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-ph2025
Quantum-machine-assisted Drug Discovery
Yidong Zhou, Jintai Chen, Jinglei Cheng +8
Drug discovery is lengthy and expensive, with traditional computer-aided design facing limits. This paper examines integrating quantum computing across the drug development cycle t…
quant-ph2024
The Stabilizer Bootstrap of Quantum Machine Learning with up to 10000 qubits
Yuqing Li, Jinglei Cheng, Xulong Tang +3
Quantum machine learning is considered one of the flagship applications of quantum computers, where variational quantum circuits could be the leading paradigm both in the near-term…