Quantum resource reduction for quantum-centric supercomputing via correlated mean-field downfolding framework
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Quantum Physics arXiv:2605.08675 (quant-ph) [Submitted on 9 May 2026] Title:Quantum resource reduction for quantum-centric supercomputing via correlated mean-field downfolding framework Authors:Thien Ngoc Tran, Lan Nguyen Tran View a PDF of the paper titled Quantum resource reduction for quantum-centric supercomputing via correlated mean-field downfolding framework, by Thien Ngoc Tran and Lan Nguyen Tran View PDF HTML (experimental) Abstract:We present OBDF-SQD, a hybrid quantum-classical method that combines one-body downfolding~(OBDF) based on one-body Møller--Plesset second-order perturbation theory (OBMP2) with sample-based quantum diagonalization~(SQD) for use in quantum-centric supercomputing~(QCS). In this approach, OBMP2 is executed classically to fold dynamical correlation from external orbitals into a renormalized one-body operator, yielding an effective active-space Hamiltonian that retains the same operator structure as the bare Hamiltonian and therefore requires no additional quantum circuit resources. SQD is then applied to this effective Hamiltonian, where, in this work, the quantum sampling is performed via the Qiskit Aer simulator rather than actual quantum hardware. We benchmark OBDF-SQD on dissociation curves of \ce{H6} chain, ring, and lattice systems and the \ce{N2} molecule in the cc-pVDZ basis, comparing against standard methods and active-space SQD (CAS-SQD). We observed that OBDF-SQD consistently improves upon CAS-SQD with the same active space. The simplicity of the one-body downfolding correction also makes the approach straightforwardly extensible to periodic solids within existing quantum embedding frameworks Comments: Subjects: Quantum Physics (quant-ph); Chemical Physics (physics.chem-ph) Cite as: arXiv:2605.08675 [quant-ph] (or arXiv:2605.08675v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.08675 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Lan Tran [view email] [v1] Sat, 9 May 2026 04:22:32 UTC (75 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum resource reduction for quantum-centric supercomputing via correlated mean-field downfolding framework, by Thien Ngoc Tran and Lan Nguyen TranView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-05 Change to browse by: physics physics.chem-ph 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?)