Hybrid quantum learning with data reuploading 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 using quantum hardware 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 multilabel tasks, achieving classification accuracy around 95%, and a hybrid quantum model with data reuploading with accuracy around 90% when recognizing handwritten decimal digits. Finally, we analyze the inference time in experimental conditions and compare the performance of the studied quantum model with known classical solutions.