Architecture-Aware Reinforcement Learning for Communication-Efficient Distributed Quantum Circuit Compilation

Quantum Physics arXiv:2608.06892 (quant-ph) [Submitted on 7 Aug 2026] Title:Architecture-Aware Reinforcement Learning for Communication-Efficient Distributed Quantum Circuit Compilation Authors:Chien-Tung Kuo, Felix Burt, Samuel Yen-Chi Chen, Kin K. Leung, Kuan-Cheng Chen View a PDF of the paper titled Architecture-Aware Reinforcement Learning for Communication-Efficient Distributed Quantum Circuit Compilation, by Chien-Tung Kuo and 4 other authors View PDF HTML (experimental) Abstract:Distributed quantum computing provides a scalable route for executing quantum circuits beyond the capacity limits of a single quantum processing unit (QPU), but it introduces a communication-aware compilation problem involving strict hardware constraints and circuit dependencies. This paper presents an architecture-aware reinforcement-learning framework that formulates distributed quantum compilation as a constrained Markov Decision Process (MDP). The compiler-level communication actions dynamically update logical-qubit placement and enable subsequent gate execution. A heterogeneous graph model represents interactions among hardware, logical qubits, and circuit operations, while a policy trained via Proximal Policy Optimization optimizes EPR-pair consumption and communication makespan. Evaluation across benchmark circuits shows that our policy matches state-of-the-art heuristics on structured workloads, with lookahead reward shaping yielding modest improvements on unstructured circuits. These results demonstrate that reinforcement learning is a flexible alternative to manual heuristics, though scalability remains a key bottleneck for practical use. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2608.06892 [quant-ph] (or arXiv:2608.06892v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2608.06892 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Chien-Tung Kuo [view email] [v1] Fri, 7 Aug 2026 07:25:18 UTC (176 KB) Full-text links: Access Paper: View a PDF of the paper titled Architecture-Aware Reinforcement Learning for Communication-Efficient Distributed Quantum Circuit Compilation, by Chien-Tung Kuo and 4 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-08 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)