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Memory-Efficient Differentiable Programming for Quantum Optimal Control of Discrete Lattices
DescriptionQuantum 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 lattice 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 have created a QOC software in the differentiable programming framework JAX that implements this approach, and demonstrate its effectiveness for lattice gauge theory.