3 papers
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
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…
cond-mat.mtrl-sci2024
Erbium doped yttrium oxide thin films grown by chemical vapour deposition for quantum technologies
Anna Blin, Alexander Kolar, Andrew Kamen +8
The obtention of quantum-grade rare-earth doped oxide thin films that can be integrated with optical cavities and microwave resonators is of great interest for the development of s…