Results for 'quantum‐dot cellular automata'

975 found
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  1.  26
    Quantum calculation of thermal effect in quantum‐dot cellular automata.Ioan Sturzu & Mahfuza Khatun - 2005 - Complexity 10 (4):73-78.
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  2. Quantum features of natural cellular automata.Hans-Thomas Elze - 2016 - In Ignazio Licata (ed.), Beyond peaceful coexistence: the emergence of space, time and quantum. London: Imperial College Press.
     
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  3. On Cellular Automata Representation of Submicroscopic Physics: From Static Space to Zuse’s Calculating Space Hypothesis.Victor Christianto, Volodymyr Krasnoholovets & Florentin Smarandache - manuscript
    In some recent papers (G. ‘t Hooft and others), it has been argued that quantum mechanics can arise from classical cellular automata. Nonetheless, G. Shpenkov has proved that the classical wave equation makes it possible to derive a periodic table of elements, which is very close to Mendeleyev’s one, and describe also other phenomena related to the structure of molecules. Hence the classical wave equation complements Schrödinger’s equation, which implies the appearance of a cellular automaton molecular model (...)
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  4.  23
    Weyl, Dirac and Maxwell Quantum Cellular Automata: Analitical Solutions and Phenomenological Predictions of the Quantum Cellular Automata Theory of Free Fields.Alessandro Bisio, Giacomo Mauro D’Ariano, Paolo Perinotti & Alessandro Tosini - 2015 - Foundations of Physics 45 (10):1203-1221.
    Recent advances on quantum foundations achieved the derivation of free quantum field theory from general principles, without referring to mechanical notions and relativistic invariance. From the aforementioned principles a quantum cellular automata theory follows, whose relativistic limit of small wave-vector provides the free dynamics of quantum field theory. The QCA theory can be regarded as an extended quantum field theory that describes in a unified way all scales ranging from an hypothetical discrete Planck scale up to the usual (...)
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  5.  41
    Free Quantum Field Theory from Quantum Cellular Automata.Alessandro Bisio, Giacomo Mauro D’Ariano, Paolo Perinotti & Alessandro Tosini - 2015 - Foundations of Physics 45 (10):1137-1152.
    After leading to a new axiomatic derivation of quantum theory, the new informational paradigm is entering the domain of quantum field theory, suggesting a quantum automata framework that can be regarded as an extension of quantum field theory to including an hypothetical Planck scale, and with the usual quantum field theory recovered in the relativistic limit of small wave-vectors. Being derived from simple principles, the automata theory is quantum ab-initio, and does not assume Lorentz covariance and mechanical notions. (...)
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  6.  7
    Remarks on the structure of Clifford quantum cellular automata.Dirk-Michael Schlingemann - 2009 - In Krzysztof Stefanski (ed.), Open Systems and Information Dynamics. World scientific publishing company. pp. 16--02.
  7.  28
    Duality Between a Deterministic Cellular Automaton and a Bosonic Quantum Field Theory in 1+1 Dimensions.Gerard ’T. Hooft - 2013 - Foundations of Physics 43 (5):597-614.
    Methods developed in a previous paper are employed to define an exact correspondence between the states of a deterministic cellular automaton in 1+1 dimensions and those of a bosonic quantum field theory. The result may be used to argue that quantum field theories may be much closer related to deterministic automata than what is usually thought possible.
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  8.  26
    Quantum Walks, Weyl Equation and the Lorentz Group.Paolo Perinotti, Giacomo Mauro D’Ariano & Alessandro Bisio - 2017 - Foundations of Physics 47 (8):1065-1076.
    Quantum cellular automata and quantum walks provide a framework for the foundations of quantum field theory, since the equations of motion of free relativistic quantum fields can be derived as the small wave-vector limit of quantum automata and walks starting from very general principles. The intrinsic discreteness of this framework is reconciled with the continuous Lorentz symmetry by reformulating the notion of inertial reference frame in terms of the constants of motion of the quantum walk dynamics. In (...)
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  9. The computable universe: from prespace metaphysics to discrete quantum mechanics.Martin Leckey - 1997 - Dissertation, Monash University
    The central motivating idea behind the development of this work is the concept of prespace, a hypothetical structure that is postulated by some physicists to underlie the fabric of space or space-time. I consider how such a structure could relate to space and space-time, and the rest of reality as we know it, and the implications of the existence of this structure for quantum theory. Understanding how this structure could relate to space and to the rest of reality requires, I (...)
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  10. Cellular automata.Francesco Berto & Jacopo Tagliabue - 2012 - Stanford Encyclopedia of Philosophy.
    Cellular automata (henceforth: CA) are discrete, abstract computational systems that have proved useful both as general models of complexity and as more specific representations of non-linear dynamics in a variety of scientific fields. Firstly, CA are (typically) spatially and temporally discrete: they are composed of a finite or denumerable set of homogeneous, simple units, the atoms or cells. At each time unit, the cells instantiate one of a finite set of states. They evolve in parallel at discrete time (...)
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  11.  36
    Reversible cellular automata with memory of delay type.Ramón Alonso-Sanz - 2014 - Complexity 20 (1):49-56.
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  12. Cellular automata, modeling, and computation.Anouk Barberousse, Sara Franceschelli & Cyrille Imbert - unknown
    Cellular Automata (CA) based simulations are widely used in a great variety of domains, fromstatistical physics to social science. They allow for spectacular displays and numerical predictions. Are they forall that a revolutionary modeling tool, allowing for “direct simulation”, or for the simulation of “the phenomenon itself”? Or are they merely models "of a phenomenological nature rather than of a fundamental one”? How do they compareto other modeling techniques? In order to answer these questions, we present a systematic (...)
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  13.  25
    Classifying cellular automata automatically: Finding gliders, filtering, and relating space-time patterns, attractor basins, and theZ parameter.Andrew Wuensche - 1999 - Complexity 4 (3):47-66.
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  14.  28
    A cellular automata model can quickly approximate UDP and TCP network traffic.Richard R. Brooks, Christopher Griffin & T. Alan Payne - 2004 - Complexity 9 (3):32-40.
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  15. Cognitive cellular automata.Pete Mandik - 2008 - In Complex Biological Systems:. Icfai University Press.
    In this paper I explore the question of how artificial life might be used to get a handle on philosophical issues concerning the mind-body problem. I focus on questions concerning what the physical precursors were to the earliest evolved versions of intelligent life. I discuss how cellular automata might constitute an experimental platform for the exploration of such issues, since cellular automata offer a unified framework for the modeling of physical, biological, and psychological processes. I discuss (...)
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  16. Ed Fredkin and the Physics of Information - An Inside Story of an Outsider Scientist.Amit Hagar - 2016 - Information and Culture 51 (3):419-443.
    This article tells the story of Ed Fredkin, a pilot, programmer, engineer, hardware designer and entrepreneur, whose work inside and outside academia has influenced major developments in computer science and in the foundations of theoretical physics for the past fifty years.
     
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  17.  45
    Cellular automata and the sciences of complexity.Howard Gutowitz - 1996 - Complexity 1 (6):29-35.
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  18.  30
    Cellular automata (abstract and discussion): complex nonadaptive systems.Erica Jen - forthcoming - Complexity.
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  19. Cellular Automata in fluid dynamics: not so different.Anouk Barberousse & Cyrille Imbert - forthcoming - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics.
  20.  22
    Asynchronous cellular automata and pattern classification.Biswanath Sethi, Souvik Roy & Sukanta Das - 2016 - Complexity 21 (S1):370-386.
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  21.  95
    Combining causal Bayes nets and cellular automata: A hybrid modelling approach to mechanisms.Alexander Gebharter & Daniel Koch - 2021 - British Journal for the Philosophy of Science 72 (3):839-864.
    Causal Bayes nets (CBNs) can be used to model causal relationships up to whole mechanisms. Though modelling mechanisms with CBNs comes with many advantages, CBNs might fail to adequately represent some biological mechanisms because—as Kaiser (2016) pointed out—they have problems with capturing relevant spatial and structural information. In this paper we propose a hybrid approach for modelling mechanisms that combines CBNs and cellular automata. Our approach can incorporate spatial and structural information while, at the same time, it comes (...)
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  22. Fine Tuning Explained? Multiverses and Cellular Automata.Francisco José Soler Gil & Manuel Alfonseca - 2013 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 44 (1):153-172.
    The objective of this paper is analyzing to which extent the multiverse hypothesis provides a real explanation of the peculiarities of the laws and constants in our universe. First we argue in favor of the thesis that all multiverses except Tegmark’s “mathematical multiverse” are too small to explain the fine tuning, so that they merely shift the problem up one level. But the “mathematical multiverse" is surely too large. To prove this assessment, we have performed a number of experiments with (...)
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  23.  65
    How to make dull cellular automata complex by adding memory: Rule 126 case study.Genaro J. Martínez, Andrew Adamatzky, Juan C. Seck‐Tuoh‐Mora & Ramon Alonso‐Sanz - 2010 - Complexity 15 (6):34-49.
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  24.  68
    From Zeldovich Approximation to Burgers’ equation: A Plausible Route to Cellular Automata Adhesion Universe.Florentin Smarandache & Victor Christianto - manuscript
    Some years ago, Hidding et al. suggest that the emergence of intricate and pervasive weblike structure of the Universe on Megaparsec scales can be approximated by a well-known equation from fluid mechanics, the Burgers’ equation. The solution to this equation can be obtained from a geometrical formalism. The resulting Adhesion formalism provides deep insight into the dynamics and topology of the Cosmic Web. It uncovers a direct connection between the conditions in the very early Universe and the complex spatial patterns (...)
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  25.  7
    Studies on the synthesis and characterization of Zn1−xCdxS and Zn1−xCdxS:Mn2+semiconductor quantum dots.R. Sakthi Sudar Saravanan, D. Pukazhselvan & C. K. Mahadevan - 2011 - Philosophical Magazine 91 (3):389-403.
    Quantum dots (3–4 nm) of Zn1− x Cd x S (both free of Mn2+ and with Mn2+ incorporated) were synthesized through a novel solvothermal-microwave irradiation technique. Detailed structural analysis of the Zn1− x Cd x S and Zn1− x Cd x S:Mn2+ (x = 0, 0.25, 0.5, 0.75 and 1) materials was carried out using powder X-ray diffraction technique. For all the compositions, the crystallite size was controlled to less than 1.5 nm. The optical energy gap for Zn1− x Cd (...)
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  26.  51
    Chaos in a Quantum Dot with Spin-Orbit Coupling.K.-F. Berggren & T. Ouchterlony - 2001 - Foundations of Physics 31 (2):233-242.
    Level statistics and nodal point distribution in a rectangular semiconductor quantum dot are studied for different degrees of spin-orbit coupling. The chaotic features occurring from the spin-orbit coupling have no classical counterpart. Using experimental values for GaSb/InAs/GaSb semiconductor quantum wells we find that level repulsion can lead to the semi-Poisson distribution for nearest level separations. Nodal lines and nodal points are also investigated. Comparison is made with nodal point distributions for fully chaotic states.
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  27.  26
    Some applications of propositional logic to cellular automata.Stefano Cavagnetto - 2009 - Mathematical Logic Quarterly 55 (6):605-616.
    In this paper we give a new proof of Richardson's theorem [31]: a global function G[MATHEMATICAL DOUBLE-STRUCK CAPITAL A] of a cellular automaton [MATHEMATICAL DOUBLE-STRUCK CAPITAL A] is injective if and only if the inverse of G[MATHEMATICAL DOUBLE-STRUCK CAPITAL A] is a global function of a cellular automaton. Moreover, we show a way how to construct the inverse cellular automaton using the method of feasible interpolation from [20]. We also solve two problems regarding complexity of cellular (...)
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  28.  28
    Combinatorial properties of some cellular automata related to the mosaic cycle concept.Claude Lobry & Hervé Elmoznino - 2000 - Acta Biotheoretica 48 (3-4):219-242.
    A cellular automaton that is related to the "mosaic cycle concept" is considered. We explain why such automata sustain very often, but not always, n-periodic trajectories (n being the number of states of the automaton). Our work is a first step in the direction of a theory of these type of automata which might be useful in modeling mosaic successions.
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  29.  18
    How to make dull cellular automata complex by adding memory: Rule 126 case study.Genaro J. Martínez, Andrew Adamatzky, Juan C. Seck-Tuoh-Mora & Ramon Alonso-Sanz - 2010 - Complexity:NA-NA.
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  30.  52
    Energy for Two-electron Quantum Dots: The Quantization Rule Approach. [REVIEW]Xiao-Yan Gu - 2006 - Foundations of Physics 36 (12):1884-1892.
    The energy spectra for two electrons in a parabolic quantum dot are calculated by the quantization rule approach. The numerical results are in excellent agreement with the results by the method of integrating directly the Schrödinger equation, and better than those by the WKB method and the WKB-DP method.
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  31.  21
    Cellular automata and the sciences of complexity (part I): A review of some outstanding problems in the theory of cellular automata[REVIEW]Howard Gutowitz - 1996 - Complexity 1 (5):16-22.
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  32.  19
    The global dynamics of cellular automata, by Andrew Wuensche and Mike Lesser.Stuart Kauffman - 2000 - Complexity 5 (6):47-48.
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  33.  16
    Planigon tessellation cellular automata.Alexander Korobov - 1999 - Complexity 4 (6):31-38.
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  34.  45
    Brave new modeling: Cellular automata and artificial neural networks for mastering complexity in economics.Janette Aschenwald, Stefan Fink & Gottfried Tappeiner - 2001 - Complexity 7 (1):39-47.
  35.  16
    Basins of attraction in cellular automata.Andrew Wuensche - 2000 - Complexity 5 (6):19-25.
  36.  19
    A Solution to the Biodiversity Paradox by Logical Deterministic Cellular Automata.Vyacheslav L. Kalmykov & Lev V. Kalmykov - 2015 - Acta Biotheoretica 63 (2):203-221.
    The paradox of biological diversity is the key problem of theoretical ecology. The paradox consists in the contradiction between the competitive exclusion principle and the observed biodiversity. The principle is important as the basis for ecological theory. On a relatively simple model we show a mechanism of indefinite coexistence of complete competitors which violates the known formulations of the competitive exclusion principle. This mechanism is based on timely recovery of limiting resources and their spatio-temporal allocation between competitors. Because of limitations (...)
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  37.  47
    Islands of Stability: Engaging Emergence from Cellular Automata to the Occupy Movement.Andrew Pickering - 2014 - Zeitschrift für Medien- Und Kulturforschung 2014 (1):121-134.
    Instead of considering »being with« in terms of non-problematic, machine-like places, where reliable entities assemble in stable relationships, STS conjures up a world where the achievement of chancy stabilisations and synchronisations is local. We have to analyse how and where a certain regularity and predictability in the intersection of scientists and their instruments, say, or of human individuals and groups, is produced. The paper reviews models of emergence drawn from the history of cybernetics—the canonical »black box,« homeostats, and cellular (...)
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  38.  19
    Densities and entropies in cellular automata.Pierre Guillon & Charalampos Zinoviadis - 2012 - In S. Barry Cooper (ed.), How the World Computes. pp. 253--263.
  39. Emergent Behavior, Cellular Automata, and Our Game “Mr. Froggy”.Robert A. Henru, Narendra S. Chaudhari & Edmond C. Prakash - forthcoming - Emergence: Complexity and Organization.
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  40.  10
    Reason out Emergence from Cellular Automata Modeling.Leilei Qi & Huaxia Zhang - 2007 - In L. Magnani & P. Li (eds.), Model-Based Reasoning in Science, Technology, and Medicine. Springer. pp. 147--159.
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  41. Syntactic autonomy, cellular automata, and RNA editing: or why self-organization needs symbols to evolve and how it might evolve them.Luis M. Rocha - 2000 - In Jerry L. R. Chandler & Gertrudis van de Vijver (eds.), Closure: emergent organizations and their dynamics. New York, NY: New York Academy of Sciences. pp. 901.
     
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  42.  18
    Emergence and induction of cellular automata rules via probabilistic reinforcement paradigms.Burton Voorhees - 2006 - Complexity 11 (3):44-57.
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  43.  5
    Emergence and induction of cellular automata rules.Burton Voorhees - 2006 - Complexity 11 (3):45.
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  44.  19
    Distributed Control of a Manufacturing System with One-Dimensional Cellular Automata.Irving Barragan-Vite, Juan C. Seck-Tuoh-Mora, Norberto Hernandez-Romero, Joselito Medina-Marin & Eva S. Hernandez-Gress - 2018 - Complexity 2018:1-15.
    We present a distributed control modeling approach for an automated manufacturing system based on the dynamics of one-dimensional cellular automata. This is inspired by the fact that both cellular automata and manufacturing systems are discrete dynamical systems where local interactions given among their elements can lead to complex dynamics, despite the simple rules governing such interactions. The cellular automaton model developed in this study focuses on two states of the resources of a manufacturing system, namely, (...)
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  45.  44
    Guidelines for dynamics‐based parameterization of one‐dimensional cellular automata rule spaces.Gina Maira Barbosa de Oliveira, Pedro P. B. de Oliveira & Nizam Omar - 2000 - Complexity 6 (2):63-71.
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  46.  23
    The emergence of dynamical complexity: An exploration using elementary cellular automata.Eduardo Mizraji - 2004 - Complexity 9 (6):33-42.
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  47.  24
    Structural and functional growth in self‐reproducing cellular automata.Eleonora Bilotta & Pietro Pantano - 2006 - Complexity 11 (6):12-29.
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  48.  4
    Islands of Stability: Engaging Emergence from Cellular Automata to the Occupy Movement.Andrew Pickering - 2014 - Zeitschrift für Medien- Und Kulturforschung 5 (1):121-134.
    Instead of considering »being with« in terms of non-problematic, machine-like places, where reliable entities assemble in stable relationships, STS conjures up a world where the achievement of chancy stabilisations and synchronisations is local.We have to analyse how and where a certain regularity and predictability in the intersection of scientists and their instruments, say, or of human individuals and groups, is produced.The paper reviews models of emergence drawn from the history of cybernetics—the canonical »black box,« homeostats, and cellular automata—to (...)
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  49.  37
    Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian.Jun Yang, Weiling Liu, Yonghua Li, Xueming Li & Quansheng Ge - 2018 - Complexity 2018:1-17.
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  50.  32
    Is prediction possible? Chaotic behavior of Multiple Equilibria Regulation Model in cellular automata topology.Ioannis D. Katerelos & Andreas G. Koulouris - 2004 - Complexity 10 (1):23-36.
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