A tailored quantum multi-programming workflow for the LUCJ ansatz enables parallel circuit execution with SQD/ext-SQD post-processing that mitigates cross-talk, yielding ethanol energies within 0.001 kcal/mol of classical HCI references.
Suppression of qubit crosstalk in a tunable coupling superconducting circuit
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HAML meta-learns a mapping from control inputs and device parameters to effective two-qubit Hamiltonian coefficients via simulation training, then adapts online with few measurements, recovering coefficients where Schrieffer-Wolff perturbation theory fails.
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A Quantum Multi-Programming Framework to Maximize Quantum Resources for the LUCJ Ansatz
A tailored quantum multi-programming workflow for the LUCJ ansatz enables parallel circuit execution with SQD/ext-SQD post-processing that mitigates cross-talk, yielding ethanol energies within 0.001 kcal/mol of classical HCI references.
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Data-Driven Hamiltonian Reduction for Superconducting Qubits via Meta-Learning
HAML meta-learns a mapping from control inputs and device parameters to effective two-qubit Hamiltonian coefficients via simulation training, then adapts online with few measurements, recovering coefficients where Schrieffer-Wolff perturbation theory fails.