论文标题
超时相关器的信息理论硬度
Information-theoretic Hardness of Out-of-time-order Correlators
论文作者
论文摘要
我们确定存在量子多体动力学的属性，如果我们可以访问超级订单相关器（OTOC），则可以有效地学习，但是如果我们只能测量时间顺序的相关器，则需要在系统大小中成倍的操作。这意味着，在某些情况下，任何仅根据时间序的相关器重建OTOC的实验协议都必须呈指数效率。我们的证据利用量子学习理论中的最新技术利用并概括了。在此过程中，我们阐明了时间订购量与超阶实验测量方案的一般定义，可以将其视为自适应量子学习算法的类别。此外，我们的结果为OTOC在量子模拟中的新应用提供了理论基础。
We establish that there are properties of quantum many-body dynamics which are efficiently learnable if we are given access to out-of-time-order correlators (OTOCs), but which require exponentially many operations in the system size if we can only measure time-ordered correlators. This implies that any experimental protocol which reconstructs OTOCs solely from time-ordered correlators must be, in certain cases, exponentially inefficient. Our proofs leverage and generalize recent techniques in quantum learning theory. Along the way, we elucidate a general definition of time-ordered versus out-of-time-order experimental measurement protocols, which can be considered as classes of adaptive quantum learning algorithms. Moreover, our results provide a theoretical foundation for novel applications of OTOCs in quantum simulations.
