Desenvolvimento de redes neurais artificiais quânticas com aplicações em química - Vitor Daniel de Viterbo (2014)
Processing of complex data and in large amount requires the use of high-speed computers in Chemistry. Liquids systems simulations, atoms and molecules interaction and their identification require computers more robust to solve these problems. This thesis describes the research, modeling and development of a neural network thatutilizes the Quantum Computation to work as a quantum neural network. Additionally, this thesis implements studies of a quantum search algorithm and number of bounds states helium gas dimers, HeNe, HeAr, HeKr and HeXe can withstand. Three steps were followed A)variable phase method, for data generation B) classical neural network, using data generated by the step above. C) quantum neural network. The purpose of following these three steps was to validate the quantum neural network. The validation was based on the informationgenerated by the classic neural network coupled to a quantum algorithm to model a quantum neural network, which will learn the behavior of a chemical system previously studied. Quantum Computation and computer approaches proves the complex evolution of classiccomputing as well as changes related to hardware and software concepts.