Reconhecimento quântico de padrões aplicados à sequências de DNA

• Ano de defesa: 2011
• Autor(a) principal: BARROS, Patrícia Silva Nascimento
• Orientador(a): OLIVEIRA JUNIOR, Wilson Rosa de
• Banca de defesa: STOSIC, Tatijana, STOSIC, Borko, FRANÇA, Felipe Maia Galvão
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal Rural de Pernambuco
• Programa de Pós-Graduação: Programa de Pós-Graduação em Biometria e Estatística Aplicada
• Departamento: Departamento de Estatística e Informática
• País: Brasil
• Palavras-chave em Português: Computação quântica; Reconhecimento quântico de padrões; Sequências de DNA; Abelha sem ferrão; Melipona quinquefasciata
• Palavras-chave em Inglês: Quantum computation; Quantum pattern recognition; DNA sequences; Singless bees
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
• Link de acesso: http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/5238

Quantum computing is a recent area of research that encompasses three known areas: mathematics, physics and computing. With the research in quantum algorithms came the need to understand and express such algorithms in terms of programming. Several languages and programming models for high-level quantum have been proposed in recent years. Quantum mechanics (QM) is a set of mathematical rules that serve for the construction of physical theories, from its inception until the present day it has been applied in various branches. In this context we developed the Quantum Computation, perhaps the most spectacular proposal for practical implementation of QM. The difficulty in developing quantum algorithms provides the use of alternative techniques to the solution of purely algorithmic problems, such as machine learning and genetic algorithms. Carlo Trugenberger proposes a model of quantum associative memory which binary patterns of n bits are stored in a quantum superposition of an appropriate subset of the computational basis of n qubits. This model solves the problem of insufficient capacity of the well known classical associative memory, providing a large improvement in capacity. The distribution proposed by Trugenberger uses the Hamming distance, where the amplitudes have a peak in the stored patterns, which has smaller distance from the entrance. The accuracy of pattern recognition can be adjusted by the parameter b, in other words increasing b increases the probability of recognition. This study examines the genetic diversity of stingless bees Melipona quinquefasciata, obtained from several wild colonies in different localities of the Chapada do Araripe-CE, Chapada da Ibiapaba-CE, city’s Canto do Buriti-PI and Luziânia-GO. DNA sequences were processed by replacing A by 00, G by 01, C by 10 and T by 11. The results show that this probability is very efficient to recognize the patterns of DNA sequences of the stingless bees Melipona quinquefasciata regions 18S and ITS1 partial. The algorithm is not computationally efficient on a classical computer, but is extremely efficient on a quantum computer. It was concluded that this method of recognition of quantum standards is better than the classic method used by Pereira.