Analysis and proposal of a quantum classifier based on open quantum systems with amplitude information loading

• Ano de defesa: 2024
• Autor(a) principal: BRITO, Eduardo Barreto
• Orientador(a): PAULA NETO, Fernando Maciano de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Pernambuco
• Programa de Pós-Graduação: Programa de Pos Graduacao em Ciencia da Computacao
• País: Brasil
• Palavras-chave em Português: Neurônio artificial quântico; Inteligência artificial quântica; Computação quântica; Sistemas quânticos abertos; Aprendizagem de máquina
• Link de acesso: https://repositorio.ufpe.br/handle/123456789/57505

Although the studies on quantum algorithms have been progressing, it is still necessary to broaden the investigation of open quantum systems. In this study, we present the use of an open quantum system to implement a quantum classifier algorithm. Zhang et al. propose a one QuBit system interacting with the environment through a unitary operator that comes from the Hamiltonian. In our proposal, the input data is loaded into the amplitude of the environment instead of being in the unitary operator. This change positively impacts the performance of different databases tested and causes a difference in the system entanglement behavior. For evaluation, the Zhang et al. model and the proposed model were tested in four real-world datasets and seven other toy problems. The results are evaluated according to accuracy and F1-Score. A deeper analysis of the Iris dataset is also done, checking the creation of entanglement and an extensive random search for better parameters on the proposed model. The results show that for most real-world dataset configurations, the proposed model, although having a simpler decision area, performed better than the one inspired by the Zhang et al. model, and that there is no pattern for the system entanglement in the Iris Dataset. Due to an underperform for both models in a linearly separable problem, an exponential kernel was introduced. It resulted in an improvement in the accuracy of both models in most of the evaluated situations.