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ETDs @PUC-Rio
Estatística
Título: EVOLVING QUANTUM ERROR CORRECTION CODES
Autor: DANIEL RIBAS TANDEITNIK
Colaborador(es): THIAGO BARBOSA DOS SANTOS GUERREIRO - Orientador
Catalogação: 28/JUN/2022 Língua(s): ENGLISH - UNITED STATES
Tipo: TEXT Subtipo: THESIS
Notas: [pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio.
[en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio.
Referência(s): [pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=59800&idi=1
[en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=59800&idi=2
DOI: https://doi.org/10.17771/PUCRio.acad.59800
Resumo:
Computational methods become essential in the face of complex problems where human intuition and traditional methods fail. Recent works present artificial neural networks capable of efficiently performing tasks intractable by conventional algorithms using machine learning, rendering it one of the most popular methods. Concomitantly, genetic algorithms, inspired by the biological processes of natural selection and mutation, have been used as a metaheuristic method to find solutions to optimization problems. We then raise the question of whether genetic algorithms have the potential to solve problems in the context of quantum computing, where human intuition decreases as physical systems grow. Specifically, we focus on the evolution of quantum error-correcting codes within the stabilizer code formalism. By specifying an appropriate fitness function, we show that we can evolve celebrated codes, such as the Perfect and Shor s code with respectively 5 and 9 qubits, in addition to new unanticipated examples. Additionally, we compared it with a brute force random search and verified an increasing superiority of the genetic algorithm as the total number of qubits increases. Given the results, we foresee that genetic algorithms can become valuable tools to perform complex applications in quantum systems and produce tailored circuits that satisfy restrictions imposed by hardware.
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