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.
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.
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