  We explore the power of reservoir computing with a single oscillator in
learning time series using quantum and classical models. We demonstrate that
this scheme learns the Mackey--Glass (MG) chaotic time series, a solution to a
delay differential equation. Our results suggest that the quantum nonlinear
model is more effective in terms of learning performance compared to a
classical non-linear oscillator. We develop approaches for measuring the
quantumness of the reservoir during the process, proving that Lee-Jeong's
measure of macroscopicity is a non-classicality measure. We note that the
evaluation of the Lee-Jeong measure is computationally more efficient than the
Wigner negativity. Exploring the relationship between quantumness and
performance by examining a broad range of initial states and varying
hyperparameters, we observe that quantumness in some cases improves the
learning performance. However, our investigation reveals that an indiscriminate
increase in quantumness does not consistently lead to improved outcomes,
necessitating caution in its application. We discuss this phenomenon and
attempt to identify conditions under which a high quantumness results in
improved performance.
