  Quantum reservoir computing provides a framework for exploiting the natural
dynamics of quantum systems as a computational resource. It can implement
real-time signal processing and solve temporal machine learning problems in
general, which requires memory and nonlinear mapping of the recent input stream
using the quantum dynamics in computational supremacy region, where the
classical simulation of the system is intractable. A nuclear magnetic resonance
spin-ensemble system is one of the realistic candidates for such physical
implementations, which is currently available in laboratories. In this paper,
considering these realistic experimental constraints for implementing the
framework, we introduce a scheme, which we call a spatial multiplexing
technique, to effectively boost the computational power of the platform. This
technique exploits disjoint dynamics, which originate from multiple different
quantum systems driven by common input streams in parallel. Accordingly, unlike
designing a single large quantum system to increase the number of qubits for
computational nodes, it is possible to prepare a huge number of qubits from
multiple but small quantum systems, which are operationally easy to handle in
laboratory experiments. We numerically demonstrate the effectiveness of the
technique using several benchmark tasks and quantitatively investigate its
specifications, range of validity, and limitations in detail.
