Dissipation as a resource for Quantum Reservoir Computing

• DOI: 10.22331/q-2024-03-20-1291
• Publication Date: 2024-03-20T16:14:25Z
• Authors: Antonio Sannia; Rodrigo Martínez-...; Miguel C. Soriano; Gian Luca Giorgi; Roberta Zambrini
• Keywords: Reservoir computing

Dissipation induced by interactions with an external environment typically hinders the performance of quantum computation, but in some cases can be turned out as a useful resource. We show potential enhancement dissipation field reservoir computing introducing tunable local losses spin network models. Our approach based on continuous is able not only to reproduce dynamics previous proposals computing, discontinuous erasing maps also enhance their performance. Control damping rates shown boost popular machine learning temporal tasks capability linearly and non-linearly process input history forecast chaotic series. Finally, we formally prove that, under non-restrictive conditions, our dissipative models form universal class for computing. It means that considering approach, it possible approximate any fading memory map arbitrary precision.