2 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…
cs.LG2025
Protein Folding with Neural Ordinary Differential Equations
Arielle Sanford, Shuo Sun, Christian B. Mendl
Recent advances in protein structure prediction, such as AlphaFold, have demonstrated the power of deep neural architectures like the Evoformer for capturing complex spatial and ev…