Optimal quantum reservoir learning in proximity to universality
Moein N. Ivaki, Matias Karjula, Tapio Ala-Nissilä · Physical Review A · 2026
The study of the boundary between classically simulable and computationally complex quantum dynamics is fundamental to understanding which physical resources may enable enhanced information-processing capabilities. We investigate this within the framework of quantum reservoir computing by introducing a tunable N -qubit random circuit model, where a fraction p of Clifford gates are probabilistically substituted with nonstabilizing conditional- T ̂ gates. We establish a direct correspondence between the reservoir's performance on temporal processing tasks and its entanglement-spectrum statistics and long-range nonstabilizer resource content. To assess scalability, we study the scaling of the antiflatness of states in the large- N limit at a fixed circuit depth ratio d / N ∼ O ( 1 ) . This is taken as a witness to the concentration of measures, a known impediment to learning in thermalizing systems. We demonstrate that the learnability and scalability of the reservoir can be continuously controlled by the parameter p , allowing us to navigate from classically tractable to maximally expressive quantum dynamics. These architecture-agnostic results provide a general strategy for designing tunable and expressive quantum reservoirs, highlighting how certain nonclassical properties control average-case intrinsic learnability and functionality.