Optimizing ZX-Diagrams with Deep Reinforcement Learning
This work addresses the open problem of optimizing ZX-diagrams for applications in quantum mechanics and computing, offering a novel approach that could enhance efficiency in quantum circuit optimization and tensor network simulation.
The paper tackles the problem of finding optimal sequences of transformation rules for ZX-diagrams, which are used in quantum processes, by applying deep reinforcement learning with graph neural networks, resulting in an agent that significantly outperforms existing methods like greedy strategies and hand-crafted algorithms.
ZX-diagrams are a powerful graphical language for the description of quantum processes with applications in fundamental quantum mechanics, quantum circuit optimization, tensor network simulation, and many more. The utility of ZX-diagrams relies on a set of local transformation rules that can be applied to them without changing the underlying quantum process they describe. These rules can be exploited to optimize the structure of ZX-diagrams for a range of applications. However, finding an optimal sequence of transformation rules is generally an open problem. In this work, we bring together ZX-diagrams with reinforcement learning, a machine learning technique designed to discover an optimal sequence of actions in a decision-making problem and show that a trained reinforcement learning agent can significantly outperform other optimization techniques like a greedy strategy, simulated annealing, and state-of-the-art hand-crafted algorithms. The use of graph neural networks to encode the policy of the agent enables generalization to diagrams much bigger than seen during the training phase.