Effect of Different Encodings and Distance Functions on Quantum Instance-Based Classifiers

In the last years, we have witnessed the increasing usage of machine learning technologies. In parallel, we have observed the raise of quantum computing, a paradigm for computing making use of quantum theory. Quantum computing can empower machine learning with theoretical properties allowing to overcome the limitations of classical computing. The translation of classical algorithms into their quantum counter-part is not trivial and hides many difficulties. We illustrate and implement alternatives for the quantum nearest neighbor classifier focusing on the challenges related to data preparation and their effect on the performance. We show that, with certain data preparation strategies, quantum algorithms are comparable with the classic version, yet allowing for a theoretical reduction of the complexity for distances calculation.