882 research outputs found
Synthetic Quantum Systems
So far proposed quantum computers use fragile and environmentally sensitive
natural quantum systems. Here we explore the new notion that synthetic quantum
systems suitable for quantum computation may be fabricated from smart
nanostructures using topological excitations of a stochastic neural-type
network that can mimic natural quantum systems. These developments are a
technological application of process physics which is an information theory of
reality in which space and quantum phenomena are emergent, and so indicates the
deep origins of quantum phenomena. Analogous complex stochastic dynamical
systems have recently been proposed within neurobiology to deal with the
emergent complexity of biosystems, particularly the biodynamics of higher brain
function. The reasons for analogous discoveries in fundamental physics and
neurobiology are discussed.Comment: 16 pages, Latex, 1 eps figure fil
Smart Nanostructures and Synthetic Quantum Systems
So far proposed quantum computers use fragile and environmentally sensitive
natural quantum systems. Here we explore the notion that synthetic quantum
systems suitable for quantum computation may be fabricated from smart
nanostructures using topological excitations of a neural-type network that can
mimic natural quantum systems. These developments are a technological
application of process physics which is a semantic information theory of
reality in which space and quantum phenomena are emergent.Comment: LaTex,14 pages 1 eps file. To be published in BioMEMS and Smart
Nanostructures, Proceedings of SPIE Conference #4590, ed. L. B. Kis
Parallel computing for brain simulation
[Abstract] Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced.
Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain.
Conclusion: This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028
Evolving Quantum Circuits and an FPGA-based Quantum Computing Emulator
The goal of the PQLG group is to develop complete methodologies, software tools and circuits for quantum logic. Our interests are mainly in logic synthesis for quantum circuits and quantum system design [10]. Emulation of quantum circuits using standard reconfigurable FPGA technology and FPGA-based Evolvable Quantum Hardware, proposed here, are research areas not yet dealt with by other research groups. A parallel software simulator was presented in [13]
Quantum Associative Memory
This paper combines quantum computation with classical neural network theory
to produce a quantum computational learning algorithm. Quantum computation uses
microscopic quantum level effects to perform computational tasks and has
produced results that in some cases are exponentially faster than their
classical counterparts. The unique characteristics of quantum theory may also
be used to create a quantum associative memory with a capacity exponential in
the number of neurons. This paper combines two quantum computational algorithms
to produce such a quantum associative memory. The result is an exponential
increase in the capacity of the memory when compared to traditional associative
memories such as the Hopfield network. The paper covers necessary high-level
quantum mechanical and quantum computational ideas and introduces a quantum
associative memory. Theoretical analysis proves the utility of the memory, and
it is noted that a small version should be physically realizable in the near
future
Morphological Computation: Nothing but Physical Computation
The purpose of this paper is to argue against the claim that morphological computation is substantially different from other kinds of physical computation. I show that some (but not all) purported cases of morphological computation do not count as specifically computational, and that those that do are solely physical computational systems. These latter cases are not, however, specific enough: all computational systems, not only morphological ones, may (and sometimes should) be studied in various ways, including their energy efficiency, cost, reliability, and durability. Second, I critically analyze the notion of “offloading” computation to the morphology of an agent or robot, by showing that, literally, computation is sometimes not offloaded but simply avoided. Third, I point out that while the morphology of any agent is indicative of the environment that it is adapted to, or informative about that environment, it does not follow that every agent has access to its morphology as the model of its environment
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