{
  "abstract": "Semiconductor quantum-dot arrays are a compelling platform for scalable quantum technologies, yet their practical operation is hindered by the complexity of tuning large-scale devices. Existing automation tools rely on simplified physical models---such as constant-capacitance approximations and equilibrium Hubbard models---which assume instantaneous relaxation to a steady state. These frameworks fail in experimentally critical regimes where measurement rates exceed tunneling dynamics, necessitating more sophisticated non-equilibrium control strategies. To bridge this gap, we introduce QArray+, an extension of the QArray framework that incorporates gate-dependent tunnel coupling and a quantum open-system description of dissipative processes. This approach enables the unified simulation of coherent interdot charge-state hybridization and the non-equilibrium latching dynamics essential for training robust machine-learning models for automated device operation. Implemented in JAX with GPU acceleration, QArray+ scales across GPUs and multi-node systems. For example, a charge stability diagram for a 100X100 grid of gate voltages over 64 dots can be computed in $\\sim0.17\\,\\mathrm{s}$ on multiple GPUs. Since interdot interactions are short-ranged and the corresponding tuning corrections are local, simulations at these scales capture the physics relevant to even larger devices. These capabilities support high-throughput dataset generation for automated device tuning.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.02736",
  "author_total": 9,
  "authors": [
    {
      "name": "Pranav Vaidhyanathan"
    },
    {
      "name": "Barnaby van Straaten"
    },
    {
      "name": "Alice Petrillo"
    },
    {
      "name": "Rahul Marchand"
    },
    {
      "name": "Edwin De Nicolo"
    },
    {
      "name": "Menno Veldhorst"
    },
    {
      "name": "Brucek Khailany"
    },
    {
      "name": "Taylor L. Patti"
    },
    {
      "name": "Natalia Ares"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fdv56dq07b9ka.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.02736",
  "fulltext_url": null,
  "id": "fdv56dq07b9ka",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.02736",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mes-hall"
    ],
    [
      "arxiv",
      "cs.DC"
    ],
    [
      "arxiv",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.02736",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fdv56dq07b9ka.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:16Z",
      "native_id": "2609.02736",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.02736"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mes-hall",
      "subfield": "condensed-matter"
    },
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "QArray+: A physics-informed GPU-accelerated simulator for quantum dot arrays",
  "title_text": "QArray+: A physics-informed GPU-accelerated simulator for quantum dot arrays",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:16Z",
  "url": "https://research.draconitic.com/paper/fdv56dq07b9ka",
  "venue": "arXiv",
  "versions": []
}