A two-fold quantum embedding strategy combined with machine learning integrates accurate quantum-mechanical energies into free energy calculations for biomolecular complexes and analyzes requirements for quantum computers to enhance such modeling.
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A layered resource estimation framework applied to three quantum applications shows practical advantage requires 10^5-10^6 physical qubits, driven by size, speed, and controllability.
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How to use quantum computers for biomolecular free energies
A two-fold quantum embedding strategy combined with machine learning integrates accurate quantum-mechanical energies into free energy calculations for biomolecular complexes and analyzes requirements for quantum computers to enhance such modeling.
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Assessing requirements to scale to practical quantum advantage
A layered resource estimation framework applied to three quantum applications shows practical advantage requires 10^5-10^6 physical qubits, driven by size, speed, and controllability.