Quantum variational distance-based centroid classifier

Abstract

Quantum machine learning is a rapidly growing field with the potential to surpass classical methods and offer novel solutions to learning problems. Supervised classification, a prominent pattern recognition task, has attracted significant attention for improving existing methods using quantum techniques. Although previous quantum algorithms for distance-based classification have shown promise, they are not compatible with current Noisy Intermediate-Scale Quantum (NISQ) devices and cannot handle multiclass problems. To address these limitations, we present the quantum Variational Distance-based Centroid Classifier (qVDCC), a hybrid approach that combines the Quantum One-Class Classifier (QOCC) and Parameterized Quantum Circuits (PQCs). This approach greatly improves the feasibility of multiclass classification on NISQ devices, as it eliminates the need for a label qubit and uses a single-qubit measurement, regardless of data size. Our simulations on seven benchmark datasets using both noisy and noise-free simulations show that qVDCC is competitive with the k-nearest neighbors algorithm, while providing exponential data compression and efficient quantum data processing.