Search results for "Complex."

showing 10 items of 5824 documents

Quantum algorithm for tree size estimation, with applications to backtracking and 2-player games

2017

We study quantum algorithms on search trees of unknown structure, in a model where the tree can be discovered by local exploration. That is, we are given the root of the tree and access to a black box which, given a vertex $v$, outputs the children of $v$. We construct a quantum algorithm which, given such access to a search tree of depth at most $n$, estimates the size of the tree $T$ within a factor of $1\pm \delta$ in $\tilde{O}(\sqrt{nT})$ steps. More generally, the same algorithm can be used to estimate size of directed acyclic graphs (DAGs) in a similar model. We then show two applications of this result: a) We show how to transform a classical backtracking search algorithm which exam…

FOS: Computer and information sciencesQuantum PhysicsSpeedupBacktrackingFOS: Physical sciences0102 computer and information sciences02 engineering and technologyComputational Complexity (cs.CC)Directed acyclic graph01 natural sciencesSearch treeCombinatoricsComputer Science - Computational Complexity010201 computation theory & mathematicsSearch algorithm020204 information systemsComputer Science - Data Structures and AlgorithmsTernary search tree0202 electrical engineering electronic engineering information engineeringData Structures and Algorithms (cs.DS)Quantum algorithmDepth-first searchQuantum Physics (quant-ph)MathematicsProceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing
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Quantum Algorithm for Dynamic Programming Approach for DAGs. Applications for Zhegalkin Polynomial Evaluation and Some Problems on DAGs

2018

In this paper, we present a quantum algorithm for dynamic programming approach for problems on directed acyclic graphs (DAGs). The running time of the algorithm is $O(\sqrt{\hat{n}m}\log \hat{n})$, and the running time of the best known deterministic algorithm is $O(n+m)$, where $n$ is the number of vertices, $\hat{n}$ is the number of vertices with at least one outgoing edge; $m$ is the number of edges. We show that we can solve problems that use OR, AND, NAND, MAX and MIN functions as the main transition steps. The approach is useful for a couple of problems. One of them is computing a Boolean formula that is represented by Zhegalkin polynomial, a Boolean circuit with shared input and non…

FOS: Computer and information sciencesQuantum PhysicsTheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITYComputer Science - Data Structures and AlgorithmsFOS: Physical sciencesData Structures and Algorithms (cs.DS)Quantum Physics (quant-ph)MathematicsofComputing_DISCRETEMATHEMATICS
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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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Superlinear advantage for exact quantum algorithms

2012

A quantum algorithm is exact if, on any input data, it outputs the correct answer with certainty (probability 1). A key question is: how big is the advantage of exact quantum algorithms over their classical counterparts: deterministic algorithms. For total Boolean functions in the query model, the biggest known gap was just a factor of 2: PARITY of N inputs bits requires $N$ queries classically but can be computed with N/2 queries by an exact quantum algorithm. We present the first example of a Boolean function f(x_1, ..., x_N) for which exact quantum algorithms have superlinear advantage over the deterministic algorithms. Any deterministic algorithm that computes our function must use N qu…

FOS: Computer and information sciencesQuantum sortGeneral Computer ScienceDeterministic algorithmGeneral MathematicsFOS: Physical sciences0102 computer and information sciencesQuantum capacityComputational Complexity (cs.CC)01 natural sciences010305 fluids & plasmasCombinatorics0103 physical sciencesQuantum phase estimation algorithmQuantum informationBoolean function010306 general physicsComputer Science::DatabasesQuantum computerMathematicsDiscrete mathematicsQuantum PhysicsFunction (mathematics)Computer Science - Computational Complexity010201 computation theory & mathematicsQuantum Fourier transformNo-teleportation theoremQuantum algorithmQuantum Physics (quant-ph)Proceedings of the forty-fifth annual ACM symposium on Theory of Computing
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Quantum Computation With Devices Whose Contents Are Never Read

2010

In classical computation, a "write-only memory" (WOM) is little more than an oxymoron, and the addition of WOM to a (deterministic or probabilistic) classical computer brings no advantage. We prove that quantum computers that are augmented with WOM can solve problems that neither a classical computer with WOM nor a quantum computer without WOM can solve, when all other resource bounds are equal. We focus on realtime quantum finite automata, and examine the increase in their power effected by the addition of WOMs with different access modes and capacities. Some problems that are unsolvable by two-way probabilistic Turing machines using sublogarithmic amounts of read/write memory are shown to…

FOS: Computer and information sciencesQuantum sortQuantum PhysicsTheoretical computer scienceQuantum Turing machineComputer scienceFormal Languages and Automata Theory (cs.FL)ComputationQuantum simulatorFOS: Physical sciencesComputer Science - Formal Languages and Automata TheoryComputational Complexity (cs.CC)Computer Science - Computational ComplexityQuantum algorithmQuantum informationComputational problemQuantum Physics (quant-ph)Quantum computer
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A Quantum Lovasz Local Lemma

2012

The Lovasz Local Lemma (LLL) is a powerful tool in probability theory to show the existence of combinatorial objects meeting a prescribed collection of "weakly dependent" criteria. We show that the LLL extends to a much more general geometric setting, where events are replaced with subspaces and probability is replaced with relative dimension, which allows to lower bound the dimension of the intersection of vector spaces under certain independence conditions. Our result immediately applies to the k-QSAT problem: For instance we show that any collection of rank 1 projectors with the property that each qubit appears in at most $2^k/(e \cdot k)$ of them, has a joint satisfiable state. We then …

FOS: Computer and information sciencesRank (linear algebra)FOS: Physical sciences0102 computer and information sciencesComputational Complexity (cs.CC)01 natural sciencesUpper and lower boundsCombinatoricsIntersectionProbability theoryArtificial Intelligence0103 physical sciences010306 general physicsLovász local lemmaIndependence (probability theory)Quantum computerMathematicsDiscrete mathematicsQuantum PhysicsComputer Science - Computational ComplexityHardware and ArchitectureControl and Systems Engineering010201 computation theory & mathematicsQubitQuantum Physics (quant-ph)SoftwareInformation SystemsVector space
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Sensitivity versus block sensitivity of Boolean functions

2010

Determining the maximal separation between sensitivity and block sensitivity of Boolean functions is of interest for computational complexity theory. We construct a sequence of Boolean functions with bs(f) = 1/2 s(f)^2 + 1/2 s(f). The best known separation previously was bs(f) = 1/2 s(f)^2 due to Rubinstein. We also report results of computer search for functions with at most 12 variables.

FOS: Computer and information sciencesSequenceComputational complexity theoryBlock (permutation group theory)Computational Complexity (cs.CC)Computer Science ApplicationsTheoretical Computer ScienceCombinatoricsComputer Science - Computational ComplexitySignal ProcessingTheory of computationSensitivity (control systems)Boolean functionAlgorithmComputer searchInformation SystemsMathematics
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Constructing Antidictionaries in Output-Sensitive Space

2021

A word $x$ that is absent from a word $y$ is called minimal if all its proper factors occur in $y$. Given a collection of $k$ words $y_1,y_2,\ldots,y_k$ over an alphabet $\Sigma$, we are asked to compute the set $\mathrm{M}^{\ell}_{y_{1}\#\ldots\#y_{k}}$ of minimal absent words of length at most $\ell$ of word $y=y_1\#y_2\#\ldots\#y_k$, $\#\notin\Sigma$. In data compression, this corresponds to computing the antidictionary of $k$ documents. In bioinformatics, it corresponds to computing words that are absent from a genome of $k$ chromosomes. This computation generally requires $\Omega(n)$ space for $n=|y|$ using any of the plenty available $\mathcal{O}(n)$-time algorithms. This is because a…

FOS: Computer and information sciencesSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniOutput sensitive algorithmsString algorithmsPhysicsAntidictionarieSettore INF/01 - InformaticaOutput sensitive algorithm0102 computer and information sciencesAbsent wordsSpace (mathematics)01 natural sciencesAntidictionariesCombinatorics010201 computation theory & mathematicsTheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITYData compressionComputer Science - Data Structures and AlgorithmsData Structures and Algorithms (cs.DS)Computer Science::Symbolic Computation[INFO]Computer Science [cs]Absent wordAlphabetWord (group theory)2019 Data Compression Conference (DCC)
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Bootstrap validation of links of a minimum spanning tree

2018

We describe two different bootstrap methods applied to the detection of a minimum spanning tree obtained from a set of multivariate variables. We show that two different bootstrap procedures provide partly distinct information that can be highly informative about the investigated complex system. Our case study, based on the investigation of daily returns of a portfolio of stocks traded in the US equity markets, shows the degree of robustness and completeness of the information extracted with popular information filtering methods such as the minimum spanning tree and the planar maximally filtered graph. The first method performs a "row bootstrap" whereas the second method performs a "pair bo…

FOS: Computer and information sciencesStatistics and ProbabilityMultivariate statisticsCorrelation coefficientCovariance matrixReplicaComplex systemMinimum spanning treeCondensed Matter Physics01 natural sciencesSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Minimum spanning tree Bootstrap Planar maximally filtered graph Information filtering Proximity based networks Random matrix theory010305 fluids & plasmasMethodology (stat.ME)0103 physical sciencesStatistics010306 general physicsRandom matrixStatistics - MethodologyMathematics
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Centrality measures for networks with community structure

2016

Understanding the network structure, and finding out the influential nodes is a challenging issue in the large networks. Identifying the most influential nodes in the network can be useful in many applications like immunization of nodes in case of epidemic spreading, during intentional attacks on complex networks. A lot of research is done to devise centrality measures which could efficiently identify the most influential nodes in the network. There are two major approaches to the problem: On one hand, deterministic strategies that exploit knowledge about the overall network topology in order to find the influential nodes, while on the other end, random strategies are completely agnostic ab…

FOS: Computer and information sciencesStatistics and ProbabilityPhysics - Physics and SocietyExploitComplex networksFOS: Physical sciencesNetwork sciencePhysics and Society (physics.soc-ph)Network theoryMachine learningcomputer.software_genreNetwork topologyImmunization strategies01 natural sciences010305 fluids & plasmas0103 physical sciences010306 general physicsMathematicsSocial and Information Networks (cs.SI)Structure (mathematical logic)[PHYS.PHYS]Physics [physics]/Physics [physics]business.industryCommunity structureComputer Science - Social and Information NetworksComplex networkEpidemic dynamicsCondensed Matter Physics[ PHYS.PHYS ] Physics [physics]/Physics [physics]Community structureArtificial intelligenceData miningbusinessCentralitycomputer
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