Optimizing ZX-Diagrams with Deep Reinforcement Learning

arXiv:2311.18588v2 Announce Type: replace-cross
Abstract: 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 …

abstract applications arxiv cs.lg diagrams fundamental language network optimization processes quant-ph quantum quantum mechanics reinforcement reinforcement learning rules set simulation tensor them transformation type utility