{
  "abstract": "Present-day sample-based quantum algorithms within quantum-centric supercomputing workflows require substantial classical post-processing, large circuit depths to produce chemically relevant samples, and long QPU times to execute the many circuits needed for molecules of chemically relevant size. We introduce FMO-QVM-HCI-HSQD, a resource-efficient sample-based framework that breaks a large problem into fragments, runs the fragments in parallel on virtual regions of the QPU, and reduces circuit depth through a half-qubit ansatz whose samples seed an HCI expansion. We demonstrate the algorithm on ibm_marrakesh and ibm_boston for the water trimer and the carmofur_Mpro complex with up to 52 qubits, benchmarking against FMO-CCSD(T), FMO-CASCI and FMO-HCI. For the water trimer (14 spin orbitals for monomers, 28 for dimers), our method reduces the two-qubit depth by 39% compared to FMO-QVM-SQD and the QPU time by 82% compared to sequential FMO-SQD, and accesses 3% of the determinant subspace versus 40% for FMO-SQD, while achieving more accurate energies. For the carmofur_Mpro complex (16 and 32 spin orbitals), it reduces the two-qubit depth by 47% and the QPU time by 20% compared to FMO-QVM-SQD, and accesses 0.007% of the determinant subspace versus 40%, while remaining within chemical accuracy. Extending the carmofur_Mpro active space in the 6-31G basis to 52 and 104 spin orbitals, sampled on 26 and 52 qubits, the total energy deviates from FMO-HCI by only 1.22 mHa. These results show that combining fragmentation, virtual QPU partitioning and half-qubit sampling makes sample-based quantum simulation of chemically relevant systems feasible on current hardware.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.41986",
  "author_total": 7,
  "authors": [
    {
      "name": "R. Pradeep"
    },
    {
      "name": "Anurag Chaudhary"
    },
    {
      "name": "Ashish Kumar Patra"
    },
    {
      "name": "Anurag K. S. V."
    },
    {
      "name": "Ruchika Bhat"
    },
    {
      "name": "Sai Shankar P."
    },
    {
      "name": "G. Jaiganesh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8jr89vfqe332n.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.41986",
  "fulltext_url": null,
  "id": "8jr89vfqe332n",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.41986",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.41986",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8jr89vfqe332n.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:15Z",
      "native_id": "2610.41986",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.41986"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Resource efficient Sample Based Quantum Simulation through Fragmentation and Virtual Partitioning of QPU",
  "title_text": "Resource efficient Sample Based Quantum Simulation through Fragmentation and Virtual Partitioning of QPU",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:51Z",
  "url": "https://research.draconitic.com/paper/8jr89vfqe332n",
  "venue": "arXiv",
  "versions": []
}