Quantum Simulation of Ligand-like Molecules through Sample-based Quantum Diagonalization in Density Matrix Embedding Framework

Quantum Physics arXiv:2511.22158 (quant-ph) [Submitted on 27 Nov 2025] Title:Quantum Simulation of Ligand-like Molecules through Sample-based Quantum Diagonalization in Density Matrix Embedding Framework Authors:Ashish Kumar Patra, Anurag K. S. V., Sai Shankar P., Ruchika Bhat, Raghavendra V., Rahul Maitra, Jaiganesh G View a PDF of the paper titled Quantum Simulation of Ligand-like Molecules through Sample-based Quantum Diagonalization in Density Matrix Embedding Framework, by Ashish Kumar Patra and 6 other authors View PDF HTML (experimental) Abstract:The accurate treatment of electron correlation in extended molecular systems remains computationally challenging using classical electronic structure methods. Hybrid quantum-classical algorithms offer a potential route to overcome these limitations; however, their practical deployment on existing quantum computers requires strategies that both reduce problem size and mitigate hardware noise. In this work, we combine Density Matrix Embedding Theory (DMET) with Sample-based Quantum Diagonalization (SQD) to compute ground-state energies of a set of natural ligand-like molecules in the minimal Slater Type Orbital (STO-3G) basis set. DMET provides a systematic fragmentation of a molecule into embedded impurity subproblems, while SQD enables construction and classical diagonalization of reduced configuration spaces through quantum sampling enhanced by iterative configuration recovery. The resulting embedded Hamiltonians are solved on IBM's Eagle R3 superconducting quantum hardware (IBM Sherbrooke). The DMET-SQD energies obtained for all systems considered exhibit strong agreement with DMET-FCI benchmark values within chemical accuracy (1 kcal/mol). These results demonstrate that sample-based quantum methods, when integrated with a robust embedding framework, can reliably extend quantum computation towards simulation of chemically relevant molecular systems, showcasing potential applications in the field of drug discovery. Comments: Subjects: Quantum Physics (quant-ph); Chemical Physics (physics.chem-ph) Cite as: arXiv:2511.22158 [quant-ph] (or arXiv:2511.22158v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2511.22158 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ashish Kumar Patra [view email] [v1] Thu, 27 Nov 2025 06:50:52 UTC (237 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum Simulation of Ligand-like Molecules through Sample-based Quantum Diagonalization in Density Matrix Embedding Framework, by Ashish Kumar Patra and 6 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2025-11 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?) 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?)