A Quantum Reservoir Computing Approach to Image Classification

Recent proposals of Quantum Neural Networks (QNNs) and their implementations in near-term quantum hardware has highlighted the severe limitations imposed by the associated resource requirements. The practical and useful implementation of quantum neural networks to scale has the address the question of the optimal approach to encoding the information to be processed, and the subsequent extraction of the processed information from a large quantum system. In this work, we consider and analyze the efficacy of a reservoir computing approach to address these issues. We propose a superconducting quantum circuit network as a physical reservoir processor and present a unified description of the device operation, from classical information input to computational output via quantum measurement. We model a small experimentally-realizable network, and compare its performance on a pattern recognition task to that of recent QNN approaches [1].

[1] F. Tacchino et. al., Quantum Science and Technology, 5(4) 044010 (2020).