论文标题
记忆有效的可区分编程，用于量子的最佳控制离散晶格
Memory-Efficient Differentiable Programming for Quantum Optimal Control of Discrete Lattices
论文作者
论文摘要
量子最佳控制问题通常是通过基于梯度的算法（例如葡萄）来解决的，葡萄的葡萄构成了指数增长，量子数量越来越多，并且记忆需求的线性增长随着时间步长的增加而遭受。使用QOC进行离散晶格表明，这些记忆要求是模拟大型型号或长时间跨度的障碍。我们采用一种非标准的区分编程方法，以合理的重新计算为代价大大降低了内存要求。该方法利用了单位矩阵的可逆性，以扭转后传出过程中的计算。我们利用在实现这种方法的可区分编程框架JAX中编写的QOC软件，并证明了其对晶格仪理论的有效性。
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.
