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Polyadic Quantum Classifier
William Cappelletti, Rebecca Erbanni, J. Keller·July 28, 2020·DOI: 10.1109/QCE49297.2020.00013
Computer SciencePhysics
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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.