# name
Quantum function fitting and classification beyond the single-qubit model

# metadata.dc.description[].value
Màster Oficial de Ciència i Tecnologia Quàntiques / Quantum Science and Technology, Facultat de Física, Universitat de Barcelona. Curs: 2021-2022. Tutora: Alba Cervera-Lierta.

# metadata.dc.description.abstract[].value
Quantum Neural Networks (QNNs) have emerged as one promising Quantum
Machine Learning (QML) technique. While the models for single and
multi-qubit QNNs have been extensively studied, it remains unknown if using
higher-dimensional systems provide any advantage. In this work, we investigate
the theoretical foundation of the qubit model and we compare it with the
qutrit prototype. First, we show that a single qubit can reproduce a Fourier series,
while a qutrit can fit a more complicated type of function, with additional
degrees of freedom that the model can adjust. Second, we explore the benefits
of the third extra level of the qutrit for the classification task. In addition,
we examine the two-qubit classifier and see that using a local cost function
on the training improves the results, according to recent studies. Beyond the
theoretical discussion, we provide numerical benchmarks of the models studied.

# metadata.dc.source[].value
Màster Oficial - Ciència i Tecnologia Quàntiques / Quantum Science and Technology

# metadata.dc.title[].value
Quantum function fitting and classification beyond the single-qubit model