Polyadic Quantum Classifier

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.