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Dissipation as a resource for Quantum Reservoir Computing
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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.
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Cited by 4 Pith papers
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Energetic Cost of Temporal Information Processing in Quantum Reservoirs
In weakly interacting quantum reservoirs, the average switching work per input is controlled by the local spin response, while interactions mediate the task-dependent trade-off between memory and nonlinearity.
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Robust quantum reservoir computers for forecasting chaotic dynamics: generalized synchronization and stability
Recurrence-free quantum reservoir computers have a constant, contractive Jacobian, which guarantees the echo state property and enables accurate inference of Lyapunov spectra and attractor dimensions.
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Connection between memory performance and optical absorption in quantum reservoir computing
In a dissipative qubit network used as a quantum reservoir, the dissipation strength that maximizes short-term memory also maximizes resonant optical absorption.
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Expressivity of Quantum Reservoir Computers
A quantum reservoir computer's expressivity is limited by its input encoding (a Fourier series of degree r, giving 2r+1 independent functions), not by its Hilbert space size.
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