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A Fast Quantum Mechanical Algorithm for Database Search
 ANNUAL ACM SYMPOSIUM ON THEORY OF COMPUTING
, 1996
"... Imagine a phone directory containing N names arranged in completely random order. In order to find someone's phone number with a probability of , any classical algorithm (whether deterministic or probabilistic)
will need to look at a minimum of names. Quantum mechanical systems can be in a supe ..."
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Cited by 1126 (10 self)
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Imagine a phone directory containing N names arranged in completely random order. In order to find someone's phone number with a probability of , any classical algorithm (whether deterministic or probabilistic)
will need to look at a minimum of names. Quantum mechanical systems can be in a
Algorithms for Quantum Computation: Discrete Logarithms and Factoring
, 1994
"... A computer is generally considered to be a universal computational device; i.e., it is believed able to simulate any physical computational device with a increase in computation time of at most a polynomial factor. It is not clear whether this is still true when quantum mechanics is taken into consi ..."
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Cited by 1103 (7 self)
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into consideration. Several researchers, starting with David Deutsch, have developed models for quantum mechanical computers and have investigated their computational properties. This paper gives Las Vegas algorithms for finding discrete logarithms and factoring integers on a quantum computer that take a number
Quantum mechanics helps in searching for a needle in a haystack
, 1997
"... Quantum mechanics can speed up a range of search applications over unsorted data. For example imagine a phone directory containing N names arranged in completely random order. To find someone's phone number with a probability of 50 % , any classical algorithm (whether deterministic or probabili ..."
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Cited by 435 (10 self)
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Quantum mechanics can speed up a range of search applications over unsorted data. For example imagine a phone directory containing N names arranged in completely random order. To find someone's phone number with a probability of 50 % , any classical algorithm (whether deterministic
Fast Parallel Algorithms for ShortRange Molecular Dynamics
 JOURNAL OF COMPUTATIONAL PHYSICS
, 1995
"... Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of interatomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dyn ..."
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Cited by 622 (6 self)
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Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of interatomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular
Grover’s Quantum Searching Algorithm is Optimal” Phys
 Rev. A
, 1999
"... I improve the tight bound on quantum searching [4] to a matching bound, thus showing that for any probability of success Grover’s quantum searching algorithm is optimal. E.g. for near certain success we have to query the oracle π/4 √ N times, where N is the size of the search space. I also show that ..."
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Cited by 101 (0 self)
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I improve the tight bound on quantum searching [4] to a matching bound, thus showing that for any probability of success Grover’s quantum searching algorithm is optimal. E.g. for near certain success we have to query the oracle π/4 √ N times, where N is the size of the search space. I also show
Quantum searching, counting and amplitude amplification by eigenvector analysis
 IN PROCEEDINGS OF RANDOMIZED ALGORITHMS, WORKSHOP OF MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE
, 1998
"... Grovers quantum searching algorithm uses a quantum computer to find the solution to fx for a given function f The algorithm which repeatedly applies a certain operator G has led to a major family of quantum algorithms for generating and counting solutions to fx for more general f By st ..."
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Cited by 17 (1 self)
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Grovers quantum searching algorithm uses a quantum computer to find the solution to fx for a given function f The algorithm which repeatedly applies a certain operator G has led to a major family of quantum algorithms for generating and counting solutions to fx for more general f
A New Kind of Science
, 2002
"... “Somebody says, ‘You know, you people always say that space is continuous. How do you know when you get to a small enough dimension that there really are enough points in between, that it isn’t just a lot of dots separated by little distances? ’ Or they say, ‘You know those quantum mechanical amplit ..."
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Cited by 850 (0 self)
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“Somebody says, ‘You know, you people always say that space is continuous. How do you know when you get to a small enough dimension that there really are enough points in between, that it isn’t just a lot of dots separated by little distances? ’ Or they say, ‘You know those quantum mechanical
Focused crawling: a new approach to topicspecific Web resource discovery
, 1999
"... The rapid growth of the WorldWide Web poses unprecedented scaling challenges for generalpurpose crawlers and search engines. In this paper we describe a new hypertext resource discovery system called a Focused Crawler. The goal of a focused crawler is to selectively seek out pages that are relevan ..."
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Cited by 628 (10 self)
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The rapid growth of the WorldWide Web poses unprecedented scaling challenges for generalpurpose crawlers and search engines. In this paper we describe a new hypertext resource discovery system called a Focused Crawler. The goal of a focused crawler is to selectively seek out pages
DecisionTheoretic Planning: Structural Assumptions and Computational Leverage
 JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 1999
"... Planning under uncertainty is a central problem in the study of automated sequential decision making, and has been addressed by researchers in many different fields, including AI planning, decision analysis, operations research, control theory and economics. While the assumptions and perspectives ..."
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Cited by 510 (4 self)
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Planning under uncertainty is a central problem in the study of automated sequential decision making, and has been addressed by researchers in many different fields, including AI planning, decision analysis, operations research, control theory and economics. While the assumptions and perspectives adopted in these areas often differ in substantial ways, many planning problems of interest to researchers in these fields can be modeled as Markov decision processes (MDPs) and analyzed using the techniques of decision theory. This paper presents an overview and synthesis of MDPrelated methods, showing how they provide a unifying framework for modeling many classes of planning problems studied in AI. It also describes structural properties of MDPs that, when exhibited by particular classes of problems, can be exploited in the construction of optimal or approximately optimal policies or plans. Planning problems commonly possess structure in the reward and value functions used to de...
An introduction to variational methods for graphical models
 TO APPEAR: M. I. JORDAN, (ED.), LEARNING IN GRAPHICAL MODELS
"... ..."
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