performance computing. Most of the high performance computer
exists as computer cluster. Computer clustering is the popular and main
stream of high-performance computing. Discover the efficiency of high performance
computing or cluster is very interesting and never enough as it is
really depending on different users. Monitoring and tuning high performance
or cluster facilities are always necessary. This project focuses on high performance
computer monitoring. Comparing queuing status and work load on
different computing nodes on the cluster. As the power consumption is main
issue nowadays, our project will also try to estimate power consumption on
these special sites and also try to support our way of doing estimation.Master i nettverks- og systemadministrasjo
Cluster state preparation using gates operating at arbitrary success probabilities
Several physical architectures allow for measurement-based quantum computing
using sequential preparation of cluster states by means of probabilistic
quantum gates. In such an approach, the order in which partial resources are
combined to form the final cluster state turns out to be crucially important.
We determine the influence of this classical decision process on the expected
size of the final cluster. Extending earlier work, we consider different
quantum gates operating at various probabilites of success. For finite
resources, we employ a computer algebra system to obtain the provably optimal
classical control strategy and derive symbolic results for the expected final
size of the cluster. We identify two regimes: When the success probability of
the elementary gates is high, the influence of the classical control strategy
is found to be negligible. In that case, other figures of merit become more
relevant. In contrast, for small probabilities of success, the choice of an
appropriate strategy is crucial.Comment: 7 pages, 9 figures, contribution to special issue of New J. Phys. on
"Measurement-Based Quantum Information Processing". Replaced with published
versio
Privacy-Preserving and Outsourced Multi-User k-Means Clustering
Many techniques for privacy-preserving data mining (PPDM) have been
investigated over the past decade. Often, the entities involved in the data
mining process are end-users or organizations with limited computing and
storage resources. As a result, such entities may want to refrain from
participating in the PPDM process. To overcome this issue and to take many
other benefits of cloud computing, outsourcing PPDM tasks to the cloud
environment has recently gained special attention. We consider the scenario
where n entities outsource their databases (in encrypted format) to the cloud
and ask the cloud to perform the clustering task on their combined data in a
privacy-preserving manner. We term such a process as privacy-preserving and
outsourced distributed clustering (PPODC). In this paper, we propose a novel
and efficient solution to the PPODC problem based on k-means clustering
algorithm. The main novelty of our solution lies in avoiding the secure
division operations required in computing cluster centers altogether through an
efficient transformation technique. Our solution builds the clusters securely
in an iterative fashion and returns the final cluster centers to all entities
when a pre-determined termination condition holds. The proposed solution
protects data confidentiality of all the participating entities under the
standard semi-honest model. To the best of our knowledge, ours is the first
work to discuss and propose a comprehensive solution to the PPODC problem that
incurs negligible cost on the participating entities. We theoretically estimate
both the computation and communication costs of the proposed protocol and also
demonstrate its practical value through experiments on a real dataset.Comment: 16 pages, 2 figures, 5 table
Scheduling Distributed Clusters of Parallel Machines: Primal-Dual and LP-based Approximation Algorithms
The Map-Reduce computing framework rose to prominence with datasets of such size that dozens of machines on a single cluster were needed for individual jobs. As datasets approach the exabyte scale, a single job may need distributed processing not only on multiple machines, but on multiple clusters. We consider a scheduling problem to minimize weighted average completion time of n jobs on m distributed clusters of parallel machines. In keeping with the scale of the problems motivating this work, we assume that (1) each job is divided into m "subjobs" and (2) distinct subjobs of a given job may be processed concurrently.
When each cluster is a single machine, this is the NP-Hard concurrent open shop problem. A clear limitation of such a model is that a serial processing assumption sidesteps the issue of how different tasks of a given subjob might be processed in parallel. Our algorithms explicitly model clusters as pools of resources and effectively overcome this issue.
Under a variety of parameter settings, we develop two constant factor approximation algorithms for this problem. The first algorithm uses an LP relaxation tailored to this problem from prior work. This LP-based algorithm provides strong performance guarantees. Our second algorithm exploits a surprisingly simple mapping to the special case of one machine per cluster. This mapping-based algorithm is combinatorial and extremely fast. These are the first constant factor approximations for this problem
Realfast: Real-Time, Commensal Fast Transient Surveys with the Very Large Array
Radio interferometers have the ability to precisely localize and better
characterize the properties of sources. This ability is having a powerful
impact on the study of fast radio transients, where a few milliseconds of data
is enough to pinpoint a source at cosmological distances. However, recording
interferometric data at millisecond cadence produces a terabyte-per-hour data
stream that strains networks, computing systems, and archives. This challenge
mirrors that of other domains of science, where the science scope is limited by
the computational architecture as much as the physical processes at play. Here,
we present a solution to this problem in the context of radio transients:
realfast, a commensal, fast transient search system at the Jansky Very Large
Array. Realfast uses a novel architecture to distribute fast-sampled
interferometric data to a 32-node, 64-GPU cluster for real-time imaging and
transient detection. By detecting transients in situ, we can trigger the
recording of data for those rare, brief instants when the event occurs and
reduce the recorded data volume by a factor of 1000. This makes it possible to
commensally search a data stream that would otherwise be impossible to record.
This system will search for millisecond transients in more than 1000 hours of
data per year, potentially localizing several Fast Radio Bursts, pulsars, and
other sources of impulsive radio emission. We describe the science scope for
realfast, the system design, expected outcomes, and ways real-time analysis can
help in other fields of astrophysics.Comment: Accepted to ApJS Special Issue on Data; 11 pages, 4 figure
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