  Recent advancements in quantum hardware have enabled the realization of
high-dimensional quantum states. This work investigates the potential of
qutrits in quantum machine learning, leveraging their larger state space for
enhanced supervised learning tasks. To that end, the Gell-Mann feature map is
introduced which encodes information within an $8$-dimensional Hilbert space.
The study focuses on classification problems, comparing Gell-Mann feature map
with maps generated by established qubit and classical models. We test
different circuit architectures and explore possibilities in optimization
techniques. By shedding light on the capabilities and limitations of
qutrit-based systems, this research aims to advance applications of low-depth
quantum circuits.
