Ensemble reservoir computing's prediction uncertainty serves as a data-driven indicator of local dynamical properties in spatiotemporal chaotic systems, matching known measures like Lyapunov spectra.
Reservoir computing approaches to recurrent neural network training.Computer science review, 3(3):127–149
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Hamiltonian-encoded quantum reservoir computing achieves ~98% MNIST accuracy with 5-6 qubits on both analog and digital platforms, with dissipation constructively suppressing scrambling at long times.
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Data-driven characterization of spatiotemporal chaos using ensemble reservoir computing
Ensemble reservoir computing's prediction uncertainty serves as a data-driven indicator of local dynamical properties in spatiotemporal chaotic systems, matching known measures like Lyapunov spectra.
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Robust Quantum Learning through Hamiltonian Reservoir Computing
Hamiltonian-encoded quantum reservoir computing achieves ~98% MNIST accuracy with 5-6 qubits on both analog and digital platforms, with dissipation constructively suppressing scrambling at long times.