标题： 从单一基态中准确学习等变量子系统 显示英文标题

标题： Accurate Learning of Equivariant Quantum Systems from a Single Ground State

摘要： 在系统参数上预测性质是量子物理中的一个重要任务，其应用范围从分子动力学到变分量子算法。 最近，针对间隙相中的基态，开发了可证明高效的算法来解决这个任务。 在这里，我们通过展示如何从一个基态样本中学习具有周期性边界条件系统的所有基态的性质，显著提高了这些算法的效率。 我们证明了预测误差在热力学极限下趋于零，并通过数值验证了结果。 显示英文摘要

摘要： Predicting properties across system parameters is an important task in quantum physics, with applications ranging from molecular dynamics to variational quantum algorithms. Recently, provably efficient algorithms to solve this task for ground states within a gapped phase were developed. Here we dramatically improve the efficiency of these algorithms by showing how to learn properties of all ground states for systems with periodic boundary conditions from a single ground state sample. We prove that the prediction error tends to zero in the thermodynamic limit and numerically verify the results.