Hybrid quantum learning with data re-uploading on a small-scale superconducting quantum simulator

Supervised quantum learning is an emergent multidisciplinary domain bridging between variational quantum algorithms and classical machine learning. Here, we study experimentally a hybrid classifier model accelerated by a quantum simulator - a linear array of four superconducting transmon artificial atoms - trained to solve multilabel classification and image recognition problems. We train a quantum circuit on simple binary and multi-label tasks, achieving classification accuracy around 95 with accuracy around 90 we analyze the inference time in experimental conditions and compare the performance of the studied quantum model with known classical solutions.