Navegando por Assunto "Computação quântica"

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Aprendizagem de máquina quântica e comitê quântico de classificadores
(2019-12-02) Araujo, Ismael Cesar da Silva; Nascimento, André Câmara Alves do; Silva, Adenilton José da; http://lattes.cnpq.br/0314035098884256; http://lattes.cnpq.br/0622594061462533; http://lattes.cnpq.br/7125338940009959
Quantum machine learning is a subarea of quantum computing that studies, among other things, the creation of equivalent classical classifiers. An ensemble of classifiers is a classification model in which the output is a combined result of the outputs of the classifiers contained in it. With the premiss that when using a sufficiently large ensemble with average classifiers, a good performance can still be obtained. This work investigates the differences in the performance of a quantum equivalent of an ensemble of classifiers, using trained and untrained classifiers. Where the simulation was mane, which the performance was measured through the calculation of the amplitude probabilities of the system. And the machine learning models of the ensemble were executed over benchmark datasets made available by scikitlearn library.
Estudo Teórico de Propriedades Estruturais e Eletrônicas e a construção de Modelos QSAR aplicados ao Cálculo da LD50 de alguns Neurotransmissores
(2019-07-10) Santos, Douglas Amorim dos; Bastos, Cristiano Costa; http://lattes.cnpq.br/6385190604693576; http://lattes.cnpq.br/0592521567528784
Neurotransmitters are chemicals produced by neurons with the biosignaling function, acting on synapses, which are the junction point of the neuron with another cell. There are three main categories of neurotransmitters, amino acids, biogenic amines and peptides. Studies have been conducted to understand how these substances are synthesized, their storage, transport and synapse mechanisms. In this work, molecular modeling was performed for nine neurotransmitters, Dopamine, Serotonin, Acetylcholine, Glutamic Acid, Adrenaline, Morphine,-Butyric Acid, Melatonin and Noradrenaline. Molecular modeling was performed using ab initio (HF and MP2), Density Functional Theory (DFT-B3LYP) and Semi-Empirical (AM1) methods, using the Gaussian 09 program. structural and electronic structures for the nine molecules, including total energy, Homo orbital energy and Lumo, ionization potential, electronic a nity, neutral molecule volumes, and the volume of their cations and anions, etc. These properties were analyzed by quantitative structure-activity relationship (QSAR), and mathematical models were constructed to predict, through multivariate analysis, the LD50 for these molecules