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Quantum Computing in Machine Learning: Performance Evaluation and Comparative Analysis
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| 1.73 MB | Adobe PDF |
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Resumo(s)
This thesis explores the integration of quantum computing into Machine Learning (ML), aiming
to evaluate the effectiveness and limitations of quantum-enhanced models compared to
classical approaches. As quantum technologies continue to develop, they offer the potential
for solving complex computational problems more efficiently, particularly in fields requiring
high-dimensional data processing. A literature review is conducted to contextualize current
advancements in Quantum Machine Learning, highlighting theoretical benefits and existing
practical limitations. This work investigates whether quantum methods, specifically the
Quantum Support Vector Classifier (QSVC), can provide measurable improvements over its
classical counterpart. The QSVC, implemented using IBM’s Qiskit ML library, replaces
traditional kernel functions with quantum kernels computed via quantum circuits and feature
maps, embedding classical data into a Hilbert space. The experimental portion of this thesis
compares classical and quantum models on two distinct datasets: one from the healthcare
domain and another from particle physics. Various parameters are tested in a constrained grid
search due to the high computational demands of quantum circuits. Results indicate that
while quantum models are significantly more resource-intensive, they can achieve
comparable or even improved performance on data that has more complex relations between
their variables. However, scalability remains a major challenge. This work concludes that
although QSVC does not yet consistently outperform classical models, it offers advantages in
specific contexts and lays the groundwork for identifying future use cases where quantum
computing may offer a practical advantage in ML.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Quantum Machine Learning Support Vector Classifier Quantum Support Vector Classifier Performance Evaluation Comparative Analysis SDG 9 - Industry, innovation and infrastructure