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arXiv:2610.18056

OpenMQPI: An MLIR-based Pipeline to Offload Quantum Circuits via OpenMP

Ercüment Kaya, Ekin Devim Şahinkaya, Minh Thanh Chung, Martin Schulz

Published Oct 13, 2026• 0 citations
arXivHTMLPDF

Abstract

Quantum computing introduces specialized software stacks and programming interfaces that are often tightly coupled to specific ecosystems and hardware backends, limiting interoperability and software reuse. For HPCQC application developers, this complicates the integration of quantum accelerators into existing applications. Existing quantum frameworks also use ecosystem-specific circuit representations and compilation pipelines, hindering integration with established heterogeneous compiler infrastructures. We propose OpenMQPI, an MLIR-based OpenMP extension for quantum programming that leverages OpenMP's standardized target offloading infrastructure to integrate quantum resources into HPC applications. OpenMQPI introduces QPIR, a lightweight MLIR dialect for representing quantum circuits within OpenMP target regions, and a compiler pipeline that performs quantum-specific optimizations and lowers circuits to executables compatible with OpenMP offloading. At runtime, an MQSS plugin enables the generated executable to offload quantum circuits to quantum hardware through the OpenMP target offloading model. We evaluate OpenMQPI using the VQE, QFT, and Deutsch-Jozsa algorithms with 5, 10, 15, and 20 qubits, comparing execution times against Qiskit and CUDA Quantum. OpenMQPI achieves the lowest average VQE execution time across all tested qubit configurations. At 20 qubits, it requires 5.18 s, compared with 5.83 s for Qiskit and 7.39 s for CUDA Quantum. For QFT and Deutsch-Jozsa, OpenMQPI achieves execution times comparable to Qiskit and lower than those of CUDA Quantum.

DOI
10.48550/arxiv.2610.18056

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Quantum computing introduces specialized software stacks and programming interfaces that are often tightly coupled to specific ecosystems and hardware backends, limiting interoperability and software reuse. For HPCQC application developers, this complicates the integration of quantum accelerators into existing applications. Existing quantum frameworks also use ecosystem-specific circuit representations and compilation pipelines, hindering integration with established heterogeneous compiler infrastructures. We propose OpenMQPI, an MLIR-based OpenMP extension for quantum programming that leverages OpenMP's standardized target offloading infrastructure to integrate quantum resources into HPC applications. OpenMQPI introduces QPIR, a lightweight MLIR dialect for representing quantum circuits within OpenMP target regions, and a compiler pipeline that performs quantum-specific optimizations and lowers circuits to executables compatible with OpenMP offloading. At runtime, an MQSS plugin enables the generated executable to offload quantum circuits to quantum hardware through the OpenMP target offloading model. We evaluate OpenMQPI using the VQE, QFT, and Deutsch-Jozsa algorithms with 5, 10, 15, and 20 qubits, comparing execution times against Qiskit and CUDA Quantum. OpenMQPI achieves the lowest average VQE execution time across all tested qubit configurations. At 20 qubits, it requires 5.18 s, compared with 5.83 s for Qiskit and 7.39 s for CUDA Quantum. For QFT and Deutsch-Jozsa, OpenMQPI achieves execution times comparable to Qiskit and lower than those of CUDA Quantum.
The Munich Quantum Software Stack (MQSS) is a compilation and runtime infrastructure designed to bridge the gap between high-performance computing (HPC) and quantum computing (QC). A unified, extensible, and efficient compilation framework becomes important as quantum devices and applications scale. This paper proposes the integration of Multi-Level Intermediate Representation (MLIR) as intermediate representation into the MQSS to address the challenges of optimizing and compiling hybrid classical-quantum applications across diverse quantum devices. Accordingly, the MQSS can separate abstraction layers, enabling high-level optimizations, seamless integration of existing quantum dialects, and targetspecific optimizations for diverse quantum devices.
Abstract Distributed quantum computing requires compiler infrastructure capable of partitioning quantum circuits across multiple quantum processing units (QPUs) and synthesizing entanglement-assisted communication channels. Existing quantum compilers—including Qiskit, t|ket⟩, and BQSKit—operate on single-QPU execution models and lack native support for multi-QPU intermediate representations. We present DQC, an MLIR-based distributed quantum compiler featuring a custom dqc dialect and a five-pass compilation pipeline: (1) interaction-graph-based qubit partitioning with weighted edge-cut minimization, (2) TeleGate synthesis replacing cross-QPU gates with EPR pair allocations and quantum teleportation sequences, (3) greedy operation reordering for communication pre-staging, (4) MPI dialect lowering for distributed runtime abstraction, and (5) LLVM IR emission targeting a quantum simulation runtime. We evaluate DQC on a distributed 4-qubit Quantum Fourier Transform and four additional benchmarks, demonstrating correct multi-QPU execution with verified quantum state output. To our knowledge, DQC is the first open-source MLIR-native compiler infrastructure targeting distributed quantum circuit execution with end-to-end compilation from high-level quantum IR to executable LLVM IR.
Quantum computing is increasingly integrated into High-Performance Computing (HPC) environments, where quantum processors act as specialized accelerators within hybrid workflows. The Munich Quantum Software Stack (MQSS) - a unified compilation and runtime framework for hybrid quantum–classical computing - provides the foundation for this integration. However, the growing heterogeneity of applications demands more flexible compilation tools. This work introduces an MultiLevel Intermediate Representation (MLIR)-based translation library that extends MQSS by enabling the conversion of CUDA-Quantum (CUDA-Q) (quake) dialects into machine learning–oriented MLIR representations compatible with modern compiler ecosystems. Leveraging MLIR’s dialect-driven design, the library enables hardware-agnostic transformations, device-specific optimizations, and seamless integration with MQSS components. The proposed approach bridges quantum compilation and contemporary machine learning frameworks, facilitating GPU-accelerated circuit simulation, hybrid quantum–classical workflows, and heterogeneous execution, thereby advancing a unified compiler infrastructure for quantum computing.
Quantum computers are making their way into High Performance Computing (HPC) centers as next-generation accelerators. Due to their physical implementation as mostly large appliances in separate racks, their number in typical data centers is significantly lower than the number of nodes offloading work to them, unlike the case with GPU accelerators. As a consequence, they form large-scale disaggregated infrastructures that pose a number of integration challenges due to their diverse implementation technologies and their need to be used as a shared resource for optimal utilization. Running hybrid High Performance Computing-Quantum Computing (HPCQC) applications in HPC environments, where the quantum portion is offloaded to the quantum processing units (QPUs), requires sophisticated resource management strategies to optimize resource utilization and performance. In this paper, we present one aspect of the Munich Quantum Software Stack (MQSS) - a Just-In-Time (JIT) compilation and execution software stack for quantum and hybrid quantum-HPC workloads - beneficial for integrating disaggregated quantum accelerators into traditional HPC workflows. MQSS is centered around a series of novel contributions aimed at optimizing the compilation process while managing the disaggregated resources ensuring efficient utilization of all available quantum resources. The key stages of our JIT compilation stack involve hardware-agnostic optimizations, quantum circuit cutting – when necessary – and a novel hardware platform selection process. Included here is description of the use of the MQSS’s Quantum Device Management Interface (QDMI), a unified interface between the software stack and the quantum accelerators, which allows connection to several, potentially different, quantum resources. Our methodology demonstrates notable advancements in HPCQC integration workflows, offering researchers a powerful framework for leveraging quantum computational capabilities. Overall, our work represents a significant step towards the practical integration of disaggregated quantum devices within the realm of supercomputing, unlocking new avenues for computational exploration and discovery.
The Multi-Level Intermediate Representation (MLIR) framework has become a cornerstone for building extensible, domain-specific compilers, with the quantum computing community already leveraging it to model quantum programs and implement basic optimizations. However, computationally intensive tasks in the quantum compilation pipeline, such as quantum circuit mapping, remain underexplored within the MLIR ecosystem. This paper proposes an MLIR-native blueprint for these non-local, quantum-specific optimization routines by reimplementing a well-established, state-of-the-art mapping A* search algorithm for qubit routing and SWAP insertion. Our evaluation demonstrates that this approach not only integrates seamlessly into an MLIR-based quantum compiler collection but also surpasses previous non-MLIR solutions in both solution quality and runtime. The implementation is open-source and publicly available at https://github.com/munich-quantum-toolkit/core.
Most of the widely used quantum programming languages and libraries are not designed for the tightly coupled nature of hybrid quantum-classical algorithms, which run on quantum resources that are integrated on-premise with classical HPC infrastructure. We propose a programming model using the API provided by OpenMP to target quantum devices, which provides an easy-to-use and efficient interface for HPC applications to utilize quantum compute resources. We have implemented a variational quantum eigensolver using the programming model, which has been tested using a classical simulator. We are in the process of testing on the quantum resources hosted at the Leibniz Supercomputing Centre (LRZ).