5 papers
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…
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…
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…
Universal Dynamics with Globally Controlled Analog Quantum Simulators
Hong-Ye Hu, Abigail McClain Gomez, Liyuan Chen +6
Analog quantum simulators with global control fields have emerged as powerful platforms for exploring complex quantum phenomena. Despite these advances, a fundamental theoretical q…
Using optimal control to guide neural-network interpolation of continuously-parameterized gates
Bikrant Bhattacharyya, Fredy An, Dominik Kozbiel +2
Control synthesis for continuously-parameterized families of quantum gates can enable critical advantages for mid-sized quantum computing applications in advance of fault-tolerance…