Optimizing Tensor Network Contraction Using Reinforcement Learning

TL;DR: This work proposes a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem and shows how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges and obtain significant improve-ments over state-of-the-art techniques.

Abstract: 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 improve-ments over state-of-the-art techniques in three varieties of circuits, including the largest scale networks used in contemporary QC.