QBalance: A Reproducible Multi-Objective Workflow for Quantum Compilation, Noise Suppression, and Error-Mitigation Strategy Selection
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Quantum Physics arXiv:2605.02966 (quant-ph) [Submitted on 3 May 2026] Title:QBalance: A Reproducible Multi-Objective Workflow for Quantum Compilation, Noise Suppression, and Error-Mitigation Strategy Selection Authors:Soumyadip Sarkar View a PDF of the paper titled QBalance: A Reproducible Multi-Objective Workflow for Quantum Compilation, Noise Suppression, and Error-Mitigation Strategy Selection, by Soumyadip Sarkar View PDF HTML (experimental) Abstract:Near-term quantum workloads are shaped by coupled compilation and execution choices: qubit layout, routing, basis translation, gate suppression, measurement mitigation, shot budget, and artifact reproducibility. This paper analyzes QBalance, a Python workflow library for dataset-level selection among quantum compilation, noise-suppression, and error-mitigation strategies built on the Qiskit ecosystem. The contribution is formulated as a finite multi-objective strategy-selection problem over circuits, backends, and transformation policies. The manuscript derives the implemented weighted objective, non-dominated selection rule, survival-product error proxy, Bayesian linear candidate-ordering surrogate, and distributional diagnostics. It also positions the system relative to established work on Qiskit pass-manager compilation, SABRE-style routing, randomized compiling, dynamical decoupling, zero-noise extrapolation, matrix-free measurement mitigation, circuit cutting, and Thompson sampling. The analysis shows that QBalance provides a reproducible orchestration and artifact model for quantum workflow studies. It also establishes precise limitations: the current bandit mechanism orders candidates but does not reduce the number of candidate evaluations, the custom layout heuristic is greedy and only partially topology-aware, the implemented ZNE helper is parity-centered, and the cutting integration is a hook rather than a full reconstruction pipeline. Subjects: Quantum Physics (quant-ph); Mathematical Software (cs.MS) Cite as: arXiv:2605.02966 [quant-ph] (or arXiv:2605.02966v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.02966 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Soumyadip Sarkar [view email] [v1] Sun, 3 May 2026 09:28:48 UTC (19 KB) Full-text links: Access Paper: View a PDF of the paper titled QBalance: A Reproducible Multi-Objective Workflow for Quantum Compilation, Noise Suppression, and Error-Mitigation Strategy Selection, by Soumyadip SarkarView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-05 Change to browse by: cs cs.MS 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?)