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    "headline": "A Local Hamiltonian Makes Neural Quantum Simulation Travel Lighter",
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    "dek": "A Bayesian localization method reduced the cost of neural-network quantum Monte Carlo while keeping test errors within the authors' acceptable range.",
    "summary": "A Bayesian localization method reduced the cost of neural-network quantum Monte Carlo while keeping test errors within the authors' acceptable range.",
    "body_text": "JAIST and ByteDance Seed researchers combined neural networks with a Bayesian localization of a pseudo-Hamiltonian to approximate electron-level material behavior. Tests reported in Nature Computational Science kept prediction errors within the study's acceptable range while lowering computational cost enough to consider larger systems. The result is a method benchmark, not a discovered material or a universal replacement for first-principles simulation.",
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      "title": "JAIST via EurekAlert: Neural-network quantum computation",
      "publisher": "Japan Advanced Institute of Science and Technology via EurekAlert",
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