论文标题
具有深神经网络的混合量子状态的正定参数化
Positive-definite parametrization of mixed quantum states with deep neural networks
论文作者
论文摘要
我们介绍了革兰氏 - 哈达马德密度运算符（GHDO），这是一种新的深神经网络结构，可以用多项式资源编码指数级的积极的半定义算子。然后，我们展示如何在GHDO中嵌入自回归结构，以直接对概率分布进行采样。当表示与环境相互作用的系统的混合量子状态时，这些属性尤其重要。最后，我们通过模拟耗散横向场模型的稳态来对此结构进行基准测试。估计当地可观察物和Rényi熵，我们对先前最新的变化方法显示出显着改善。
We introduce the Gram-Hadamard Density Operator (GHDO), a new deep neural-network architecture that can encode positive semi-definite density operators of exponential rank with polynomial resources. We then show how to embed an autoregressive structure in the GHDO to allow direct sampling of the probability distribution. These properties are especially important when representing and variationally optimizing the mixed quantum state of a system interacting with an environment. Finally, we benchmark this architecture by simulating the steady state of the dissipative transverse-field Ising model. Estimating local observables and the Rényi entropy, we show significant improvements over previous state-of-the-art variational approaches.
