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    Unlocking the high dimensional’ potential: Comparative analysis of qubits and qutrits in variational quantum neural networks
    (ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2025) Acar, Erdi; Yılmaz, İhsan
    Quantum machine learning is a promising research area with great potential. In particular, Variational quantum neural networks (VQNN) have shown high performance in many applications. However, while qubits, which are 2-level quantum systems, are the standard building blocks of quantum computing, the development of qudits, i.e. d-level quantum systems, has opened up new opportunities in VQNNs thanks to many properties such as robustness to noise and more quantum information processing with fewer quantum resources. In this study, we present a comparative analysis of qubits and qutrits (3-level quantum systems) systems performance in VQNNs while also exploring the effect of encoding strategies and entanglement on classifier performance. Our findings contribute to a better understanding the benefits and limitations of using qutrits in VQNN and pave the way for future developments in this field.