  Amidst the rapid advancements in experimental technology,
noise-intermediate-scale quantum (NISQ) devices have become increasingly
programmable, offering versatile opportunities to leverage quantum
computational advantage. Here we explore the intricate dynamics of programmable
NISQ devices for quantum reservoir computing. Using a genetic algorithm to
configure the quantum reservoir dynamics, we systematically enhance the
learning performance. Remarkably, a single configured quantum reservoir can
simultaneously learn multiple tasks, including a synthetic oscillatory network
of transcriptional regulators, chaotic motifs in gene regulatory networks, and
the fractional-order Chua's circuit. Our configured quantum reservoir computing
yields highly precise predictions for these learning tasks, outperforming
classical reservoir computing. We also test the configured quantum reservoir
computing in foreign exchange (FX) market applications and demonstrate its
capability to capture the stochastic evolution of the exchange rates with
significantly greater accuracy than classical reservoir computing approaches.
Through comparison with classical reservoir computing, we highlight the unique
role of quantum coherence in the quantum reservoir, which underpins its
exceptional learning performance. Our findings suggest the exciting potential
of configured quantum reservoir computing for exploiting the quantum
computation power of NISQ devices in developing artificial general
intelligence.
