Persistence of superextensive scaling on quantum hardware

Determine whether the superextensive performance scaling demonstrated for driven, disordered, interacting quantum many-body reservoirs persists when implemented on physical quantum hardware.

Background

The paper numerically studies two-dimensional disordered transverse-field Ising networks as quantum reservoirs and reports that forecasting precision grows superextensively with the number of spins at the onset of information scrambling. These results are obtained through noiseless numerical simulations, with system sizes reaching up to approximately 20 spins for the principal forecasting benchmarks.

The authors note that practical hardware introduces effects not fully captured by the simulations, including finite measurement-shot budgets, device noise, state-preparation and readout errors, and implementation-specific resource costs. Establishing whether the observed scaling survives under these experimental conditions is therefore necessary to validate its relevance for scalable quantum reservoir computing.