Abstract
The boundary between classically simulable and computationally superior quantum systems is fundamental to identifying true quantum advantage. We investigate this within the framework of quantum reservoir computing by introducing a tunable -qubit random circuit model, where a fraction of Clifford gates are probabilistically substituted with nonstabilizing conditional- 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 anti-flatness of states in the large- limit at a fixed circuit depth ratio . This is taken as a witness to 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 , allowing us to navigate from classically tractable to maximally expressive quantum dynamics. These architecture-agnostic results offer a general strategy for designing powerful and trainable quantum machine learning systems and clarify the physical resources underpinning quantum computational advantage.