Search results for "Mathematica"

showing 10 items of 7971 documents

Exact quantum algorithms have advantage for almost all Boolean functions

2014

It has been proved that almost all $n$-bit Boolean functions have exact classical query complexity $n$. However, the situation seemed to be very different when we deal with exact quantum query complexity. In this paper, we prove that almost all $n$-bit Boolean functions can be computed by an exact quantum algorithm with less than $n$ queries. More exactly, we prove that ${AND}_n$ is the only $n$-bit Boolean function, up to isomorphism, that requires $n$ queries.

FOS: Computer and information sciencesNuclear and High Energy Physics81P68 03D15Parity functionBoolean circuitGeneral Physics and AstronomyFOS: Physical sciencesBoolean algebras canonically definedComputational Complexity (cs.CC)Theoretical Computer ScienceCombinatoricsBoolean expressionBoolean functionMathematical PhysicsComputer Science::DatabasesMathematicsDiscrete mathematicsSymmetric Boolean functionQuantum PhysicsProduct termComputer Science::Information RetrievalStatistical and Nonlinear PhysicsComputer Science - Computational ComplexityComputational Theory and MathematicsMaximum satisfiability problemQuantum Physics (quant-ph)
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Quantum lower bound for inverting a permutation with advice

2014

Given a random permutation $f: [N] \to [N]$ as a black box and $y \in [N]$, we want to output $x = f^{-1}(y)$. Supplementary to our input, we are given classical advice in the form of a pre-computed data structure; this advice can depend on the permutation but \emph{not} on the input $y$. Classically, there is a data structure of size $\tilde{O}(S)$ and an algorithm that with the help of the data structure, given $f(x)$, can invert $f$ in time $\tilde{O}(T)$, for every choice of parameters $S$, $T$, such that $S\cdot T \ge N$. We prove a quantum lower bound of $T^2\cdot S \ge \tilde{\Omega}(\epsilon N)$ for quantum algorithms that invert a random permutation $f$ on an $\epsilon$ fraction of…

FOS: Computer and information sciencesNuclear and High Energy PhysicsComputer Science - Cryptography and SecurityGeneral Physics and AstronomyFOS: Physical sciencesOne-way functionComputational Complexity (cs.CC)Upper and lower boundsTheoretical Computer ScienceCyclic permutationCombinatoricsPermutationMathematical PhysicsMathematicsDiscrete mathematicsQuantum PhysicsBit-reversal permutationStatistical and Nonlinear PhysicsRandom permutationComputer Science - Computational ComplexityComputational Theory and MathematicsQuantum algorithmQuantum Physics (quant-ph)Advice (complexity)Cryptography and Security (cs.CR)MathematicsofComputing_DISCRETEMATHEMATICS
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An Empirical Study of the Relation Between Community Structure and Transitivity

2012

One of the most prominent properties in real-world networks is the presence of a community structure, i.e. dense and loosely interconnected groups of nodes called communities. In an attempt to better understand this concept, we study the relationship between the strength of the community structure and the network transitivity (or clustering coefficient). Although intuitively appealing, this analysis was not performed before. We adopt an approach based on random models to empirically study how one property varies depending on the other. It turns out the transitivity increases with the community structure strength, and is also affected by the distribution of the community sizes. Furthermore, …

FOS: Computer and information sciencesPhysics - Physics and SocietyProperty (philosophy)FOS: Physical sciencesPhysics and Society (physics.soc-ph)[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]01 natural sciencesComplex NetworksClustering010305 fluids & plasmasEmpirical research0103 physical sciences010306 general physicstransitivityCommunity StructureClustering coefficientMathematicsSocial and Information Networks (cs.SI)Transitive relationCommunity structure[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Computer Science - Social and Information NetworksComplex networkDegree distributionZero (linguistics)Mathematical economics
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Structural bias in population-based algorithms

2014

Abstract Challenging optimisation problems are abundant in all areas of science and industry. Since the 1950s, scientists have responded to this by developing ever-diversifying families of ‘black box’ optimisation algorithms. The latter are designed to be able to address any optimisation problem, requiring only that the quality of any candidate solution can be calculated via a ‘fitness function’ specific to the problem. For such algorithms to be successful, at least three properties are required: (i) an effective informed sampling strategy, that guides the generation of new candidates on the basis of the fitnesses and locations of previously visited candidates; (ii) mechanisms to ensure eff…

FOS: Computer and information sciencesQA75Mathematical optimizationInformation Systems and ManagementPopulation-based algorithmsFitness landscapemedia_common.quotation_subjectPopulationStructural biasEvolutionary computationPopulation-based algorithmEvolutionary computationTheoretical Computer ScienceArtificial IntelligenceBlack boxEconometricsQuality (business)OptimisationAlgorithmic designNeural and Evolutionary Computing (cs.NE)educationMathematicsmedia_commonta113education.field_of_studyFitness functionPopulation sizeComputer Science - Neural and Evolutionary ComputingComputer Science ApplicationsControl and Systems EngineeringAlgorithmSoftwarePopulation variance
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Mahonian STAT on words

2016

In 2000, Babson and Steingrimsson introduced the notion of what is now known as a permutation vincular pattern, and based on it they re-defined known Mahonian statistics and introduced new ones, proving or conjecturing their Mahonity. These conjectures were proved by Foata and Zeilberger in 2001, and by Foata and Randrianarivony in 2006.In 2010, Burstein refined some of these results by giving a bijection between permutations with a fixed value for the major index and those with the same value for STAT , where STAT is one of the statistics defined and proved to be Mahonian in the 2000 Babson and Steingrimsson's paper. Several other statistics are preserved as well by Burstein's bijection.At…

FOS: Computer and information sciencesQA75[ INFO ] Computer Science [cs]Discrete Mathematics (cs.DM)Major index0102 computer and information sciencesMathematical Analysis01 natural sciencesWords and PermutationsCombinatorial problemsEquidistributionTheoretical Computer ScienceCombinatoricssymbols.namesakePermutationBijectionsFOS: MathematicsMathematics - CombinatoricsMathematical proofs[INFO]Computer Science [cs]0101 mathematicsStatisticMathematicsStatisticZ665Algebraic combinatoricsMathematics::CombinatoricsFormal power seriesPatternPermutationsEulerian path16. Peace & justiceComputer Science Applications010101 applied mathematics010201 computation theory & mathematicsCombinatoricsSignal ProcessingsymbolsBijectionCombinatorics (math.CO)Information SystemsComputer Science - Discrete Mathematics
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Quantum finite multitape automata

1999

Quantum finite automata were introduced by C.Moore, J.P. Crutchfield, and by A.Kondacs and J.Watrous. This notion is not a generalization of the deterministic finite automata. Moreover, it was proved that not all regular languages can be recognized by quantum finite automata. A.Ambainis and R.Freivalds proved that for some languages quantum finite automata may be exponentially more concise rather than both deterministic and probabilistic finite automata. In this paper we introduce the notion of quantum finite multitape automata and prove that there is a language recognized by a quantum finite automaton but not by a deterministic or probabilistic finite automata. This is the first result on …

FOS: Computer and information sciencesQuantum PhysicsComputer Science - Computational ComplexityTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGESTheoryofComputation_COMPUTATIONBYABSTRACTDEVICESFormal Languages and Automata Theory (cs.FL)FOS: Physical sciencesComputer Science - Formal Languages and Automata TheoryComputational Complexity (cs.CC)Quantum Physics (quant-ph)Nonlinear Sciences::Cellular Automata and Lattice GasesComputer Science::Formal Languages and Automata Theory
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The minimal probabilistic and quantum finite automata recognizing uncountably many languages with fixed cutpoints

2019

Discrete Mathematics & Theoretical Computer Science ; vol. 22 no. 1 ; Automata, Logic and Semantics ; 1365-8050

FOS: Computer and information sciencesQuantum PhysicsFormal Languages and Automata Theory (cs.FL)FOS: Physical sciencesComputer Science - Formal Languages and Automata TheoryComputational Complexity (cs.CC)Nonlinear Sciences::Cellular Automata and Lattice GasesComputer Science - Computational ComplexityMathematics::LogicTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGESComputer Science::Discrete MathematicsComputer Science::Logic in Computer ScienceComputingMilieux_COMPUTERSANDSOCIETYMathematics::Metric GeometryQuantum Physics (quant-ph)Computer Science::Formal Languages and Automata Theory
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Automata and Quantum Computing

2015

Quantum computing is a new model of computation, based on quantum physics. Quantum computers can be exponentially faster than conventional computers for problems such as factoring. Besides full-scale quantum computers, more restricted models such as quantum versions of finite automata have been studied. In this paper, we survey various models of quantum finite automata and their properties. We also provide some open questions and new directions for researchers. Keywords: quantum finite automata, probabilistic finite automata, nondeterminism, bounded error, unbounded error, state complexity, decidability and undecidability, computational complexity

FOS: Computer and information sciencesQuantum PhysicsTheoryofComputation_COMPUTATIONBYABSTRACTDEVICESFormal Languages and Automata Theory (cs.FL)FOS: Physical sciencesTheoryofComputation_GENERALComputer Science - Formal Languages and Automata TheoryComputational Complexity (cs.CC)68Q10 68Q12 68Q15 68Q19 68Q45Computer Science - Computational ComplexityTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGESComputerSystemsOrganization_MISCELLANEOUSQuantum Physics (quant-ph)Computer Science::Formal Languages and Automata Theory
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Random Interruptions in Cooperation for Spectrum Sensing in Cognitive Radio Networks

2015

In this paper, a new cooperation structure for spectrum sensing in cognitive radio networks is proposed which outperforms the existing commonly-used ones in terms of energy efficiency. The efficiency is achieved in the proposed design by introducing random interruptions in the cooperation process between the sensing nodes and the fusion center, along with a compensation process at the fusion center. Regarding the hypothesis testing problem concerned, first, the proposed system behavior is thoroughly analyzed and its associated likelihood-ratio test (LRT) is provided. Next, based on a general linear fusion rule, statistics of the global test summary are derived and the sensing quality is cha…

FOS: Computer and information sciencesSemidefinite programmingMathematical optimizationta213Computer scienceInformation Theory (cs.IT)Computer Science - Information Theory010401 analytical chemistrydecision/data fusion020206 networking & telecommunications02 engineering and technology01 natural sciencesStatistical power0104 chemical sciencescooperative spectrum sensingCognitive radionon-ideal reporting channelsefficiency0202 electrical engineering electronic engineering information engineeringcognitive radio (CR)False alarmElectrical and Electronic EngineeringStatistical hypothesis testingEfficient energy use
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A novel exact representation of stationary colored Gaussian processes (fractional differential approach)

2010

A novel representation of functions, called generalized Taylor form, is applied to the filtering of white noise processes. It is shown that every Gaussian colored noise can be expressed as the output of a set of linear fractional stochastic differential equations whose solution is a weighted sum of fractional Brownian motions. The exact form of the weighting coefficients is given and it is shown that it is related to the fractional moments of the target spectral density of the colored noise.

FOS: Computer and information sciencesStatistics and ProbabilityDifferential equationFOS: Physical sciencesGeneral Physics and AstronomyStatistics - ComputationStochastic differential equationsymbols.namesakeSpectral MomentsApplied mathematicsStationary processeGaussian processCondensed Matter - Statistical MechanicsComputation (stat.CO)Mathematical PhysicsMathematicsGeneralized functionStatistical Mechanics (cond-mat.stat-mech)Statistical and Nonlinear PhysicsMathematical Physics (math-ph)White noiseClosed and exact differential formsColors of noiseGaussian noiseFractional CalculuModeling and SimulationsymbolsSettore ICAR/08 - Scienza Delle Costruzioni
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