推荐系统加速量子控制优化
Recommender System Expedited Quantum Control Optimization
摘要：
量子控制优化算法通常用于生成最佳量子门或有效的量子态转移。然而，在设计有效的优化算法时存在两个主要挑战，即克服对局部最优解的敏感性和提高计算速度。前一个挑战可以通过设计混合算法来解决，例如梯度和模拟退火方法的组合。在这里，我们提出并演示了使用机器学习方法，特别是推荐系统（RS），以应对后者的挑战，提高计算效率。我们首先描述如何建立一个评级矩阵，涉及梯度或门等级。然后，我们建立RS可以快速，准确地预测稀疏评级矩阵的元素。使用这种方法，我们加快了基于梯度上升的量子控制优化，即GRAPE，并展示了高达8个量子比特的更快性能。最后，我们描述和实现的混合算法，即SAGRAPE的计算速度的增强。
英文摘要：
Quantum control optimization algorithms are routinely used to generate optimal quantum gates or efficient quantum state transfers. However, there are two main challenges in designing efficient optimization algorithms, namely overcoming the sensitivity to local optima and improving the computational speed. The former challenge can be dealt with by designing hybrid algorithms, such as a combination of gradient and simulated annealing methods. Here, we propose and demonstrate the use of a machine learning method, specifically the recommender system (RS), to deal with the latter challenge of enhancing computational efficiency. We first describe ways to set up a rating matrix involving gradients or gate fidelities. We then establish that RS can rapidly and accurately predict elements of a sparse rating matrix. Using this approach, we expedite a gradient ascent based quantum control optimization, namely GRAPE and demonstrate the faster performance for up to 8 qubits. Finally, we describe and implement the enhancement of the computational speed of a hybrid algorithm, namely SAGRAPE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
