论文标题
使用量子机学习从黑洞中检索信息
Retrieving information from a black hole using quantum machine learning
论文作者
论文摘要
在开创性的论文[JHEP09（2007）120]中，海顿和普雷基尔表明，假设猎犬对发射的鹰辐射具有完美的控制，并且可以从黑洞中检索信息，并且可以完全控制黑洞的内部动力学。在本文中，我们表明，对于$ t $掺杂的克利福德黑洞 - 也就是说，由随机的clifford电路建模，掺杂了$ t $ t $ nont-clifford资源 - 可以通过$ \ exp（-αT）$使用Quantum Machine学习，同时仅使用Quantim Machine学习，同时仅使用访问量的订单订购，就可以通过fidelity缩放来学习信息检索解码器。我们表明，可学习性和非可学习性之间的交叉是由黑洞中存在的非稳定器量驱动的，并绘制了一种不同的量子复杂性方法。
In a seminal paper[JHEP09(2007)120], Hayden and Preskill showed that information can be retrieved from a black hole that is sufficiently scrambling, assuming that the retriever has perfect control of the emitted Hawking radiation and perfect knowledge of the internal dynamics of the black hole. In this paper, we show that for $t-$doped Clifford black holes - that is, black holes modeled by random Clifford circuits doped with an amount $t$ of non-Clifford resources - an information retrieval decoder can be learned with fidelity scaling as $\exp(-αt)$ using quantum machine learning while having access only to out-of-time-order correlation functions. We show that the crossover between learnability and non-learnability is driven by the amount of non-stabilizerness present in the black hole and sketch a different approach to quantum complexity.
