<ul><li>Reservoir computing is a form of machine learning that utilizes nonlinear dynamical systems to perform complex tasks in a cost-effective manner.</li><li>Recent advancements in reservoir computing include the use of inherently stochastic reservoirs, such as quantum reservoir computing.</li><li>This paper investigates the universality of stochastic reservoir computers by using a stochastic system for reservoir computing.</li><li>The study proves that stochastic reservoir computers are universal approximating classes and demonstrates improved performance compared to deterministic reservoir computers in certain cases.</li></ul>

Stochastic Reservoir Computers

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