Quantum Information Harvesting with the Parallel Quantum Flow Algorithm
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Quantum Physics arXiv:2606.04186 (quant-ph) [Submitted on 2 Jun 2026] Title:Quantum Information Harvesting with the Parallel Quantum Flow Algorithm Authors:Nicholas P. Bauman, Ajay Panyala, Chenxu Liu, Muqing Zheng, Meng Wang, Karol Kowalski View a PDF of the paper titled Quantum Information Harvesting with the Parallel Quantum Flow Algorithm, by Nicholas P. Bauman and 5 other authors View PDF HTML (experimental) Abstract:The Quantum Flow (QFlow) algorithm provides a resource-efficient framework for describing correlated many-body systems on hybrid quantum-classical architectures. By enabling parallel utilization of quantum and classical resources, QFlow offers a scalable pathway toward simulations of realistic systems. In this Letter, we report a high-performance computing (HPC) implementation of the QFlow formalism based on a singles-and-doubles model. We demonstrate its performance for target spaces comprising 82 and 114 orbitals, where the flow includes all 6 active electrons in 6 active orbitals type active spaces. In the largest QFlow simulations, we optimize 1.17 million wave function parameters using the equivalent of 12 qubits. Despite the modest qubit requirements of the underlying active-space problems, the method recovers over $95\%$ of the total correlation energy obtained with the coupled cluster singles and doubles (CCSD) approach for systems dominated by dynamical correlation effects, which remain challenging for existing quantum algorithms. We further show that the QFlow formalism retains high accuracy in extended basis sets with diffuse functions, highlighting its potential for realistic large-scale quantum chemistry simulations. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2606.04186 [quant-ph] (or arXiv:2606.04186v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2606.04186 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Karol Kowalski [view email] [v1] Tue, 2 Jun 2026 20:08:10 UTC (1,002 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum Information Harvesting with the Parallel Quantum Flow Algorithm, by Nicholas P. Bauman and 5 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?)