[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82682-en":3,"doc-seo-82682-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82682,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","MLIR for Quantum Beyond Gate Cancellation: Quantum Circuit Mapping Reimagined","The Multi-Level Intermediate Representation (MLIR) framework supports extensible, domain-specific compilers, and the quantum community has used it to model quantum programs and apply basic optimizations. This paper targets computationally intensive quantum compilation tasks—specifically quantum circuit mapping—within the MLIR ecosystem. An MLIR-native blueprint reimplements a state-of-the-art A* search approach for qubit routing and SWAP insertion. Results show seamless integration, improved solution quality and runtime versus prior non-MLIR methods, with open-source implementation.","MLIR for Quantum Beyond Gate Cancellation: Quantum Circuit Mapping Reimagined  \nMatthias Reumann∗ , Yannick Stade∗ , Robert Wille∗†, and Lukas Burgholzer∗†  \n∗ Chair for Design Automation, Technical University of Munich, Munich, Germany †Munich Quantum Software Company GmbH, Garching near Munich, Germany  \n{matthias.reumann, yannick.stade, robert.wille, [lukas.burgholzer}@tum.de](lukas.burgholzer}@tum.de)[ ](lukas.burgholzer}@tum.de)[www.cda.cit.tum.de/research/quantum](www.cda.cit.tum.de/research/quantum)  \narXiv :2607 .02616v1 [ quant-ph] 1 Jul 2026  \nAbstract—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 [github.com/munich-quantum-toolkit/core](github.com/munich-quantum-toolkit/core).  \nIndex Terms—quantum computing, quantum circuit mapping, quantum compilation  \nI. INTRODUCTION  \nAs quantum computing hardware scales beyond the noisy intermediate-scale quantum (NISQ) era [1, 2], the complexity of quantum programs that can be executed increases significantly. However, the inherent noise and limited coherence times of current and near-term devices necessitate highly optimized compilation pipelines to maximize circuit fidelity. While simple, local optimizations such as gate cancellation or fusion are standard in most compilers, they are increasingly insufficient to bridge the gap between algorithmic requirements and hardware capabilities. Consequently, there is a growing need for advanced, global optimization routines capable of significantly reducing circuit depth and error rates.  \nDeveloping such sophisticated compilers from scratch is a formidable software engineering challenge. Instead of reinventing the wheel, the classical compiler community has converged on Multi-Level Intermediate Representation (MLIR) [3] as a modular and extensible framework for building domain-specific compilers. Following its immense success in the machine learning and high-performance computing domains [4–7], MLIR is gaining traction in the quantum computing community. Several works have explored modeling quantum programs within MLIRand implementing basic optimizations [8–12] . However, these efforts largely focus on feasibility studies involving relatively simple, local transformations. A crucial question remains unanswered: Is MLIR suitable for implementing complex,  \nquantum-specific optimization routines that are non-local and computationally intensive?  \nIn this work, we address this question by investigating quantum circuit mapping—often synonymously referred to as qubit routing or layout synthesis in the literature—within the MLIR ecosystem. Quantum circuit mapping is a mandatory and critical step in the compilation pipeline for architectures with restricted qubit connectivity, such as superconducting quantum processors. The problem involves transforming an abstract quantum circuit into one that respects the hardware’s coupling graph by inserting SWAP gates, aiming to minimize the added overhead. Since the problem is NP-complete and the search space grows exponentially with the number of qubits, it represents a significantly more challenging test case for MLIR than typical peephole optimiz","cbCaimL3va0lAPqW","https://ap.wps.com/l/cbCaimL3va0lAPqW","pdf",424244,1,11,"English","en",105,"# Introduction\n## Quantum circuit mapping as a critical, NP-complete step\n# Background\n## Multi-Level Intermediate Representation (MLIR)\n## Hardware-constrained compilation needs","[{\"question\":\"What optimization does the paper focus on within the MLIR ecosystem?\",\"answer\":\"The paper focuses on quantum circuit mapping, also known as qubit routing or layout synthesis, including qubit placement under restricted connectivity through SWAP insertion.\"},{\"question\":\"How is the proposed mapping approach implemented?\",\"answer\":\"It reimplements a state-of-the-art A* search algorithm using MLIR-native abstractions for qubit routing and SWAP insertion.\"},{\"question\":\"What performance benefits does the MLIR-native implementation provide?\",\"answer\":\"The evaluation shows better solution quality (fewer SWAP gates) and improved runtime compared with the previous non-MLIR C++ implementation.\"}]",1784182260,28,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"mlir-for-quantum-beyond-gate-cancellation-quantum-circuit-mapping-reimagined","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/mlir-for-quantum-beyond-gate-cancellation-quantum-circuit-mapping-reimagined/82682/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What optimization does the paper focus on within the MLIR ecosystem?","Question",{"text":75,"@type":76},"The paper focuses on quantum circuit mapping, also known as qubit routing or layout synthesis, including qubit placement under restricted connectivity through SWAP insertion.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the proposed mapping approach implemented?",{"text":80,"@type":76},"It reimplements a state-of-the-art A* search algorithm using MLIR-native abstractions for qubit routing and SWAP insertion.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance benefits does the MLIR-native implementation provide?",{"text":84,"@type":76},"The evaluation shows better solution quality (fewer SWAP gates) and improved runtime compared with the previous non-MLIR C++ 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