Search results for "Computer Science - Data Structures and Algorithms"

showing 10 items of 64 documents

Variable time amplitude amplification and a faster quantum algorithm for solving systems of linear equations

2010

We present two new quantum algorithms. Our first algorithm is a generalization of amplitude amplification to the case when parts of the quantum algorithm that is being amplified stop at different times. Our second algorithm uses the first algorithm to improve the running time of Harrow et al. algorithm for solving systems of linear equations from O(kappa^2 log N) to O(kappa log^3 kappa log N) where \kappa is the condition number of the system of equations.

FOS: Computer and information sciencesMathematics::LogicQuantum PhysicsComputer Science - Computational ComplexityComputer Science - Data Structures and AlgorithmsFOS: Physical sciencesData Structures and Algorithms (cs.DS)Computational Complexity (cs.CC)Quantum Physics (quant-ph)
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The rightmost equal-cost position problem.

2013

LZ77-based compression schemes compress the input text by replacing factors in the text with an encoded reference to a previous occurrence formed by the couple (length, offset). For a given factor, the smallest is the offset, the smallest is the resulting compression ratio. This is optimally achieved by using the rightmost occurrence of a factor in the previous text. Given a cost function, for instance the minimum number of bits used to represent an integer, we define the Rightmost Equal-Cost Position (REP) problem as the problem of finding one of the occurrences of a factor whose cost is equal to the cost of the rightmost one. We present the Multi-Layer Suffix Tree data structure that, for…

FOS: Computer and information sciencesOffset (computer science)Computer scienceSuffix treeComputer Science - Information Theorylaw.inventionCombinatoricslawLog-log plotComputer Science - Data Structures and AlgorithmsCompression schemetext compressiondictionary text compressionData Structures and Algorithms (cs.DS)LZ77 compressiondata compressionLossless compressionfull text indexSuffix Tree Data StructuresSettore INF/01 - InformaticaInformation Theory (cs.IT)Data structurePrefixCompression ratioCompression scheme; Constant time; Suffix Tree Data StructuresAlgorithmData compressionConstant time
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Whom to befriend to influence people

2020

Alice wants to join a new social network, and influence its members to adopt a new product or idea. Each person $v$ in the network has a certain threshold $t(v)$ for {\em activation}, i.e adoption of the product or idea. If $v$ has at least $t(v)$ activated neighbors, then $v$ will also become activated. If Alice wants to activate the entire social network, whom should she befriend? More generally, we study the problem of finding the minimum number of links that a set of external influencers should form to people in the network, in order to activate the entire social network. This {\em Minimum Links} Problem has applications in viral marketing and the study of epidemics. Its solution can be…

FOS: Computer and information sciencesPhysics - Physics and SocietyGeneral Computer ScienceFOS: Physical sciencesPhysics and Society (physics.soc-ph)0102 computer and information sciences02 engineering and technology01 natural sciencesSocial networksGraphTheoretical Computer ScienceCombinatoricsComputer Science - Data Structures and AlgorithmsGreedy algorithmFOS: Mathematics0202 electrical engineering electronic engineering information engineeringMathematics - CombinatoricsData Structures and Algorithms (cs.DS)Greedy algorithmTime complexityNP-completeMathematicsSocial and Information Networks (cs.SI)Social networkDiscrete mathematicsBinary treeDegree (graph theory)Computer Science (all)Order (ring theory)Computer Science - Social and Information NetworksJoin (topology)Influence maximizationGreedy algorithms010201 computation theory & mathematicsGraphs; Greedy algorithms; Influence maximization; NP-complete; Social networksProduct (mathematics)020201 artificial intelligence & image processingCombinatorics (math.CO)Constant (mathematics)GraphsTheoretical Computer Science
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Quadratic speedup for finding marked vertices by quantum walks

2020

A quantum walk algorithm can detect the presence of a marked vertex on a graph quadratically faster than the corresponding random walk algorithm (Szegedy, FOCS 2004). However, quantum algorithms that actually find a marked element quadratically faster than a classical random walk were only known for the special case when the marked set consists of just a single vertex, or in the case of some specific graphs. We present a new quantum algorithm for finding a marked vertex in any graph, with any set of marked vertices, that is (up to a log factor) quadratically faster than the corresponding classical random walk.

FOS: Computer and information sciencesQuadratic growthQuantum PhysicsQuantum algorithmsSpeedupMarkov chainMarkov chainsProbability (math.PR)FOS: Physical sciencesRandom walkVertex (geometry)CombinatoricsQuadratic equationSearch by random walkQuantum searchComputer Science - Data Structures and AlgorithmsFOS: MathematicsData Structures and Algorithms (cs.DS)Quantum walkQuantum algorithmQuantum Physics (quant-ph)Mathematics - ProbabilityMathematicsQuantum walks
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Search by quantum walks on two-dimensional grid without amplitude amplification

2011

We study search by quantum walk on a finite two dimensional grid. The algorithm of Ambainis, Kempe, Rivosh (quant-ph/0402107) takes O(\sqrt{N log N}) steps and finds a marked location with probability O(1/log N) for grid of size \sqrt{N} * \sqrt{N}. This probability is small, thus amplitude amplification is needed to achieve \Theta(1) success probability. The amplitude amplification adds an additional O(\sqrt{log N}) factor to the number of steps, making it O(\sqrt{N} log N). In this paper, we show that despite a small probability to find a marked location, the probability to be within an O(\sqrt{N}) neighbourhood (at an O(\sqrt[4]{N}) distance) of the marked location is \Theta(1). This all…

FOS: Computer and information sciencesQuantum PhysicsComputer Science - Computational ComplexityComputer Science - Data Structures and AlgorithmsFOS: Physical sciencesData Structures and Algorithms (cs.DS)Computational Complexity (cs.CC)Nuclear ExperimentQuantum Physics (quant-ph)
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Parameterized Quantum Query Complexity of Graph Collision

2013

We present three new quantum algorithms in the quantum query model for \textsc{graph-collision} problem: \begin{itemize} \item an algorithm based on tree decomposition that uses $O\left(\sqrt{n}t^{\sfrac{1}{6}}\right)$ queries where $t$ is the treewidth of the graph; \item an algorithm constructed on a span program that improves a result by Gavinsky and Ito. The algorithm uses $O(\sqrt{n}+\sqrt{\alpha^{**}})$ queries, where $\alpha^{**}(G)$ is a graph parameter defined by \[\alpha^{**}(G):=\min_{VC\text{-- vertex cover of}G}{\max_{\substack{I\subseteq VC\\I\text{-- independent set}}}{\sum_{v\in I}{\deg{v}}}};\] \item an algorithm for a subclass of circulant graphs that uses $O(\sqrt{n})$ qu…

FOS: Computer and information sciencesQuantum PhysicsComputer Science - Computational ComplexityComputer Science::Information RetrievalComputer Science - Data Structures and AlgorithmsFOS: Physical sciencesData Structures and Algorithms (cs.DS)Computational Complexity (cs.CC)Quantum Physics (quant-ph)MathematicsofComputing_DISCRETEMATHEMATICS
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New Developments in Quantum Algorithms

2010

In this survey, we describe two recent developments in quantum algorithms. The first new development is a quantum algorithm for evaluating a Boolean formula consisting of AND and OR gates of size N in time O(\sqrt{N}). This provides quantum speedups for any problem that can be expressed via Boolean formulas. This result can be also extended to span problems, a generalization of Boolean formulas. This provides an optimal quantum algorithm for any Boolean function in the black-box query model. The second new development is a quantum algorithm for solving systems of linear equations. In contrast with traditional algorithms that run in time O(N^{2.37...}) where N is the size of the system, the …

FOS: Computer and information sciencesQuantum PhysicsComputer Science - Computational ComplexityComputerSystemsOrganization_MISCELLANEOUSComputer Science - Data Structures and AlgorithmsFOS: Physical sciencesTheoryofComputation_GENERALData Structures and Algorithms (cs.DS)Computational Complexity (cs.CC)Quantum Physics (quant-ph)
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Span-program-based quantum algorithm for the rank problem

2011

Recently, span programs have been shown to be equivalent to quantum query algorithms. It is an open problem whether this equivalence can be utilized in order to come up with new quantum algorithms. We address this problem by providing span programs for some linear algebra problems. We develop a notion of a high level span program, that abstracts from loading input vectors into a span program. Then we give a high level span program for the rank problem. The last section of the paper deals with reducing a high level span program to an ordinary span program that can be solved using known quantum query algorithms.

FOS: Computer and information sciencesQuantum PhysicsComputer Science - Data Structures and AlgorithmsComputer Science::Programming LanguagesFOS: Physical sciencesData Structures and Algorithms (cs.DS)Quantum Physics (quant-ph)Computer Science::Databases
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Quantum Speedups for Exponential-Time Dynamic Programming Algorithms

2018

In this paper we study quantum algorithms for NP-complete problems whose best classical algorithm is an exponential time application of dynamic programming. We introduce the path in the hypercube problem that models many of these dynamic programming algorithms. In this problem we are asked whether there is a path from $0^n$ to $1^n$ in a given subgraph of the Boolean hypercube, where the edges are all directed from smaller to larger Hamming weight. We give a quantum algorithm that solves path in the hypercube in time $O^*(1.817^n)$. The technique combines Grover's search with computing a partial dynamic programming table. We use this approach to solve a variety of vertex ordering problems o…

FOS: Computer and information sciencesQuantum PhysicsComputer Science - Data Structures and AlgorithmsFOS: Physical sciencesData Structures and Algorithms (cs.DS)Quantum Physics (quant-ph)
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Coins Make Quantum Walks Faster

2004

We show how to search N items arranged on a $\sqrt{N}\times\sqrt{N}$ grid in time $O(\sqrt N \log N)$, using a discrete time quantum walk. This result for the first time exhibits a significant difference between discrete time and continuous time walks without coin degrees of freedom, since it has been shown recently that such a continuous time walk needs time $\Omega(N)$ to perform the same task. Our result furthermore improves on a previous bound for quantum local search by Aaronson and Ambainis. We generalize our result to 3 and more dimensions where the walk yields the optimal performance of $O(\sqrt{N})$ and give several extensions of quantum walk search algorithms for general graphs. T…

FOS: Computer and information sciencesQuantum PhysicsComputer Science - Data Structures and AlgorithmsTheoryofComputation_GENERALFOS: Physical sciencesData Structures and Algorithms (cs.DS)Quantum Physics (quant-ph)
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