703,741 research outputs found
Discord and quantum computational resources
Discordant states appear in a large number of quantum phenomena and seem to
be a good indicator of divergence from classicality. While there is evidence
that they are essential for a quantum algorithm to have an advantage over a
classical one, their precise role is unclear. We examine the role of discord in
quantum algorithms using the paradigmatic framework of `restricted distributed
quantum gates' and show that manipulating discordant states using local
operations has an associated cost in terms of entanglement and communication
resources. Changing discord reduces the total correlations and reversible
operations on discordant states usually require non-local resources. Discord
alone is, however, not enough to determine the need for entanglement. A more
general type of similar quantities, which we call K-discord, is introduced as a
further constraint on the kinds of operations that can be performed without
entanglement resources.Comment: Closer to published versio
Trading classical and quantum computational resources
We propose examples of a hybrid quantum-classical simulation where a
classical computer assisted by a small quantum processor can efficiently
simulate a larger quantum system. First we consider sparse quantum circuits
such that each qubit participates in O(1) two-qubit gates. It is shown that any
sparse circuit on n+k qubits can be simulated by sparse circuits on n qubits
and a classical processing that takes time . Secondly, we
study Pauli-based computation (PBC) where allowed operations are
non-destructive eigenvalue measurements of n-qubit Pauli operators. The
computation begins by initializing each qubit in the so-called magic state.
This model is known to be equivalent to the universal quantum computer. We show
that any PBC on n+k qubits can be simulated by PBCs on n qubits and a classical
processing that takes time . Finally, we propose a purely
classical algorithm that can simulate a PBC on n qubits in a time where . This improves upon the brute-force simulation
method which takes time . Our algorithm exploits the fact that
n-fold tensor products of magic states admit a low-rank decomposition into
n-qubit stabilizer states.Comment: 14 pages, 4 figure
Computational aeroelasticity challenges and resources
In the past decade, there has been much activity in the development of computational methods for the analysis of unsteady transonic aerodynamics about airfoils and wings. Significant features are illustrated which must be addressed in the treatment of computational transonic unsteady aerodynamics. The flow regimes for an aircraft on a plot of lift coefficient vs. Mach number are indicated. The sequence of events occurring in air combat maneuvers are illustrated. And further features of transonic flutter are illustrated. Also illustrated are several types of aeroelastic response which were encountered and which offer challenges for computational methods. The four cases illustrate problem areas encountered near the boundaries of aircraft envelopes, as operating condition change from high speed, low angle conditions to lower speed, higher angle conditions
Parallel memetic algorithms for independent job scheduling in computational grids
In this chapter we present parallel implementations of Memetic Algorithms (MAs) for the problem of scheduling independent jobs in computational grids. The problem of scheduling in computational grids is known for its high demanding computational time. In this work we exploit the intrinsic parallel nature of MAs as well as the fact that computational grids offer large amount of resources, a part of which could be used to compute the efficient allocation of jobs to grid resources.
The parallel models exploited in this work for MAs include both fine-grained and coarse-grained parallelization and their hybridization. The resulting schedulers have been tested through different grid scenarios generated by a grid simulator to match different possible configurations of computational grids in terms of size (number of jobs and resources) and computational characteristics of resources. All in all, the result of this work showed that Parallel MAs are very good alternatives in order to match different performance requirement on fast scheduling of jobs to grid resources.Peer ReviewedPostprint (author's final draft
Distributed Feature Extraction Using Cloud Computing Resources
The need to expand the computational resources in a massive surveillance network is clear but traditional means of purchasing new equipment for short-term tasks every year is wasteful. In this work I will provide evidence in support of utilizing a cloud computing infrastructure to perform computationally intensive feature extraction tasks on data streams. Efficient off-loading of computational tasks to cloud resources will require a minimization of the time needed to expand the cloud resources, an efficient model of communication and a study of the interplay between the in-network computational resources and remote resources in the cloud. This report provides strong evidence that the use of cloud computing resources in a near real-time distributed sensor network surveillance system, ASAP, is feasible. A face detection web service operating on an Amazon EC2 instance is shown to provide processing of 10-15 frames per second.Umakishore Ramachandran - Faculty Mentor ; Rajnish Kumar - Committee Member/Second Reade
Complexity-Aware Scheduling for an LDPC Encoded C-RAN Uplink
Centralized Radio Access Network (C-RAN) is a new paradigm for wireless
networks that centralizes the signal processing in a computing cloud, allowing
commodity computational resources to be pooled. While C-RAN improves
utilization and efficiency, the computational load occasionally exceeds the
available resources, creating a computational outage. This paper provides a
mathematical characterization of the computational outage probability for
low-density parity check (LDPC) codes, a common class of error-correcting
codes. For tractability, a binary erasures channel is assumed. Using the
concept of density evolution, the computational demand is determined for a
given ensemble of codes as a function of the erasure probability. The analysis
reveals a trade-off: aggressively signaling at a high rate stresses the
computing pool, while conservatively backing-off the rate can avoid
computational outages. Motivated by this trade-off, an effective
computationally aware scheduling algorithm is developed that balances demands
for high throughput and low outage rates.Comment: Conference on Information Sciences and Systems (CISS) 2017, to appea
Computation in Classical Mechanics
There is a growing consensus that physics majors need to learn computational
skills, but many departments are still devoid of computation in their physics
curriculum. Some departments may lack the resources or commitment to create a
dedicated course or program in computational physics. One way around this
difficulty is to include computation in a standard upper-level physics course.
An intermediate classical mechanics course is particularly well suited for
including computation. We discuss the ways we have used computation in our
classical mechanics courses, focusing on how computational work can improve
students' understanding of physics as well as their computational skills. We
present examples of computational problems that serve these two purposes. In
addition, we provide information about resources for instructors who would like
to include computation in their courses.Comment: 6 pages, 3 figures, submitted to American Journal of Physic
Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees
Algorithms typically come with tunable parameters that have a considerable
impact on the computational resources they consume. Too often, practitioners
must hand-tune the parameters, a tedious and error-prone task. A recent line of
research provides algorithms that return nearly-optimal parameters from within
a finite set. These algorithms can be used when the parameter space is infinite
by providing as input a random sample of parameters. This data-independent
discretization, however, might miss pockets of nearly-optimal parameters: prior
research has presented scenarios where the only viable parameters lie within an
arbitrarily small region. We provide an algorithm that learns a finite set of
promising parameters from within an infinite set. Our algorithm can help
compile a configuration portfolio, or it can be used to select the input to a
configuration algorithm for finite parameter spaces. Our approach applies to
any configuration problem that satisfies a simple yet ubiquitous structure: the
algorithm's performance is a piecewise constant function of its parameters.
Prior research has exhibited this structure in domains from integer programming
to clustering
Real-time co-ordinated resource management in a computational enviroment
Design co-ordination is an emerging engineering design management philosophy with its emphasis on timeliness and appropriateness. Furthermore, a key element of design coordination has been identified as resource management, the aim of which is to facilitate the optimised use of resources throughout a dynamic and changeable process. An approach to operational design co-ordination has been developed, which incorporates the appropriate techniques to ensure that the aim of co-ordinated resource management can be fulfilled. This approach has been realised within an agent-based software system, called the Design Coordination System (DCS), such that a computational design analysis can be managed in a coherent and co-ordinated manner. The DCS is applied to a computational analysis for turbine blade design provided by industry. The application of the DCS involves resources, i.e. workstations within a computer network, being utilised to perform the computational analysis involving the use of a suite of software tools to calculate stress and vibration characteristics of turbine blades. Furthermore, the application of the system shows that the utilisation of resources can be optimised throughout the computational design analysis despite the variable nature of the computer network
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