Superpositional Quantum Network Topologies
International Journal of Theoretical Physics, 43, 2029-2040 (2004) We introduce superposition-based quantum networks composed of (i) the classical perceptron model of multilayered, feedforward neural networks and (ii) the algebraic model of evolving reticular quantum structures as described in quant...
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| Main Authors | , , |
|---|---|
| Format | Journal Article |
| Language | English |
| Published |
12.11.2003
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| Subjects | |
| Online Access | Get full text |
| DOI | 10.48550/arxiv.q-bio/0311016 |
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| Abstract | International Journal of Theoretical Physics, 43, 2029-2040 (2004) We introduce superposition-based quantum networks composed of (i) the
classical perceptron model of multilayered, feedforward neural networks and
(ii) the algebraic model of evolving reticular quantum structures as described
in quantum gravity. The main feature of this model is moving from particular
neural topologies to a quantum metastructure which embodies many differing
topological patterns. Using quantum parallelism, training is possible on
superpositions of different network topologies. As a result, not only classical
transition functions, but also topology becomes a subject of training. The main
feature of our model is that particular neural networks, with different
topologies, are quantum states. We consider high-dimensional dissipative
quantum structures as candidates for implementation of the model. |
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| AbstractList | International Journal of Theoretical Physics, 43, 2029-2040 (2004) We introduce superposition-based quantum networks composed of (i) the
classical perceptron model of multilayered, feedforward neural networks and
(ii) the algebraic model of evolving reticular quantum structures as described
in quantum gravity. The main feature of this model is moving from particular
neural topologies to a quantum metastructure which embodies many differing
topological patterns. Using quantum parallelism, training is possible on
superpositions of different network topologies. As a result, not only classical
transition functions, but also topology becomes a subject of training. The main
feature of our model is that particular neural networks, with different
topologies, are quantum states. We consider high-dimensional dissipative
quantum structures as candidates for implementation of the model. |
| Author | Altman, Christopher Zapatrin, Roman Pykacz, Jaroslaw |
| Author_xml | – sequence: 1 givenname: Christopher surname: Altman fullname: Altman, Christopher – sequence: 2 givenname: Jaroslaw surname: Pykacz fullname: Pykacz, Jaroslaw – sequence: 3 givenname: Roman surname: Zapatrin fullname: Zapatrin, Roman |
| BackLink | https://doi.org/10.48550/arXiv.q-bio/0311016$$DView paper in arXiv https://doi.org/10.1023/B:IJTP.0000049008.51567.ec$$DView published paper (Access to full text may be restricted) |
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classical perceptron... |
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| SubjectTerms | Physics - Quantum Physics Quantitative Biology - Neurons and Cognition |
| Title | Superpositional Quantum Network Topologies |
| URI | https://arxiv.org/abs/q-bio/0311016 |
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