论文标题
量子多体状态的硬件有效学习
Hardware-efficient learning of quantum many-body states
论文作者
论文摘要
高度纠缠的多粒子系统的有效表征是量子科学中的重要挑战。最近的事态发展表明,数量适中的随机测量足以学习量子多体系统的许多特性。但是,实施此类测量需要完全控制各个粒子,这在许多实验平台中都无法使用。在这项工作中,我们介绍了在对单个粒子进行任何控制的系统中学习量子多体状态的严格,有效的算法,包括当每个粒子都受到相同的全局磁场的影响,并且没有其他Ancilla颗粒。我们从数值上证明了算法在估算U(1)晶格计理论中估计能量密度的有效性,并使用非常有限的测量能力对拓扑顺序进行分类。
Efficient characterization of highly entangled multi-particle systems is an outstanding challenge in quantum science. Recent developments have shown that a modest number of randomized measurements suffices to learn many properties of a quantum many-body system. However, implementing such measurements requires complete control over individual particles, which is unavailable in many experimental platforms. In this work, we present rigorous and efficient algorithms for learning quantum many-body states in systems with any degree of control over individual particles, including when every particle is subject to the same global field and no additional ancilla particles are available. We numerically demonstrate the effectiveness of our algorithms for estimating energy densities in a U(1) lattice gauge theory and classifying topological order using very limited measurement capabilities.