  The emerging technology of quantum reservoir computing (QRC) stands out in
the noisy-intermediate scale quantum era (NISQ) for its exceptional efficiency
and adaptability. By harnessing the power of quantum computing, it holds a
great potential to untangle complex economic markets, as demonstrated here in
an application to food price crisis prediction - a critical effort in combating
food waste and establishing sustainable food chains. Nevertheless, a pivotal
consideration for its success is the optimal design of the quantum reservoirs,
ensuring both high performance and compatibility with current devices. In this
paper, we provide an efficient criterion for that purpose, based on the
complexity of the reservoirs. Our results emphasize the crucial role of optimal
design in the algorithm performance, especially in the absence of external
regressor variables, showcasing the potential for novel insights and
transformative applications in the field of time series prediction using
quantum computing.
