Superpositional Quantum Network Topologies Export

@misc{citeulike:737, abstract = {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.}, archivePrefix = {arXiv}, author = {Altman, Christopher and Pykacz, Jaroslaw and Zapatrin, Roman}, citeulike-article-id = {737}, citeulike-linkout-0 = {http://arxiv.org/abs/q-bio.NC/0311016}, citeulike-linkout-1 = {http://arxiv.org/pdf/q-bio.NC/0311016}, day = {30}, eprint = {q-bio.NC/0311016}, keywords = {networks, quantum-networks, topology}, month = {July}, posted-at = {2004-11-16 22:45:36}, title = {Superpositional Quantum Network Topologies}, url = {http://arxiv.org/abs/q-bio.NC/0311016}, year = {2004} }

TY - ELEC ID - citeulike:737 L3 - citeulike-article-id:737 N2 - 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. TI - Superpositional Quantum Network Topologies KW - networks KW - quantum-networks KW - topology AU - Altman, Christopher AU - Pykacz, Jaroslaw AU - Zapatrin, Roman PY - 2004/07/30/ UR - http://arxiv.org/abs/q-bio.NC/0311016 ER -