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

TL;DR

This paper demonstrates a hybrid quantum-classical classifier using a small superconducting quantum simulator, achieving high accuracy on classification tasks and analyzing inference performance.

Contribution

It introduces a hybrid quantum classifier with data re-uploading on a superconducting quantum device, showcasing practical implementation and performance analysis.

Findings

Achieved around 95% accuracy on simple classification tasks.

Demonstrated approximately 90% accuracy on handwritten digit recognition.

Analyzed inference time and compared quantum model performance with classical solutions.

Abstract

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%, and a hybrid model with data re-uploading 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.