Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing
Year: 2021
Type: article
Abstract: Quantum reservoir computing aims at harnessing the rich dynamics of quantum systems for machine-learning purposes. It can be used for online time series processing while having a remarkably low training cost. Here, we establish the potential of continuous-variable Gaussian states of linear dynamical systems for quantum reservoir computing. We prove that Gaussian resources are enough for universal reservoir computing. We find that encoding the input into Gaussian states is both a source and a means to tune the nonlinearity of the overall input-output map. We further show that the full potential of the proposed model can be reached by encoding to quantum fluctuations, such as squeezed vacuum, instead of classical fields or thermal fluctuations. Our results introduce a research paradigm for reservoir computing harnessing quantum systems and engineered Gaussian quantum states.
Source: Communications Physics
Institutions: Consejo Superior de Investigaciones Científicas, Institute for Cross-Disciplinary Physics and Complex Systems, Universitat de les Illes Balears, Centre National de la Recherche Scientifique, Collège de France, Université Paris Sciences et Lettres, Laboratoire Kastler Brossel, École Normale Supérieure - PSL, Sorbonne Université
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,290
Awards: G113450782, G1175115578, G1855990336, G2262748287, G2309174360, G2373305540, G2660406951, G3007729222, G300979063, G3480869486, G3518538256, G3690828678, G425668148, COntinuous variables Quantum COmplex Networks, G430777504, G5080475149, G5421974903, G7084143925, G8255908834, G8684520763, G916329020, G99296377
OpenAlex ID: W3136756438