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    "story_id": "mp-2026-11-12-001",
    "source_story_id": "tmp-lead-hardware-trained-quantum-eigensolver",
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    "headline": "The Quantum Circuit Learns From the Hardware",
    "slug": "the-quantum-circuit-learns-from-the-hardware",
    "dek": "A generative policy trained with a 70-qubit processor proposes far shorter circuits for molecular energy estimates, then reuses them on unseen organotin systems.",
    "summary": "A generative policy trained with a 70-qubit processor proposes far shorter circuits for molecular energy estimates, then reuses them on unseen organotin systems.",
    "body_text": "Researchers trained transformer policies to generate quantum state-preparation circuits, first in simulation and then with an IBM Heron processor inside the loop at 46 and 70 qubits. The four main hardware-training runs used 120 million shots, while reward terms discouraged collapse to circuits that sampled only a simple reference state. Applied without retraining to organotin hydroxide models, 27 of 28 runs at 46 to 70 qubits landed within 15 millihartree of a density-matrix renormalization reference after one hardware-sampling round. The selected circuits used 34 to 72 times fewer two-qubit gates than a compared ansatz at 56 to 70 qubits; these are reported molecular-model results, not a general quantum advantage or a chemical discovery.",
    "why_it_matters": "A generative policy trained with a 70-qubit processor proposes far shorter circuits for molecular energy estimates, then reuses them on unseen organotin systems.",
    "limitations": [
      "The four main hardware-training runs used 120 million shots, while reward terms discouraged collapse to circuits that sampled only a simple reference state.",
      "The selected circuits used 34 to 72 times fewer two-qubit gates than a compared ansatz at 56 to 70 qubits; these are reported molecular-model results, not a general quantum advantage or a chemical discovery."
    ],
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    "first_published_at": "2026-11-12T09:00:00.000-05:00",
    "modified_at": "2026-11-12T09:00:00.000-05:00",
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        "text": "A generative policy trained with a 70-qubit processor proposes far shorter circuits for molecular energy estimates, then reuses them on unseen organotin systems.",
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        "qualification": "The four main hardware-training runs used 120 million shots, while reward terms discouraged collapse to circuits that sampled only a simple reference state."
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    "source_ids": [
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    "tags": [
      "quantum",
      "eigensolver",
      "hardware",
      "molecules",
      "circuits"
    ],
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  "sources": [
    {
      "source_id": "source-2026-11-12-001",
      "title": "arXiv:2611.13755v1",
      "publisher": "arXiv / Cornell University",
      "url": "https://arxiv.org/abs/2611.13755v1",
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      "source_type": "primary_research",
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      "published_at": "2026-11-10T03:54:35.000-05:00",
      "accessed_at": "2026-11-12T08:24:40.465-05:00",
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    "name": "The Machine Press",
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    "description": "A daily newspaper for the age of artificial intelligence."
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    "title": "The Quantum Circuit Learns From the Hardware",
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