论文标题
针对人工神经网络的采样不对称开放量子系统
Sampling asymmetric open quantum systems for artificial neural networks
论文作者
论文摘要
虽然基于受限的玻尔兹曼机器架构和大都市采样方法的建立神经网络方法非常适合对称开放量子系统，但它们导致不符合转换不变性的设置的可扩展性和系统错误，而不受训练参数（例如样本大小）的对称性。为了克服这一代表性限制，我们提出了一种混合抽样策略，该策略明确考虑了不对称的特性，实现了快速收敛时间和不对称开放系统的高可扩展性，强调了人工神经网络的普遍适用性。
While established neural network approaches based on restricted Boltzmann machine architectures and Metropolis sampling methods are well suited for symmetric open quantum systems, they result in poor scalability and systematic errors for setups without symmetries of translational invariance, independent of training parameters such as the sample size. To overcome this representational limit, we present a hybrid sampling strategy which takes asymmetric properties explicitly into account, achieving fast convergence times and high scalability for asymmetric open systems, underlining the universal applicability of artificial neural networks.
