Barycentric programming: Classical realization of programs for universal quantum processors
A programmable quantum processor implements a target unitary using information stored in a classical or quantum program register. For arbitrary unitaries in $\mathrm{SU}(d)$, quantum programs attain the optimal leading-order memory cost, whereas deterministic classical rounding requires twice as much memory for the same diamond-distance error. We show that this gap disappears when classical randomization is allowed. We introduce barycentric programming, which stores the label of a unitary randomly selected from a finite set, with probabilities chosen so that first-order approximation errors cancel in the averaged unitary channel. Consequently, the diamond-distance error scales quadratically with the discretization spacing. For any $ν$-parameter family of unitaries forming a compact, smooth manifold without boundary, this method achieves error $\varepsilon$ using only $(ν/2)\log_2(1/\varepsilon)+O(1)$ classical bits. We also prove a matching lower bound for arbitrary quantum programs, determining the optimal leading-order program size for general unitary families. Thus, classical randomization achieves the optimal scaling $(ν/2+o(1))\log_2(1/\varepsilon)$ without requiring coherent quantum memory.