{
  "checked_on": "2026-10-25",
  "scope": "Selected primary research for a proposed Universal Informatics Quantum Lab guide. This is a focused selection, not a systematic review. Bibliographic records, original summaries and links only; no third-party figures or article text copied.",
  "papers": [
    {
      "id": "kornjaca-2024-qrc",
      "title": "Large-scale quantum reservoir learning with an analog quantum computer",
      "authors_short": "Milan Kornja\u010da et al.",
      "url": "https://arxiv.org/abs/2407.02553",
      "evidence": "Preprint; Aquila quantum hardware and classical comparisons",
      "route": "Neutral atoms / quantum reservoir computing",
      "finding": "Biological or other input features can be encoded into neutral-atom dynamics; measured observables feed a classical predictor. Experiments include classification and time-series tasks up to 108 qubits.",
      "limit": "Performance is task-dependent. Synthetic kernel comparisons do not establish a general biological advantage or an OXTR result."
    },
    {
      "id": "giusto-2026-protein-qek",
      "title": "Harnessing a 256-qubit Neutral Atom Simulator for Graph Classification",
      "authors_short": "Edoardo Giusto et al.",
      "url": "https://arxiv.org/abs/2605.04737",
      "evidence": "Preprint; Aquila hardware on protein graphs",
      "route": "Neutral atoms / biological classification",
      "finding": "Measured Aquila responses supplied a quantum kernel for protein graph classification, with performance comparable to the selected classical graph benchmark.",
      "limit": "Graph embedding restricts the tested subset. The study does not test OXTR, Fibonacci representations or transcription-factor occupancy."
    },
    {
      "id": "antoncich-2026-medical-qrc",
      "title": "Quantum Reservoir Computing with Neutral Atoms on a Small, Complex, Medical Dataset",
      "authors_short": "Luke Antoncich et al.",
      "url": "https://arxiv.org/abs/2602.14641",
      "evidence": "Preprint; Aquila hardware and emulation",
      "route": "Neutral atoms / biomedical features",
      "finding": "The authors used Aquila responses to generate features for medical-data classification and compared them with emulated features.",
      "limit": "Emulated features showed overfitting. Aggregated feature selection risks test leakage in hardware comparisons; definitive hardware advantage over classical models remains unestablished."
    },
    {
      "id": "li-2018-tf-binding",
      "title": "Quantum annealing versus classical machine learning applied to a simplified computational biology problem",
      "authors_short": "Richard Y. Li, Rosa Di Felice, Remo Rohs and Daniel A. Lidar",
      "journal": "npj Quantum Information",
      "published": "2018-02-21",
      "doi": "10.1038/s41534-018-0060-8",
      "url": "https://www.nature.com/articles/s41534-018-0060-8",
      "evidence": "Peer reviewed; quantum hardware and classical comparisons",
      "route": "Annealing / transcription-factor binding",
      "finding": "A D-Wave annealer classified and ranked experimental transcription-factor binding data using simplified small datasets.",
      "limit": "Results depend on small training sets and the tested formulations; performance relative to classical methods declines as training data increase. This does not establish general quantum speedup.",
      "platform_relevance": "A bridge to our DNA-binding research, with a classical baseline and held-out evaluation. It is not evidence for an OXTR-specific quantum result.",
      "source_checked": "Publisher abstract, results and discussion"
    },
    {
      "id": "boev-2021-genome-assembly",
      "title": "Genome assembly using quantum and quantum-inspired annealing",
      "authors_short": "A. S. Boev et al.",
      "journal": "Scientific Reports",
      "published": "2021-06-23",
      "doi": "10.1038/s41598-021-88321-5",
      "url": "https://www.nature.com/articles/s41598-021-88321-5",
      "evidence": "Peer reviewed; QPU, hybrid and classical quantum-inspired methods",
      "route": "Annealing / DNA assembly",
      "finding": "DNA read ordering is expressed as a binary optimization problem and tested with synthetic sequences and bacteriophage-derived data.",
      "limit": "The larger bacteriophage graph required manual classical partitioning and a hybrid backend; synthetic reads excluded sequencing errors. The work does not establish general superiority over classical assembly.",
      "platform_relevance": "A small, inspectable read-ordering exercise can teach the path from DNA to graph to optimization to reconstructed sequence.",
      "source_checked": "Publisher methods, results and limitations"
    },
    {
      "id": "chen-2024-haplotype",
      "title": "Haplotype-resolved assembly of diploid and polyploid genomes using quantum computing",
      "authors_short": "Yibo Chen et al.",
      "journal": "Cell Reports Methods",
      "published_online": "2024-04-12",
      "issue_date": "2024-05-20",
      "doi": "10.1016/j.crmeth.2024.100754",
      "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11133727/",
      "evidence": "Peer reviewed; small QPU proof of concept and separate classical scaling",
      "route": "Annealing / haplotype reconstruction",
      "finding": "The authors mapped phasing and assembly to a vehicle-routing formulation and reconstructed short synthetic diploid and triploid examples on D-Wave.",
      "limit": "The larger human MHC-region experiment used Google's classical OR-Tools. The quantum examples used 50-base-pair diploid and 35-base-pair triploid sequences; these are not whole-genome QPU assemblies.",
      "platform_relevance": "Teach both the optimization formulation and the boundary between a quantum demonstration and classical scaling.",
      "source_checked": "Full article on PMC plus PubMed bibliographic record",
      "rights_note": "Article is CC BY-NC-ND; retain our original summary and citation rather than reusing its figures."
    },
    {
      "id": "ebadi-2022-neutral-atom-mis",
      "title": "Quantum optimization of maximum independent set using Rydberg atom arrays",
      "authors_short": "Sepehr Ebadi et al.",
      "journal": "Science",
      "published_online": "2022-05-05",
      "doi": "10.1126/science.abo6587",
      "url": "https://www.science.org/doi/10.1126/science.abo6587",
      "open_author_source": "https://arxiv.org/abs/2202.09372",
      "evidence": "Peer reviewed; neutral-atom quantum hardware",
      "route": "Neutral atoms / graph optimization",
      "finding": "Rydberg arrays with up to 289 qubits experimentally solved maximum-independent-set graph instances.",
      "limit": "This is a graph-optimization foundation, not a genomics experiment. The speedup comparison in the paper is scoped to its instances and classical simulated-annealing benchmark, not all classical solvers. It is not a record of execution on our Aquila account.",
      "platform_relevance": "Explain why selecting mutually compatible graph vertices maps naturally to Rydberg blockade.",
      "source_checked": "Author-hosted paper, arXiv record and MIT publication page"
    },
    {
      "id": "garrigues-2025-docking-v2",
      "title": "A Scalable Heuristic for Molecular Docking on Neutral-Atom Quantum Processors",
      "authors_short": "Mathieu Garrigues, Victor Onofre, Wesley Coelho and S. Acheche",
      "venue": "arXiv preprint",
      "first_posted": "2025-08-25",
      "version_date": "2026-04-24",
      "version": "v2",
      "doi": "10.48550/arXiv.2508.18147",
      "url": "https://arxiv.org/abs/2508.18147v2",
      "evidence": "Preprint; neutral-atom emulator",
      "route": "Neutral-atom methods / molecular docking",
      "finding": "A decomposed graph-based method was evaluated on ten protein-ligand complexes with 225\u2013585 vertices.",
      "limit": "The reported solver uses an emulator. This is not a QuEra Aquila hardware result, a general docking speedup, or demonstrated drug-discovery success. A greedy baseline is not the full range of classical docking methods.",
      "platform_relevance": "An emerging docking direction, clearly labelled as simulation research alongside the hardware foundations.",
      "source_checked": "arXiv v2 abstract and full HTML; no journal publication shown in the checked record"
    }
  ]
}