Quantum Ensemble for Classification

A powerful way to improve performance in machine learning is to construct an ensemble that combines the predictions of multiple models. Ensemble methods are often much more accurate and lower variance than the individual classifiers that make them up but have high requirements in terms of memory and computational time. In fact, a large number of alternative algorithms is usually adopted, each requiring to query all available data. We propose a new quantum algorithm that exploits quantum superposition, entanglement and interference to build an ensemble of classification models. Thanks to the generation of the several quantum trajectories in superposition, we obtain $B$ transformations of the quantum state which encodes the training set in only $log\left(B\right)$ operations. This implies an exponential speed-up in the size of the ensemble with respect to classical methods. Furthermore, when considering the overall cost of the algorithm, we show that the training of a single weak classifier impacts additively rather than multiplicatively, as it usually happens. We also present small-scale experiments, defining a quantum version of the cosine classifier and using the IBM qiskit environment to show how the algorithm works.