From observations to complexity of quantum states via unsupervised learning
From observations to complexity of quantum states via unsupervised learning
复制标题
通过无监督学习从观察到量子态的复杂性
作者：
M. Schmitt;Z. Lenarčič
巨大的复杂性是通用量子态的一个令人生畏的特性，这对理论处理构成了重大挑战，特别是在非平衡设置中。因此，识别局部不太复杂的状态，从而用（经典）有效理论描述是至关重要的。我们使用自动编码器神经网络的无监督学习，通过确定重现局部观测所需的最小参数数量来检测时间演化状态的局部复杂性。后者可用作热化的探针，分配在开放设置的密度矩阵的局部复杂性和重建底层的哈密顿算子。我们的方法是从（噪声）量子模拟器获得的数据的理想诊断工具，因为它只需要实际访问的本地观测。
The vast complexity is a daunting property of generic quantum states that poses a significant challenge for theoretical treatment, especially in non-equilibrium setups. Therefore, it is vital to recognize states which are locally less complex and thus describable with (classical) effective theories. We use unsupervised learning with autoencoder neural networks to detect the local complexity of time-evolved states by determining the minimal number of parameters needed to reproduce local observations. The latter can be used as a probe of thermalization, to assign the local complexity of density matrices in open setups and for the reconstruction of underlying Hamiltonian operators. Our approach is an ideal diagnostics tool for data obtained from (noisy) quantum simulators because it requires only practically accessible local observations.
影响因子：
6.4
作者：
Preskill, John
通讯作者：
Preskill, John
影响因子：
6.4
作者：
Noh, Kyungjoo;Jiang, Liang;Fefferman, Bill
通讯作者：
Fefferman, Bill
影响因子：
19.6
作者：
Haferkamp, Jonas;Faist, Philippe;Halpern, Nicole Yunger
通讯作者：
Halpern, Nicole Yunger