This paper demonstrates 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. To validate our work, we completed a preliminary implementation of QDFO and conducted a case study on the real-world QIR code. Our observational study indicates that the LLVM optimizer can further optimize the QIR code preprocessed by our algorithm. Additionally, our dataflow-based optimization algorithm effectively reduces redundant operations in the QIR code, demonstrating the effectiveness of our approach. Furthermore, we also observed that after optimization by our algorithm, the readability of the QIR code has significantly improved, which will facilitate further study by programmers on the QIR code.