The concurrent rise of artificial intelligence and quantum information poses opportunity for creating interdisciplinary technologies like quantum neural networks. Quantum reservoir processing, introduced here, is a platform for quantum information processing developed on the principle of reservoir computing that is a form of artificial neural network. A quantum reservoir processor can perform qualitative tasks like recognizing quantum states that are entangled as well as quantitative tasks like estimating a non-linear function of an input quantum state (e.g. entropy, purity or logarithmic negativity). In this way experimental schemes that require measurements of multiple observables can be simplified to measurement of one observable on a trained quantum reservoir processor.
@article{arxiv.1811.10335,
title = {Quantum reservoir processing},
author = {Sanjib Ghosh and Andrzej Opala and Michał Matuszewski and Tomasz Paterek and Timothy C. H. Liew},
journal= {arXiv preprint arXiv:1811.10335},
year = {2019}
}