Hybrid HPC-Quantum Simulations: DFT-Quantum Embedding for Molecular Systems

Quantum Physics arXiv:2608.12884 (quant-ph) [Submitted on 13 Aug 2026] Title:Hybrid HPC-Quantum Simulations: DFT-Quantum Embedding for Molecular Systems Authors:Namrata Manglani, Samrit Maity, Shashank Sharma, Tejjan Arora, Soham Phulare, Shreyas Kadam, Sanjay Wandhekar View a PDF of the paper titled Hybrid HPC-Quantum Simulations: DFT-Quantum Embedding for Molecular Systems, by Namrata Manglani and Samrit Maity and Shashank Sharma and Tejjan Arora and Soham Phulare and Shreyas Kadam and Sanjay Wandhekar View PDF HTML (experimental) Abstract:Scientific simulations demand methods combining scalability with predictive accuracy.

Density Functional Theory (DFT) on High-Performance Computing (HPC) enables large-scale electronic-structure simulations but is limited by approximations affecting strongly correlated systems and band-gap predictions. Quantum computing offers a pathway to address this, though current Noisy Intermediate-Scale Quantum (NISQ) hardware remains constrained by qubit resources, noise, and execution cost. This work presents a hybrid DFT-Quantum Embedding (QDFT) framework integrating classical HPC-based DFT with a quantum electronic-structure solver. Large systems are partitioned to isolate a chemically relevant active space, treated via the Variational Quantum Eigensolver (VQE), while the remaining degrees of freedom are described by DFT. The framework incorporates active-space selection, embedded Hamiltonian construction, symmetry preservation, operator mapping, self-consistent density updating, and modular classical-quantum coupling. We focus on noiseless quantum simulation to systematically evaluate accuracy, convergence, active-space dependence, computational cost, and HPC scalability without hardware noise. Detailed profiling identifies computational bottlenecks and highlights limitations of CPU-based quantum simulation. A QPU runtime-estimation methodology is additionally developed to assess execution requirements on actual quantum hardware.

Results demonstrate quantum embedding's potential to improve selected electronic-structure properties while retaining classical HPC's scalability. Noisy quantum simulation and QPU execution remain key future directions, providing a pathway toward practical, scalable HPC-quantum hybrid simulations as hardware matures. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2608.12884 [quant-ph] (or arXiv:2608.12884v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2608.12884 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Namrata Manglani [view email] [v1] Thu, 13 Aug 2026 06:57:25 UTC (10,659 KB) Full-text links: Access Paper: View a PDF of the paper titled Hybrid HPC-Quantum Simulations: DFT-Quantum Embedding for Molecular Systems, by Namrata Manglani and Samrit Maity and Shashank Sharma and Tejjan Arora and Soham Phulare and Shreyas Kadam and Sanjay WandhekarView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-08 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?)