Título: | EVOLVING QUANTUM ERROR CORRECTION CODES | ||||||||||||
Autor: |
DANIEL RIBAS TANDEITNIK |
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Colaborador(es): |
THIAGO BARBOSA DOS SANTOS GUERREIRO - Orientador |
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Catalogação: | 28/JUN/2022 | Língua(s): | ENGLISH - UNITED STATES |
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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. |
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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 |
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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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