{
  "abstract": "Sample-based quantum diagonalization (SQD) has emerged as a promising route for quantum-centric supercomputing, relying on classical diagonalization of the molecular Hamiltonian within a hardware-sampled determinant subspace. However, its accuracy degrades in strongly correlated regimes where the relevant determinant space exceeds what finite-shot sampling can capture. In this work, we introduce Quantum Wavefunction Augmentation via Variational Autoencoders (Q-WAVE), a hybrid method that combines determinants sampled via SqDRIFT Krylov circuits and configuration interaction singles and doubles (CISD) determinants with generative machine learning. Using a custom $β$-annealed variational autoencoder (VAE) model, Q-WAVE iteratively expands this basis toward the variational ground state. The VAE learns the wavefunction's primary support structure from the combined hardware and CISD seed in a continuous latent space, generating new dominant determinants beyond any fixed excitation hierarchy. The resulting compact wavefunction exceeds what can be extracted from raw hardware samples alone. We demonstrate sub-millihartree accuracy compared to full configuration interaction for $\\text{H}_2\\text{O}$ and $\\text{N}_2$ dissociation. Finally, we establish Q-WAVE's scalability on a 52-qubit ethylene system (achieving sub-millihartree accuracy versus CCSD(T)) and a highly correlated 60-qubit $\\text{Cr}_2$ stress test that attains chemical accuracy upon a final perturbative correction.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.07972",
  "author_total": 3,
  "authors": [
    {
      "name": "Sonaldeep Halder"
    },
    {
      "name": "Chayan Patra"
    },
    {
      "name": "Rahul Maitra"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4xmme23qtddgh.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.07972",
  "fulltext_url": null,
  "id": "4xmme23qtddgh",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.07972",
  "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",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7212074714",
  "pdf_url": "https://arxiv.org/pdf/2609.07972",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4xmme23qtddgh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:48Z",
      "native_id": "2609.07972",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.07972"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Quantum Wavefunction Augmentation via Variational Autoencoders",
  "title_text": "Quantum Wavefunction Augmentation via Variational Autoencoders",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:48Z",
  "url": "https://research.draconitic.com/paper/4xmme23qtddgh",
  "venue": "arXiv",
  "versions": []
}