TreeVQA: A Tree-Structured Execution Framework for Shot Reduction in Variational Quantum Algorithms
Summarize this article with:
Quantum Physics arXiv:2512.12068 (quant-ph) [Submitted on 12 Dec 2025] Title:TreeVQA: A Tree-Structured Execution Framework for Shot Reduction in Variational Quantum Algorithms Authors:Yuewen Hou, Dhanvi Bharadwaj, Gokul Subramanian Ravi View a PDF of the paper titled TreeVQA: A Tree-Structured Execution Framework for Shot Reduction in Variational Quantum Algorithms, by Yuewen Hou and 2 other authors View PDF HTML (experimental) Abstract:Variational Quantum Algorithms (VQAs) are promising for near- and intermediate-term quantum computing, but their execution cost is substantial. Each task requires many iterations and numerous circuits per iteration, and real-world applications often involve multiple tasks, scaling with the precision needed to explore the application's energy landscape. This demands an enormous number of execution shots, making practical use prohibitively expensive. We observe that VQA costs can be significantly reduced by exploiting execution similarities across an application's tasks. Based on this insight, we propose TreeVQA, a tree-based execution framework that begins by executing tasks jointly and progressively branches only as their quantum executions diverge. Implemented as a VQA wrapper, TreeVQA integrates with typical VQA applications. Evaluations on scientific and combinatorial benchmarks show shot count reductions of $25.9\times$ on average and over $100\times$ for large-scale problems at the same target accuracy. The benefits grow further with increasing problem size and precision requirements. Comments: Subjects: Quantum Physics (quant-ph); Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC); Emerging Technologies (cs.ET) Cite as: arXiv:2512.12068 [quant-ph] (or arXiv:2512.12068v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2512.12068 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yuewen Hou [view email] [v1] Fri, 12 Dec 2025 22:30:31 UTC (5,490 KB) Full-text links: Access Paper: View a PDF of the paper titled TreeVQA: A Tree-Structured Execution Framework for Shot Reduction in Variational Quantum Algorithms, by Yuewen Hou and 2 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2025-12 Change to browse by: cs cs.AR cs.DC cs.ET 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?) Links to Code Toggle Papers with Code (What is Papers with Code?) 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?)