Pattern Tree: Enhancing Efficiency in Quantum Circuit Optimization Based on Pattern-matching

TL;DR

This paper introduces a pattern tree framework to improve quantum circuit optimization efficiency, significantly reducing compilation time and enhancing performance on NISQ devices through optimized pattern matching.

Contribution

The study presents a novel pattern tree structure for organizing transformation rules, reducing redundancy and increasing pattern matching efficiency in quantum circuit optimization.

Findings

Reduces pattern matching execution time by 20% on benchmarks.

Potential to optimize compilation time by up to 90%.

Demonstrates practical effectiveness of the pattern tree approach.

Abstract

Quantum circuit optimization is essential for improving the performance of quantum algorithms, particularly on Noisy Intermediate-Scale Quantum (NISQ) devices with limited qubit connectivity and high error rates. Pattern matching has proven to be an effective technique for identifying and optimizing subcircuits by replacing them with functionally equivalent, efficient versions, including reducing circuit depth and facilitating platform portability. However, existing approaches face challenges in handling large-scale circuits and numerous transformation rules, often leading to redundant matches and increased compilation time. In this study, we propose a novel framework for quantum circuit optimization based on pattern matching to enhance its efficiency. Observing redundancy in applying existing transformation rules, our method employs a pattern tree structure to organize these rules, reducing redundant operations during the execution of the pattern-matching algorithm and improving matching efficiency. We design and implement a compilation framework to demonstrate the practicality of the pattern tree approach. Experimental results show that pattern-tree-based pattern matching can reduce execution time by an average of 20% on a well-accepted benchmark set. Furthermore, we analyze how to build a pattern tree to maximize the optimization of compilation time. The evaluation results demonstrate that our approach has the potential to optimize compilation time by 90%.