Scalable Pattern Matching in Computation Graphs

Abstract: Graph rewriting is a popular tool for the optimisation and modification of graph expressions in domains such as compilers, machine learning and quantum computing. The underlying data structures are often port graphs - graphs with labels at edge endpoints. A pre-requisite for graph rewriting is the ability to find graph patterns. We propose a new solution to pattern matching in port graphs. Its novelty lies in the use of a pre-computed data structure that makes the pattern matching runtime complexity independent of the number of patterns. This offers a significant advantage over existing solutions for use cases with large sets of small patterns. Our approach is particularly well-suited for quantum superoptimisation. We provide an implementation and benchmarks showing that our algorithm offers a 20x speedup over current implementations on a dataset of 10000 real world patterns describing quantum circuits.