Dissipation as a resource for Quantum Reservoir Computing

Antonio Sannia, Rodrigo Martínez-Peña, Miguel C. Soriano, Gian Luca Giorgi, and Roberta Zambrini

Institute for Cross-Disciplinary Physics and Complex Systems (IFISC) UIB-CSIC, Campus Universitat Illes Balears, 07122, Palma de Mallorca, Spain.

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

In the domain of quantum computing, the conventional view posits that interactions with external environments are detrimental to computational performance. However, our research unveils a paradigm shift, demonstrating the advantageous role of dissipation in in quantum machine learning. Specifically, within the burgeoning field of quantum reservoir computing, we show the benefits of introducing engineered dissipation into spin networks models. Through comprehensive benchmarking tests encompassing tasks spanning linear and nonlinear memory, as well as forecasting capacity, we found a pronounced enhancement in computational efficacy. Moreover, we establish, through formal proof under non-restrictive conditions, the universality of our dissipative models for reservoir computing.

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[4] Johannes Nokkala, Gian Luca Giorgi, and Roberta Zambrini, "Retrieving past quantum features with deep hybrid classical-quantum reservoir computing", arXiv:2401.16961, (2024).

[5] P. Renault, J. Nokkala, G. Roeland, N. Y. Joly, R. Zambrini, S. Maniscalco, J. Piilo, N. Treps, and V. Parigi, "Experimental Optical Simulator of Reconfigurable and Complex Quantum Environment", PRX Quantum 4 4, 040310 (2023).

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