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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
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
Statistical mechanics of complex networks
 Rev. Mod. Phys
"... Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as ra ..."
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Cited by 2083 (10 self)
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Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as random graphs, it is increasingly recognized that the topology and evolution of real
Traffic and related selfdriven manyparticle systems
, 2000
"... Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ‘‘phantom traffic jams’ ’ even though drivers all like to drive fast? ..."
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Cited by 336 (38 self)
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Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ‘‘phantom traffic jams’ ’ even though drivers all like to drive fast? What are the mechanisms behind stopandgo traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction in the volume of traffic cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize into lanes, while similar systems ‘‘freeze by heating’’? All of these questions have been answered by applying and extending methods from statistical physics and nonlinear dynamics to selfdriven manyparticle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particlebased), mesoscopic (gaskinetic), and macroscopic (fluiddynamic) models. Attention is also paid to the formulation of a micromacro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for selfdriven manyparticle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socioeconomic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well.
Implications of quantum automata for contextuality
 In CIAA, LNCS
, 2014
"... Abstract. We construct zeroerror quantum finite automata (QFAs) for promise problems which cannot be solved by boundederror probabilistic finite automata (PFAs). Here is a summary of our results: 1. There is a promise problem solvable by an exact twoway QFA in exponential expected time, but not b ..."
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Cited by 2 (0 self)
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Abstract. We construct zeroerror quantum finite automata (QFAs) for promise problems which cannot be solved by boundederror probabilistic finite automata (PFAs). Here is a summary of our results: 1. There is a promise problem solvable by an exact twoway QFA in exponential expected time
Quantum Algorithms for Finding Extrema with Unary Predicates
, 2007
"... We study the problem of finding the maximum or the minimum of a given set S = {x0, x1,... xn−1}, each element xi drawn from some finite universe U of real numbers. We assume that the inputs are abstracted within an oracle O where we can only gain information through unary comparisons in the form ”Is ..."
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We study the problem of finding the maximum or the minimum of a given set S = {x0, x1,... xn−1}, each element xi drawn from some finite universe U of real numbers. We assume that the inputs are abstracted within an oracle O where we can only gain information through unary comparisons in the form
Probabilistic Modeling of QuantumDot Cellular Automata
, 2007
"... Probabilistic modeling of quantumdot cellular automata ..."
Quantum Finite Multitape Automata
, 1999
"... Quantum finite automata were introduced by C. Moore, J. P. Crutchfield [MC 97], and by A. Kondacs and J. Watrous [KW 97]. This notion is not a generalization of the deterministic finite automata. Moreover, in [KW 97] it was proved that not all regular languages can be recognized by quantum finite au ..."
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Cited by 3 (2 self)
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language recognized by a quantum finite automaton but not by deterministic or probabilistic finite automata. This is the first result on a problem which can be solved by a quantum computer but not by a deterministic or probabilistic computer. Additionally we discover unexpected probabilistic automata
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