论文标题
使用增强学习优化张量网络收缩
Optimizing Tensor Network Contraction Using Reinforcement Learning
论文作者
论文摘要
量子计算（QC）彻底改变了计算，但目前仍然有限。为了开发和测试量子算法，通常在古典计算机上模拟量子电路。模拟复杂的量子电路需要计算大型张量网络的收缩。收缩的顺序（路径）可能会对计算成本产生巨大影响，但是找到有效的顺序是一个具有挑战性的组合优化问题。 我们提出了一种加强学习（RL）方法与图形神经网络（GNN）相结合，以解决收缩排序问题。由于巨大的搜索空间，重尾奖励分配以及具有挑战性的信贷分配，问题极具挑战性。我们展示了使用GNN作为基本政策结构的精心实施的RL-Antent如何解决这些挑战，并在三种电路中的最先进技术方面获得了重大改进，包括当代QC中使用的最大规模网络。
Quantum Computing (QC) stands to revolutionize computing, but is currently still limited. To develop and test quantum algorithms today, quantum circuits are often simulated on classical computers. Simulating a complex quantum circuit requires computing the contraction of a large network of tensors. The order (path) of contraction can have a drastic effect on the computing cost, but finding an efficient order is a challenging combinatorial optimization problem. We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem. The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment. We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges and obtain significant improvements over state-of-the-art techniques in three varieties of circuits, including the largest scale networks used in contemporary QC.
