Integrating Quantum Software Tools with(in) MLIR

2026-01-05

SCA/HPC Asia

AI Summary

This work addresses the lack of interoperability among current quantum compilation tools, which hinders the development of a unified software stack. It presents the first systematic integration of PennyLane and the Munich Quantum Toolkit (MQT) through the MLIR framework, leveraging MLIR’s intermediate representation and LLVM infrastructure to establish reusable integration pathways and best practices. By lowering the barrier to entry for quantum software engineers adopting MLIR, this approach enables seamless interoperability between distinct quantum toolchains. The study provides both a practical roadmap and actionable strategies for constructing a modular, interoperable quantum software ecosystem, thereby advancing the standardization and composability of quantum programming tools.

Technology Category

• Machine Learning: Quantum Machine Learning
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Constraint Satisfaction and Optimization: Solvers and Tools

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Compilers transform code into action: They convert high-level programs into executable hardware instructions—a crucial step in enabling reliable and scalable quantum computation. However, quantum compilation is still in its infancy, and many existing solutions are ad hoc, often developed independently and from scratch. The resulting lack of interoperability leads to significant missed potential, as quantum software tools remain isolated and cannot be seamlessly integrated into cohesive toolchains. The Multi-Level Intermediate Representation (MLIR) has addressed analogous challenges in the classical domain. It was developed within the LLVM project, which has long powered robust software stacks and enabled compilation across diverse software and hardware components, with particular importance in high-performance computing environments. However, MLIR’s steep learning curve poses a significant barrier to entry, particularly in quantum computing, where much of the software stack is still predominantly built by experimentalists out of necessity rather than by experienced software engineers. This paper provides a practical and hands-on guide for quantum (software) engineers to overcome this steep learning curve. Through a concrete case study linking Xanadu’s PennyLane framework with the Munich Quantum Toolkit (MQT), we outline actionable integration steps, highlight best practices, and share hard-earned insights from real-world development. This work aims to support quantum tool developers in navigating MLIR’s complexities and to foster its adoption as a unifying bridge across a rapidly growing ecosystem of quantum software tools, ultimately guiding the development of more modular, interoperable, and integrated quantum software stacks.

Problem

• quantum compilation
• interoperability
• MLIR
• quantum software tools
• toolchain integration

Innovation

• MLIR
• quantum compilation
• software interoperability
• PennyLane
• quantum software stack