论文标题
高准确性哈密顿学习通过离域量子状态演变
High-accuracy Hamiltonian learning via delocalized quantum state evolutions
论文作者
论文摘要
学习量子多体系统动态的未知哈密顿官是一项艰巨的任务。在本手稿中，我们提出了一种基于对单个时间依赖性状态的重复测量的可能策略。我们证明，对于在汉密尔顿特征巴西人中被离域的州而言，学习过程的准确性是最大化的。这意味着离域是用于哈密顿学习的量子资源，可以利用它来选择学习算法的最佳初始状态。我们研究了针对测量数量的重建的误差缩放，并提供了关于模拟量子系统的学习算法的示例。
Learning the unknown Hamiltonian governing the dynamics of a quantum many-body system is a challenging task. In this manuscript, we propose a possible strategy based on repeated measurements on a single time-dependent state. We prove that the accuracy of the learning process is maximized for states that are delocalized in the Hamiltonian eigenbasis. This implies that delocalization is a quantum resource for Hamiltonian learning, that can be exploited to select optimal initial states for learning algorithms. We investigate the error scaling of our reconstruction with respect to the number of measurements, and we provide examples of our learning algorithm on simulated quantum systems.
