79,179 research outputs found
Parallel Quantum Rapidly-Exploring Random Trees
In this paper, we present the Parallel Quantum Rapidly-Exploring Random Tree
(Pq-RRT) algorithm, a parallel version of the Quantum Rapidly-Exploring Random
Trees (q-RRT) algorithm. Parallel Quantum RRT is a parallel quantum algorithm
formulation of a sampling-based motion planner that uses Quantum Amplitude
Amplification to search databases of reachable states for addition to a tree.
In this work we investigate how parallel quantum devices can more efficiently
search a database, as the quantum measurement process involves the collapse of
the superposition to a base state, erasing probability information and
therefore the ability to efficiently find multiple solutions. Pq-RRT uses a
manager/parallel-quantum-workers formulation, inspired by traditional parallel
motion planning, to perform simultaneous quantum searches of a feasible state
database. We present results regarding likelihoods of multiple parallel units
finding any and all solutions contained with a shared database, with and
without reachability errors, allowing efficiency predictions to be made. We
offer simulations in dense obstacle environments showing efficiency,
density/heatmap, and speed comparisons for Pq-RRT against q-RRT, classical RRT,
and classical parallel RRT. We then present Quantum Database Annealing, a
database construction strategy for Pq-RRT and q-RRT that uses a temperature
construct to define database creation over time for balancing exploration and
exploitation.Comment: 14 pages, 15 figure
Adjust genetic algorithm parameter by fuzzy system
Genetic algorithm is one of the random searches algorithm. Genetic algorithm is a method that uses genetic evolution as a model of problem solving. Genetic algorithm for selecting the best population, but the choices are not as heuristic information to be used in specific issues. In order to obtain optimal solutions and efficient use of fuzzy systems with heuristic rules that we would aim to increase the efficiency of parallel genetic algorithms using fuzzy logic immigration, which in fact do this by optimizing the parameters compared with the use of fuzzy system is done
Seedless clustering in all-sky searches for gravitational-wave transients
The problem of searching for unmodeled gravitational-wave bursts can be
thought of as a pattern recognition problem: how to find statistically
significant clusters in spectrograms of strain power when the precise signal
morphology is unknown. In a previous publication, we showed how "seedless
clustering" can be used to dramatically improve the sensitivity of searches for
long-lived gravitational-wave transients. In order to manage the computational
costs, this initial analysis focused on externally triggered searches where the
source location and emission time are both known to some degree of precision.
In this paper, we show how the principle of seedless clustering can be extended
to facilitate computationally-feasible, all-sky searches where the direction
and emission time of the source are entirely unknown. We further demonstrate
that it is possible to achieve a considerable reduction in computation time by
using graphical processor units (GPUs), thereby facilitating more sensitive
searches.Comment: 9 pages, 2 figure
Efficient transfer entropy analysis of non-stationary neural time series
Information theory allows us to investigate information processing in neural
systems in terms of information transfer, storage and modification. Especially
the measure of information transfer, transfer entropy, has seen a dramatic
surge of interest in neuroscience. Estimating transfer entropy from two
processes requires the observation of multiple realizations of these processes
to estimate associated probability density functions. To obtain these
observations, available estimators assume stationarity of processes to allow
pooling of observations over time. This assumption however, is a major obstacle
to the application of these estimators in neuroscience as observed processes
are often non-stationary. As a solution, Gomez-Herrero and colleagues
theoretically showed that the stationarity assumption may be avoided by
estimating transfer entropy from an ensemble of realizations. Such an ensemble
is often readily available in neuroscience experiments in the form of
experimental trials. Thus, in this work we combine the ensemble method with a
recently proposed transfer entropy estimator to make transfer entropy
estimation applicable to non-stationary time series. We present an efficient
implementation of the approach that deals with the increased computational
demand of the ensemble method's practical application. In particular, we use a
massively parallel implementation for a graphics processing unit to handle the
computationally most heavy aspects of the ensemble method. We test the
performance and robustness of our implementation on data from simulated
stochastic processes and demonstrate the method's applicability to
magnetoencephalographic data. While we mainly evaluate the proposed method for
neuroscientific data, we expect it to be applicable in a variety of fields that
are concerned with the analysis of information transfer in complex biological,
social, and artificial systems.Comment: 27 pages, 7 figures, submitted to PLOS ON
An Overview of Approaches to Modernize Quantum Annealing Using Local Searches
I describe how real quantum annealers may be used to perform local (in state
space) searches around specified states, rather than the global searches
traditionally implemented in the quantum annealing algorithm. The quantum
annealing algorithm is an analogue of simulated annealing, a classical
numerical technique which is now obsolete. Hence, I explore strategies to use
an annealer in a way which takes advantage of modern classical optimization
algorithms, and additionally should be less sensitive to problem
mis-specification then the traditional quantum annealing algorithm.Comment: In Proceedings PC 2016, arXiv:1606.06513. An extended version of this
contribution will appear on arXiv soon which will describe more detailed
algorithms, comment more on robustness to problem mis-specification, comment
on thermal sampling applications, and discuss applications on real device
Proteins Wriggle
We propose an algorithmic strategy for improving the efficiency of Monte
Carlo searches for the low-energy states of proteins. Our strategy is motivated
by a model of how proteins alter their shapes. In our model when proteins fold
under physiological conditions, their backbone dihedral angles change
synchronously in groups of four or more so as to avoid steric clashes and
respect the kinematic conservation laws. They wriggle; they do not thrash. We
describe a simple algorithm that can be used to incorporate wriggling in Monte
Carlo simulations of protein folding. We have tested this wriggling algorithm
against a code in which the dihedral angles are varied independently
(thrashing). Our standard of success is the average root-mean-square distance
(rmsd) between the alpha-carbons of the folding protein and those of its native
structure. After 100,000 Monte Carlo sweeps, the relative decrease in the mean
rmsd, as one switches from thrashing to wriggling, rises from 11% for the
protein 3LZM with 164 amino acids (aa) to 40% for the protein 1A1S with 313 aa
and 47% for the protein 16PK with 415 aa. These results suggest that wriggling
is useful and that its utility increases with the size of the protein. One may
implement wriggling on a parallel computer or a computer farm.Comment: 12 pages, 2 figures, JHEP late
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