Polyadic Quantum Classifier
William Cappelletti, Rebecca Erbanni, Joaqu\'in Keller

TL;DR
This paper presents a supervised quantum machine learning algorithm designed for multi-class classification on NISQ devices, demonstrating promising accuracy on real quantum hardware and various datasets.
Contribution
It introduces a novel parametric quantum circuit approach for multi-class classification tailored for NISQ architectures, validated on IBMq hardware.
Findings
Achieved good accuracy on IBMq 5-qubit quantum computer
Successfully classified Iris dataset and XOR problem extensions
Evaluated performance on binary and quaternary synthetic datasets
Abstract
We introduce here a supervised quantum machine learning algorithm for multi-class classification on NISQ architectures. A parametric quantum circuit is trained to output a specific bit string corresponding to the class of the input datapoint. We train and test it on an IBMq 5-qubit quantum computer and the algorithm shows good accuracy --compared to a classical machine learning model-- for ternary classification of the Iris dataset and an extension of the XOR problem. Furthermore, we evaluate with simulations how the algorithm fares for a binary and a quaternary classification on resp. a known binary dataset and a synthetic dataset.
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