The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Peter Nyman - One of the best experts on this subject based on the ideXlab platform.
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On hyperbolic interferences in the quantum-like representation algorithm for the case of triple–valued observables
arXiv: Quantum Physics, 2011Co-Authors: Peter NymanAbstract:In this thesis we study quantum-like representation and simulation of quantum algorithms by using classical computers.The quantum--like representation algorithm (QLRA) was introduced by A. Khrennikov (1997) to solve the ``inverse Born's rule problem'', i.e. to construct a representation of probabilistic data-- measured in any context of science-- and represent this data by a complex or more general probability amplitude which matches a generalization of Born's rule.The outcome from QLRA matches the formula of total probability with an additional trigonometric, hyperbolic or hyper-trigonometric interference term and this is in fact a generalization of the familiar formula of interference of probabilities. We study representation of statistical data (of any origin) by a probability amplitude in a complex algebra and a Clifford algebra (algebra of hyperbolic numbers). The statistical data is collected from measurements of two dichotomous and trichotomous observables respectively. We see that only special statistical data (satisfying a number of nonlinear constraints) have a quantum--like representation. We also study simulations of quantum computers on classical computers.Although it can not be denied that great progress have been made in quantum technologies, it is clear that there is still a huge gap between the creation of experimental quantum computers and realization of a quantum computer that can be used in applications. Therefore the simulation of quantum computations on classical computers became an important part in the attempt to cover this gap between the theoretical mathematical formulation of quantum mechanics and the realization of quantum computers. Of course, it can not be expected that quantum algorithms would help to solve NP problems for polynomial time on classical computers. However, this is not at all the aim of classical simulation. The second part of this thesis is devoted to adaptation of the Mathematica Symbolic Language to known quantum algorithms and corresponding simulations on classical computers. Concretely we represent Simon's algorithm, Deutsch-Josza algorithm, Shor's algorithm, Grover's algorithm and quantum error-correcting codes in the Mathematica Symbolic Language. We see that the same framework can be used for all these algorithms. This framework will contain the characteristic property of the Symbolic Language representation of quantum computing and it will be a straightforward matter to include future algorithms in this framework.
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QI - A Symbolic Classical Computer Language for Simulation of Quantum Algorithms
Quantum Interaction, 2009Co-Authors: Peter NymanAbstract:Quantum computing is an extremely promising research combining theoretical and experimental quantum physics, mathematics, quantum information theory and computer science. Classical simulation of quantum computations will cover part of the gap between the theoretical mathematical formulation of quantum mechanics and the realization of quantum computers. One of the most important problems in "quantum computer science" is the development of new Symbolic Languages for quantum computing and the adaptation of existing Symbolic Languages for classical computing to quantum algorithms. The present paper is devoted to the adaptation of the Mathematica Symbolic Language to known quantum algorithms and corresponding simulation on the classical computer. Concretely we shall represent in the Mathematica Symbolic Language Simon's algorithm, the Deutsch-Josza algorithm, Grover's algorithm, Shor's algorithm and quantum error-correcting codes. We shall see that the same framework can be used for all these algorithms. This framework will contain the characteristic property of the Symbolic Language representation of quantum computing and it will be a straightforward matter to include this framework in future algorithms.
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Simulation of Quantum Error Correcting Code
arXiv: Quantum Physics, 2008Co-Authors: Peter NymanAbstract:In this thesis we study quantum-like representation and simulation of quantum algorithms by using classical computers.The quantum--like representation algorithm (QLRA) was introduced by A. Khrennikov (1997) to solve the ``inverse Born's rule problem'', i.e. to construct a representation of probabilistic data-- measured in any context of science-- and represent this data by a complex or more general probability amplitude which matches a generalization of Born's rule.The outcome from QLRA matches the formula of total probability with an additional trigonometric, hyperbolic or hyper-trigonometric interference term and this is in fact a generalization of the familiar formula of interference of probabilities. We study representation of statistical data (of any origin) by a probability amplitude in a complex algebra and a Clifford algebra (algebra of hyperbolic numbers). The statistical data is collected from measurements of two dichotomous and trichotomous observables respectively. We see that only special statistical data (satisfying a number of nonlinear constraints) have a quantum--like representation. We also study simulations of quantum computers on classical computers.Although it can not be denied that great progress have been made in quantum technologies, it is clear that there is still a huge gap between the creation of experimental quantum computers and realization of a quantum computer that can be used in applications. Therefore the simulation of quantum computations on classical computers became an important part in the attempt to cover this gap between the theoretical mathematical formulation of quantum mechanics and the realization of quantum computers. Of course, it can not be expected that quantum algorithms would help to solve NP problems for polynomial time on classical computers. However, this is not at all the aim of classical simulation. The second part of this thesis is devoted to adaptation of the Mathematica Symbolic Language to known quantum algorithms and corresponding simulations on classical computers. Concretely we represent Simon's algorithm, Deutsch-Josza algorithm, Shor's algorithm, Grover's algorithm and quantum error-correcting codes in the Mathematica Symbolic Language. We see that the same framework can be used for all these algorithms. This framework will contain the characteristic property of the Symbolic Language representation of quantum computing and it will be a straightforward matter to include future algorithms in this framework.
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Simulation of Simon’s Algorithm in Mathematica
2008Co-Authors: Peter NymanAbstract:In this thesis we study quantum-like representation and simulation of quantum algorithms by using classical computers.The quantum--like representation algorithm (QLRA) was introduced by A. Khrennikov (1997) to solve the ``inverse Born's rule problem'', i.e. to construct a representation of probabilistic data-- measured in any context of science-- and represent this data by a complex or more general probability amplitude which matches a generalization of Born's rule.The outcome from QLRA matches the formula of total probability with an additional trigonometric, hyperbolic or hyper-trigonometric interference term and this is in fact a generalization of the familiar formula of interference of probabilities. We study representation of statistical data (of any origin) by a probability amplitude in a complex algebra and a Clifford algebra (algebra of hyperbolic numbers). The statistical data is collected from measurements of two dichotomous and trichotomous observables respectively. We see that only special statistical data (satisfying a number of nonlinear constraints) have a quantum--like representation. We also study simulations of quantum computers on classical computers.Although it can not be denied that great progress have been made in quantum technologies, it is clear that there is still a huge gap between the creation of experimental quantum computers and realization of a quantum computer that can be used in applications. Therefore the simulation of quantum computations on classical computers became an important part in the attempt to cover this gap between the theoretical mathematical formulation of quantum mechanics and the realization of quantum computers. Of course, it can not be expected that quantum algorithms would help to solve NP problems for polynomial time on classical computers. However, this is not at all the aim of classical simulation. The second part of this thesis is devoted to adaptation of the Mathematica Symbolic Language to known quantum algorithms and corresponding simulations on classical computers. Concretely we represent Simon's algorithm, Deutsch-Josza algorithm, Shor's algorithm, Grover's algorithm and quantum error-correcting codes in the Mathematica Symbolic Language. We see that the same framework can be used for all these algorithms. This framework will contain the characteristic property of the Symbolic Language representation of quantum computing and it will be a straightforward matter to include future algorithms in this framework.
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Representation of Quantum Algorithms with Symbolic Language and Simulation on Classical Computer
2008Co-Authors: Peter NymanAbstract:Utvecklandet av kvantdatorn ar ett ytterst lovande projekt som kombinerar teoretisk och experimental kvantfysik, matematik, teori om kvantinformation och datalogi. Under forsta steget i utvecklande ...
Antonin Rossier-bisaillon - One of the best experts on this subject based on the ideXlab platform.
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The Structural Effects of Modality on the Rise of Symbolic Language: A Rebuttal of Evolutionary Accounts and a Laboratory Demonstration
Frontiers in psychology, 2018Co-Authors: Victor J. Boucher, Annie C. Gilbert, Antonin Rossier-bisaillonAbstract:Why does Symbolic communication in humans develop primarily in an oral medium, and how do theories of Language origin explain this? Non-human primates, despite their ability to learn and use Symbolic signs, do not develop symbols as in oral Language. This partly owes to the lack of a direct cortico-motoneuron control of vocalizations in these species compared to humans. Yet such modality-related factors that can impinge on the rise of Symbolic Language are interpreted differently in two types of evolutionary storylines. (1) Some theories posit that Symbolic Language originated in a gestural modality, as in "sign Languages." However, this overlooks work on emerging sign and spoken Languages showing that gestures and speech shape signs differently. (2) In modality-dependent theories, some emphasize the role of iconic sounds, though these lack the efficiency of arbitrary symbols. Other theorists suggest that ontogenesis serves to identify human-specific mechanisms underlying an evolutionary shift from pitch varying to orally modulated vocalizations (babble). This shift creates numerous oral features that can support efficient Symbolic associations. We illustrate this principle using a sound-picture association task with 40 learners who hear words in an unfamiliar Language (Mandarin) with and without a filtering of oral features. Symbolic associations arise more rapidly and accurately for sounds containing oral features compared to sounds bearing only pitch features, an effect also reported in experiments with infants. The results imply that, beyond a competence to learn and use symbols, the rise of Symbolic Language rests on the types of signs that a modality of expression affords.
A. Massarotti - One of the best experts on this subject based on the ideXlab platform.
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A parallel Symbolic Language for vision analysis
Proceedings of the First International Conference on Massively Parallel Computing Systems (MPCS) The Challenges of General-Purpose and Special-Purpose, 1Co-Authors: M. Giordano, M.m. Furnari, A. MassarottiAbstract:In this paper we describe the design criteria for a Parallel Symbolic Language for Vision Analysis. The essential feature of a such Language is the use of the notion of structured parallelism in order to capture all the forms of parallelism used in vision analysis. After introducing the need for Symbolic Parallel Languages for the vision analysis, we show how PARALATION LISP can be extended to manage it in a user transparent way. Then, we describe the design criteria used for our Parallel Symbolic Vision Analysis Language project and the current implementation status. >
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A parallel Symbolic Language for vision analysis
1993 Computer Architectures for Machine Perception, 1Co-Authors: M.m. Furnari, M. Giordano, A. MassarottiAbstract:The authors present an extended version of Paralation Lisp that we developed and used to program, in a unified and user friendly parallel programming environment, a large part of the computer vision algorithms. They describe the computer vision area, motivating the need for the notion of structured parallelism. Next, the Paralation Lisp flexibility, that allows one to cope with structured parallelism, is described. Furthermore, how to build a complex hierarchy of objects relating to vision applications, and how to attach a different form of parallelism to each one is also described starting from the paralation notion. Finally, current research efforts in developing the parallel implementation strategies of the Paralation Lisp on a distributed memory parallel system (DMPS) is described.
Gordon Wyeth - One of the best experts on this subject based on the ideXlab platform.
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Find my office: Navigating real space from semantic descriptions
2016Co-Authors: Ben Talbot, Obadiah Lam, Ruth Schulz, Feras Dayoub, Ben Upcroft, Gordon WyethAbstract:This paper shows that by using only Symbolic Language phrases, a mobile robot can purposefully navigate to specified rooms in previously unexplored environments. The robot intelligently organises a Symbolic Language description of the unseen environment and “imagines” a representative map, called the abstract map. The abstract map is an internal representation of the topological structure and spatial layout of Symbolically defined locations. To perform goal-directed exploration, the abstract map creates a high-level semantic plan to reason about spaces beyond the robot’s known world. While completing the plan, the robot uses the metric guidance provided by a spatial layout, and grounded observations of door labels, to efficiently guide its navigation. The system is shown to complete exploration in unexplored spaces by travelling only 13.3% further than the optimal path.
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ICRA - Find my office: Navigating real space from semantic descriptions
2016 IEEE International Conference on Robotics and Automation (ICRA), 2016Co-Authors: Ben Talbot, Obadiah Lam, Ruth Schulz, Feras Dayoub, Ben Upcroft, Gordon WyethAbstract:This paper shows that by using only Symbolic Language phrases, a mobile robot can purposefully navigate to specified rooms in previously unexplored environments. The robot intelligently organises a Symbolic Language description of the unseen environment and “imagines” a representative map, called the abstract map. The abstract map is an internal representation of the topological structure and spatial layout of Symbolically defined locations. To perform goal-directed exploration, the abstract map creates a high-level semantic plan to reason about spaces beyond the robot's known world. While completing the plan, the robot uses the metric guidance provided by a spatial layout, and grounded observations of door labels, to efficiently guide its navigation. The system is shown to complete exploration in unexplored spaces by travelling only 13.3% further than the optimal path.
Victor J. Boucher - One of the best experts on this subject based on the ideXlab platform.
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The Structural Effects of Modality on the Rise of Symbolic Language: A Rebuttal of Evolutionary Accounts and a Laboratory Demonstration
Frontiers in psychology, 2018Co-Authors: Victor J. Boucher, Annie C. Gilbert, Antonin Rossier-bisaillonAbstract:Why does Symbolic communication in humans develop primarily in an oral medium, and how do theories of Language origin explain this? Non-human primates, despite their ability to learn and use Symbolic signs, do not develop symbols as in oral Language. This partly owes to the lack of a direct cortico-motoneuron control of vocalizations in these species compared to humans. Yet such modality-related factors that can impinge on the rise of Symbolic Language are interpreted differently in two types of evolutionary storylines. (1) Some theories posit that Symbolic Language originated in a gestural modality, as in "sign Languages." However, this overlooks work on emerging sign and spoken Languages showing that gestures and speech shape signs differently. (2) In modality-dependent theories, some emphasize the role of iconic sounds, though these lack the efficiency of arbitrary symbols. Other theorists suggest that ontogenesis serves to identify human-specific mechanisms underlying an evolutionary shift from pitch varying to orally modulated vocalizations (babble). This shift creates numerous oral features that can support efficient Symbolic associations. We illustrate this principle using a sound-picture association task with 40 learners who hear words in an unfamiliar Language (Mandarin) with and without a filtering of oral features. Symbolic associations arise more rapidly and accurately for sounds containing oral features compared to sounds bearing only pitch features, an effect also reported in experiments with infants. The results imply that, beyond a competence to learn and use symbols, the rise of Symbolic Language rests on the types of signs that a modality of expression affords.
