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Proceedings Paper

Quantum optimization for solving nonconvex problem
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Paper Abstract

This paper presents a quantum optimization problem and solid-state quantum computing architectures. Quantum approach to global optimization and NP-complete problems are considered. Our approach to global optimization based on quantum mechanical entanglement, quantum resonant tunneling, cellular automaton and geometric control methods. A quantum optimization algorithm combines the properties of classical simulated annealing with the possibility of quantum tunneling between the minima. Quantum computation exploits the property of quantum states to implement quantum parallelism for global nonconvex optimization problem. This paper considers new mathematical models of classical (CL) and quantum-mechanical lattices (QML). System-theoretic results on the observability, controllability and minimal realizability theorems are formulated for CL. The cellular dynamaton (CD) based on quantum oscillators is presented. We investigate the conditions when stochastic resonance can occur through the interaction of dynamical neurons with intrinsic deterministic noise and an external periodic control.

Paper Details

Date Published: 4 August 2003
PDF: 12 pages
Proc. SPIE 5105, Quantum Information and Computation, (4 August 2003); doi: 10.1117/12.485658
Show Author Affiliations
Vitaliy Alexeevich Yatsenko, Institute of Space Research (Ukraine)


Published in SPIE Proceedings Vol. 5105:
Quantum Information and Computation
Eric Donkor; Andrew R. Pirich; Howard E. Brandt, Editor(s)

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