论文标题
用于量子最佳控制的迭代梯度上升脉冲工程算法
Iterative Gradient Ascent Pulse Engineering algorithm for quantum optimal control
论文作者
论文摘要
梯度上升脉冲工程算法（葡萄）是解决量子最佳控制问题的典型方法。但是，它在计算量子系统的时间演变中具有指数级的资源，而量子系统的数量越来越多，这是其在大问题系统中应用的障碍。为了减轻此问题，我们提出了一种迭代葡萄算法（IGRAPE），以准备所需的量子状态，其中通过散布操作将大规模的，资源消耗的优化问题分解为一组低维度优化的副标。因此，这些子问题可以与较少的计算资源并行解决。对于物理平台，例如核磁共振（NMR）和超导量子系统，我们表明，当在12 Q Q Qubits的系统中准备所需的量子状态时，Igrape可以在葡萄中提供多达13倍的速度。使用四量NMR系统，我们还通过实验验证了Igrape算法的可行性。
Gradient ascent pulse engineering algorithm (GRAPE) is a typical method to solve quantum optimal control problems. However, it suffers from an exponential resource in computing the time evolution of quantum systems with the increasing number of qubits, which is a barrier for its application in large-qubit systems. To mitigate this issue, we propose an iterative GRAPE algorithm (iGRAPE) for preparing a desired quantum state, where the large-scale, resource-consuming optimization problem is decomposed into a set of lower-dimensional optimization subproblems by disentanglement operations. Consequently these subproblems can be solved in parallel with less computing resources. For physical platforms such as nuclear magnetic resonance (NMR) and superconducting quantum systems, we show that iGRAPE can provide up to 13-fold speedup over GRAPE when preparing desired quantum states in systems within 12 qubits. Using a four-qubit NMR system, we also experimentally verify the feasibility of the iGRAPE algorithm.
