Modular quantum extreme reservoir computing

Quantum reservoir computing employs fixed quantum dynamics as a feature map for machine learning. Integrating multiple quantum reservoirs, however, raises a key question: how few inter-module connections are sufficient to match the performance of a single reservoir? To address this, we explicitly separate intra-module dynamics from inter-module couplings and systematically examine different connectivity schemes. We find that even a small number of well-placed connections between two modules can match single-reservoir accuracy, with simple one-to-one connections proving highly effective. Performance generally improves with increasing inter-module entanglement, and these correlations persist for both $ZZ$-coupled and random modular reservoirs. Extensions to three modules and evaluations across multiple datasets (MNIST, Fashion-MNIST, CIFAR-10) suggest that the modular architecture can be applied to diverse reservoir types and image-classification datasets. These results motivate modular quantum reservoir designs that align naturally with realistic hardware, such as two-dimensional quantum-chip layouts or networks of small integrated quantum systems.