Dataflow-Based Optimization for Quantum Intermediate Representation Programs

TL;DR

This paper introduces QDFO, a dataflow-based optimization method for Microsoft QIR that reduces redundant quantum operations, enhancing the efficiency of quantum programs through preprocessing and targeted optimization.

Contribution

QDFO is a novel dataflow-based approach that preprocesses and optimizes QIR code to improve quantum program efficiency and facilitate further LLVM optimizations.

Findings

Reduces redundant quantum operations in QIR code

Preprocessing enhances LLVM optimizer effectiveness

Effective on real-world quantum code

Abstract

This paper proposes QDFO, a dataflow-based optimization approach to Microsoft QIR. QDFO consists of two main functions: one is to preprocess the QIR code so that the LLVM optimizer can capture more optimization opportunities, and the other is to optimize the QIR code so that duplicate loading and constructing of qubits and qubit arrays can be avoided. We evaluated our work on the IBM Challenge Dataset, the results show that our method effectively reduces redundant operations in the QIR code. We also completed a preliminary implementation of QDFO and conducted a case study on the real-world code. Our observational study indicates that the LLVM optimizer can further optimize the QIR code preprocessed by our algorithm. Both the experiments and the case study demonstrate the effectiveness of our approach.