Hamiltonian-Guided Leverage Embedding: Robust Subspace Compression for Efficient QAOA Parameter Estimation
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Quantum Physics arXiv:2606.07814 (quant-ph) [Submitted on 5 Jun 2026] Title:Hamiltonian-Guided Leverage Embedding: Robust Subspace Compression for Efficient QAOA Parameter Estimation Authors:Sumanta Mukherjee, Kalyan Dasgupta, Surya Shravan Kumar Sajja, Kameshwaran Sampath, Abhishek Singh, Dhriti Verma, Dzung Phan, Jayant Kalagnanam View a PDF of the paper titled Hamiltonian-Guided Leverage Embedding: Robust Subspace Compression for Efficient QAOA Parameter Estimation, by Sumanta Mukherjee and 7 other authors View PDF HTML (experimental) Abstract:The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical framework for combinatorial optimization on near-term quantum devices. A central bottleneck is the classical estimation of its variational parameters {\gamma} and {\beta}, which must be optimized over a high-dimensional, non-convex landscape corrupted by sampling noise. We observe that the classical feature matrices constructed from QAOA measurement samples exhibit pronounced low-rank structure, and exploit this property for noise-robust, reduced-dimension parameter search. We present the Hamiltonian-Guided Leverage Embedding (HGLE) algorithm - a hybrid pipeline that encodes low-energy quantum samples into a weighted Ising feature matrix and compresses it via leverage-score row sampling, provably preserving the dominant rank-rsubspace geometry. The compressed representation drives a classical trust-region loop for ({\gamma}, {\beta}) estimation at a fraction of the original cost. We provide formal guarantees for rank preservation and energy approximation error, and demonstrate robustness across problem types (Max-Cut, Maximum Independent Set) and graph topologies of varying density. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2606.07814 [quant-ph] (or arXiv:2606.07814v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2606.07814 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Kalyan Dasgupta [view email] [v1] Fri, 5 Jun 2026 19:47:28 UTC (1,343 KB) Full-text links: Access Paper: View a PDF of the paper titled Hamiltonian-Guided Leverage Embedding: Robust Subspace Compression for Efficient QAOA Parameter Estimation, by Sumanta Mukherjee and 7 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-06 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?)