  Quantum optimal control problems are typically solved by gradient-based
algorithms such as GRAPE, which suffer from exponential growth in storage with
increasing number of qubits and linear growth in memory requirements with
increasing number of time steps. Employing QOC for discrete lattices reveals
that these memory requirements are a barrier for simulating large models or
long time spans. We employ a nonstandard differentiable programming approach
that significantly reduces the memory requirements at the cost of a reasonable
amount of recomputation. The approach exploits invertibility properties of the
unitary matrices to reverse the computation during back-propagation. We utilize
QOC software written in the differentiable programming framework JAX that
implements this approach, and demonstrate its effectiveness for lattice gauge
theory.
