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IEEE International Conference on Quantum Computing and Engineering (QCE)

QIRopt: An Optimization Method for Quantum Intermediate Representation

Junjie Luo, Haoyu Zhang, Jianjun Zhao

Published Aug 30, 2025• 0 citations
Publisher

Abstract

This paper proposes a dataflow-based optimization method called Quantum Intermediate Representation Optimization (QIRopt) within the LLVM infrastructure. QIRopt has two main functions: preprocessing QIR code to expose more optimization opportunities to the LLVM optimizer and optimizing QIR code to avoid redundant loading and construction of qubits and qubit arrays. We evaluated QIRopt on the IBM Challenge Dataset, and the results demonstrate its effectiveness in reducing redundant operations in QIR code. A preliminary implementation of QIRopt and a case study on real-world code further confirm that our approach enhances the LLVM optimizer's ability to optimize preprocessed QIR code.

DOI
10.1109/qce65121.2025.00058

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This paper proposes a dataflow-based optimization method called Quantum Intermediate Representation Optimization (QIRopt) within the LLVM infrastructure. QIRopt has two main functions: preprocessing QIR code to expose more optimization opportunities to the LLVM optimizer and optimizing QIR code to avoid redundant loading and construction of qubits and qubit arrays. We evaluated QIRopt on the IBM Challenge Dataset, and the results demonstrate its effectiveness in reducing redundant operations in QIR code. A preliminary implementation of QIRopt and a case study on real-world code further confirm that our approach enhances the LLVM optimizer's ability to optimize preprocessed QIR code.
Quantum Intermediate Representation (QIR) is an LLVM-based intermediate representation developed by Microsoft for quantum program compilers. QIR is designed to offer a universal solution for quantum program compilers, decoupled from both front-end languages and back-end hardware, thereby eliminating the need for redundant development of intermediate representations and compilers. However, the lack of a formal definition and reliance on natural language descriptions in the current state of QIR result in interpretational ambiguity and a dearth of rigor in implementing quantum functions. In this paper, we present formal definitions for QIR's data types and instruction sets to establish correctness and safety assurances for operations and intermediate code conversions within QIR. To demonstrate the effectiveness of our approach, we provide examples of unsafe QIR codes where errors can be identified with our method.
Quantum Intermediate Representation (QIR) is a Microsoft-developed, LLVM-based intermediate representation for quantum program compilers. QIR aims to provide a general solution for quantum program compilers independent of front-end languages and back-end hardware, thus avoiding duplicate development of intermediate representations and compilers. Since it is still under development, QIR is described in natural language and lacks a formal definition, leading to ambiguity in its interpretation and a lack of rigor in implementing quantum functions. In this paper, we provide formal definitions for the data types and instruction sets of QIR, aiming to provide correctness and security guarantees for operations and intermediate code conversions in QIR. To validate our design, we show some samples of unsafe QIR code where errors can be detected by our formal approach.
Intermediate representations (IRs) play a crucial role in the software stack of a quantum computer to facilitate efficient optimizations for executing an application on hardware. One of those IRs is the Quantum Intermediate Representation (QIR), which builds on the classical LLVM compiler infrastructure. In this article, we outline different approaches to how QIR can be adopted. This exploration culminates in a demonstration of what it takes to turn an existing quantum circuit simulator into a QIR runtime and that such a transition is less daunting than it might seem at first. We further show that switching to QIR does not entail any performance deficits compared to the original simulator. On the contrary, the presented steps effortlessly allow adding support for arbitrary classical control flow to any classical simulator. We conclude with an outlook on future directions using QIR. The implemented QIR runtime is available under https://github.com/munich-quantum-toolkit/core.
We propose an IR for quantum computing that directly exposes quantum and classical data dependencies for the purpose of optimization. The Quantum Intermediate Representation for Optimization (QIRO) consists of two dialects, one input dialect and one that is specifically tailored to enable quantum-classical co-optimization. While the first employs a perhaps more intuitive memory-semantics (quantum operations act on qubits via side-effects), the latter uses value-semantics (operations consume and produce states) to integrate quantum dataflow in the IR’s Static Single Assignment (SSA) graph. Crucially, this allows for a host of optimizations that leverage dataflow analysis. We discuss how to map existing quantum programming languages to the input dialect and how to lower the resulting IR to the optimization dialect. We present a prototype implementation based on MLIR that includes several quantum-specific optimization passes. Our benchmarks show that significant improvements in resource requirements are possible even through static optimization. In contrast to circuit optimization at run time, this is achieved while incurring only a small constant overhead in compilation time, making this a compelling approach for quantum program optimization at application scale.
Hybrid languages like the quantum intermediate representation (QIR) are essential for programming systems that mix quantum and conventional computing models, while execution of these programs is often deferred to a system-specific implementation. Here, we develop the QIR Execution Engine (QIR-EE) for parsing, interpreting, and executing QIR across multiple hardware platforms. QIR-EE uses LLVM to execute hybrid instructions specifying quantum programs and, by design, presents extension points that support customized runtime and hardware environments. We demonstrate an implementation that uses the XACC quantum hardware-accelerator library to dispatch prototypical quantum programs on different commercial quantum platforms and numerical simulators, and we validate execution of QIR-EE on IonQ, Quantinuum, and IBM hardware. Our results highlight the efficiency of hybrid executable architectures for handling mixed instructions, managing mixed data, and integrating with quantum computing frameworks to realize cross-platform execution.