  Quantum computing and neural networks show great promise for the future of
information processing. In this paper we study a quantum reservoir computer
(QRC), a framework harnessing quantum dynamics and designed for fast and
efficient solving of temporal machine learning tasks such as speech
recognition, time series prediction and natural language processing.
Specifically, we study memory capacity and accuracy of a quantum reservoir
computer based on the fully connected transverse field Ising model by
investigating different forms of inter-spin interactions and computing
timescales. We show that variation in inter-spin interactions leads to a better
memory capacity in general, by engineering the type of interactions the
capacity can be greatly enhanced and there exists an optimal timescale at which
the capacity is maximized. To connect computational capabilities to physical
properties of the underlaying system, we also study the out-of-time-ordered
correlator and find that its faster decay implies a more accurate memory.
Furthermore, as an example application on real world data, we use QRC to
predict stock values.
