Unified framework for quantum classification

Quantum machine learning is an emerging field that combines machine learning with advances in quantum technologies. Many works have suggested great possibilities of using near-term quantum hardware in supervised learning. Motivated by these developments, we present an embedding-based framework for supervised learning with trainable quantum circuits. We introduce both explicit and implicit approaches. The aim of these approaches is to map data from different classes to separated locations in the Hilbert space via a parametrized quantum circuit. We will show that the implicit approach is a generalization of a recently introduced strategy, so-called quantum metric learning. Furthermore, we discuss an intrinsic connection between the explicit approach and those previously proposed quantum classification models. The implicit and explicit approaches, together, provide a unified framework for quantum classification. The utility of our framework is demonstrated by our noise-free and noisy numerical simulations. Moreover, we have conducted classification testing with both implicit and explicit approaches using several IBM Q devices.