Paper
https://www.nature.com/articles/s41467-025-58349-6
Description
Stochastic reservoir computing is a reservoir computing setup where the reservoir dynamics and/or observations are random (e.g., hardware with probabilistic switching or measurement noise). Instead of training a readout on a single sampled reservoir trajectory, you train on distributional features of the reservoir state at each timestep, typically estimated from repeated “shots”(multiple runs of the same input).
Core ideas:
- The reservoir can be modeled as a controlled stochastic dynamical system (often a Markov process driven by the input).
- For a given input sequence, the reservoir produces a distribution over states, not a deterministic state.
- The readout is trained on probability/expectation features (e.g., per-node marginals or averaged states across shots), making performance depend on shot noise (more shots → better estimates).
- This can provide a very rich effective feature space, especially in physical/quantum/p-bit style reservoirs where stochasticity is intrinsic.
Paper
https://www.nature.com/articles/s41467-025-58349-6
Description
Stochastic reservoir computing is a reservoir computing setup where the reservoir dynamics and/or observations are random (e.g., hardware with probabilistic switching or measurement noise). Instead of training a readout on a single sampled reservoir trajectory, you train on distributional features of the reservoir state at each timestep, typically estimated from repeated “shots”(multiple runs of the same input).
Core ideas: