论文标题
在横向字段中有效推断
Efficient inference in the transverse field Ising model
论文作者
论文摘要
在本文中，我们引入了一种近似方法，以求解横向场ISING模型的量子腔方程。该方法依赖于假想时间轨迹（路径）的确切空腔分布的射影近似。在类似算法的上下文中，一个关键特征是分布的经典和量子部分的明确分离。数值模拟显示出与腔方程的采样溶液相比，汉密尔顿（如果可能的话）和文献中的其他近似推理方法的精确溶液相比。该新算法的计算复杂性与基础晶格的连通性线性缩放，从而使高度连接网络的研究能够进行研究，因为这些网络经常在量子机器学习问题中遇到。
In this paper we introduce an approximate method to solve the quantum cavity equations for transverse field Ising models. The method relies on a projective approximation of the exact cavity distributions of imaginary time trajectories (paths). A key feature, novel in the context of similar algorithms, is the explicit separation of the classical and quantum parts of the distributions. Numerical simulations show accurate results in comparison with the sampled solution of the cavity equations, the exact diagonalization of the Hamiltonian (when possible) and other approximate inference methods in the literature. The computational complexity of this new algorithm scales linearly with the connectivity of the underlying lattice, enabling the study of highly connected networks, as the ones often encountered in quantum machine learning problems.
