LLM-driven evolutionary program synthesis discovers Generalized Superfast Encodings with exact distance 5 (and 6 on one instance) for molecular Hamiltonians, the first beyond distance 3.
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Constructs free-fermion subsystem codes with a 2D topological example, graph-based solvability algorithm, and gap analysis via skew energy and median eigenvalues.
Classical RNNs trained on small instances provide parameter initializations for QAOA and VQE that reduce total optimization iterations and generalize across problem sizes.
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Evolving Quantum Error-Correcting Encodings for Molecular Simulation
LLM-driven evolutionary program synthesis discovers Generalized Superfast Encodings with exact distance 5 (and 6 on one instance) for molecular Hamiltonians, the first beyond distance 3.
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Free-Fermion Subsystem Codes
Constructs free-fermion subsystem codes with a 2D topological example, graph-based solvability algorithm, and gap analysis via skew energy and median eigenvalues.
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Learning to learn with quantum neural networks via classical neural networks
Classical RNNs trained on small instances provide parameter initializations for QAOA and VQE that reduce total optimization iterations and generalize across problem sizes.