论文标题
变异量子算法的快速梯度估计
Fast gradient estimation for variational quantum algorithms
论文作者
论文摘要
许多用于训练变量量子算法的优化方法基于估计成本函数的梯度。由于量子测量的统计性质，该估计需要许多电路评估，这是整个方法的至关重要的瓶颈。我们提出了一种新的梯度估计方法，以减轻这一测量挑战并减少所需的测量回合。在贝叶斯框架内并基于广义参数偏移规则，我们使用有关电路的先验信息来找到一种估计策略，该策略同时使预期的统计和系统误差最小化。我们证明，这种方法可以显着超过传统的梯度估计方法，从而将所需的测量回合减少到通用QAOA设置的数量级。我们的分析还表明，通过有限差异的估计可以优于参数转移规则，而小规模和中等测量预算的梯度准确性。
Many optimization methods for training variational quantum algorithms are based on estimating gradients of the cost function. Due to the statistical nature of quantum measurements, this estimation requires many circuit evaluations, which is a crucial bottleneck of the whole approach. We propose a new gradient estimation method to mitigate this measurement challenge and reduce the required measurement rounds. Within a Bayesian framework and based on the generalized parameter shift rule, we use prior information about the circuit to find an estimation strategy that minimizes expected statistical and systematic errors simultaneously. We demonstrate that this approach can significantly outperform traditional gradient estimation methods, reducing the required measurement rounds by up to an order of magnitude for a common QAOA setup. Our analysis also shows that an estimation via finite differences can outperform the parameter shift rule in terms of gradient accuracy for small and moderate measurement budgets.
