论文标题
减少量子最佳控制的内存要求
Reducing Memory Requirements of Quantum Optimal Control
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。这些记忆要求是模拟大型型号或长时间跨度的障碍。我们创建了一种非标准自动分化技术，可以通过利用单一基质的倒数是其共轭转置来计算葡萄来计算所需的梯度。我们的方法大大减少了葡萄的记忆要求，而耗资合理的重新成员。我们根据JAX中的实现提出基准结果。
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. These memory requirements are a barrier for simulating large models or long time spans. We have created a nonstandard automatic differentiation technique that can compute gradients needed by GRAPE by exploiting the fact that the inverse of a unitary matrix is its conjugate transpose. Our approach significantly reduces the memory requirements for GRAPE, at the cost of a reasonable amount of recomputation. We present benchmark results based on an implementation in JAX.
