  Gradient Ascent Pulse Engineering (GRAPE) is a popular technique in quantum
optimal control, and can be combined with automatic differentiation (AD) to
facilitate on-the-fly evaluation of cost-function gradients. We illustrate that
the convenience of AD comes at a significant memory cost due to the cumulative
storage of a large number of states and propagators. For quantum systems of
increasing Hilbert space size, this imposes a significant bottleneck. We
revisit the strategy of hard-coding gradients in a scheme that fully avoids
propagator storage and significantly reduces memory requirements. Separately,
we present improvements to numerical state propagation to enhance runtime
performance. We benchmark runtime and memory usage and compare this approach to
AD-based implementations, with a focus on pushing towards larger Hilbert space
sizes. The results confirm that the AD-free approach facilitates the
application of optimal control for large quantum systems which would otherwise
be difficult to tackle.
