{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.26833",
  "title": "Supervised Learning with Quantum Hamiltonian Embeddings",
  "summary": "The method replaces a classical supervised classifier with a parameterized data-dependent Hamiltonian whose approximate ground state defines the decision rule, trained and executed on quantum processors. It costs access to quantum hardware (demonstrated on IBM Heron processors), a quantum ground-state approximation subroutine, and a hybrid training pipeline, none of which fit a 24 GB GPU or standard API budget. It can fail through noise and limited qubit counts on current hardware, unproven practical advantage over classical baselines on real workloads, and dependence on cryptographic assumptions for its classical-hardness claim.",
  "rationale": "The framework requires quantum processors and offers no demonstrated accuracy, speed, or cost benefit over classical models the reader can already run, so it is irrelevant to production adoption on constrained classical infrastructure.",
  "technique": "Quantum Hamiltonian embeddings for supervised learning",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-10-19",
  "as_of": "2026-10-20",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.26833",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces a classical supervised classifier with a parameterized data-dependent Hamiltonian whose approximate ground state defines the decision rule, trained and executed on quantum processors. It costs access to quantum hardware (demonstrated on IBM Heron processors), a quantum ground-state approximation subroutine, and a hybrid training pipeline, none of which fit a 24 GB GPU or standard API budget. It can fail through noise and limited qubit counts on current hardware, unproven practical advantage over classical baselines on real workloads, and dependence on cryptographic assumptions for its classical-hardness claim.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.26833",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
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      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}