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quant-ph2026
Data-Driven Hamiltonian Reduction for Superconducting Qubits via Meta-Learning
Arielle Sanford, Andrew T. Kamen, Frederic T. Chong +1
We introduce HAML (Hamiltonian Adaptation via Meta-Learning), a framework for fast online adaptation of effective Hamiltonian models of superconducting quantum processors. HAML pro…
quant-ph2026
Quantum Noise Suppression at Scale with Crosstalk-Robust Gate Sets
Andy J. Goldschmidt, Emilio Peláez Cisneros, Ryan Sitler +3
We introduce crosstalk-robust gate sets, which are obtained using a novel, scalable optimal control problem exploiting locality. Through the suppression of pairwise quantum crossta…
quant-ph2026
Comparing and correcting robustness metrics for quantum optimal control
Andrew T. Kamen, Samuel Fine, Bikrant Bhattacharyya +2
Control pulses that nominally optimize fidelity are sensitive to routine hardware drift and modeling errors. Robust quantum optimal control seeks error-insensitive control pulses t…