The dynamical behavior of complex quantum systems can be harnessed for information processing. With this aim, quantum reservoir computing (QRC) with Ising spin networks was recently introduced as a quantum version of classical reservoir computing. In turn, reservoir computing is a neuro-inspired machine learning technique that consists in exploiting dynamical systems to solve nonlinear and temporal tasks. We characterize the performance of the spin-based QRC model with the Information Processing Capacity (IPC), which allows to quantify the computational capabilities of a dynamical system beyond specific tasks. The influence on the IPC of the input injection frequency, time multiplexing, and different measured observables encompassing local spin measurements as well as correlations is addressed. We find conditions for an optimum input driving and provide different alternatives for the choice of the output variables used for the readout. This work establishes a clear picture of the computational capabilities of a quantum network of spins for reservoir computing. Our results pave the way to future research on QRC both from the theoretical and experimental points of view.

可以利用复杂量子系统的动力学行为进行信息处理。为此，最近引入了具有Ising自旋网络的量子储层计算（QRC）作为经典储层计算的量子版本。反过来，油藏计算是一种受神经启发的机器学习技术，它包括利用动力系统来解决非线性和时间任务。我们利用信息处理能力（IPC）来表征基于自旋的QRC模型的性能，该模型可以量化超出特定任务的动态系统的计算能力。解决了输入注入频率，时分多路复用以及包括本地自旋测量以及相关性在内的不同测量可观测值对IPC的影响。我们找到了最佳输入驱动的条件，并为选择用于读数的输出变量提供了不同的选择。这项工作为油藏计算建立了自旋量子网络的计算能力的清晰图景。从理论和实验的角度来看，我们的研究结果为未来QRC研究铺平了道路。