10 citations · 13 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits
Tianlong Chen, Zhenyu Zhang, Hanrui Wang +6
Parameterized Quantum Circuits (PQC) have obtained increasing popularity thanks to their great potential for near-term Noisy Intermediate-Scale Quantum (NISQ) computers. Achieving…
RobustState: Boosting Fidelity of Quantum State Preparation via Noise-Aware Variational Training
Hanrui Wang, Yilian Liu, Pengyu Liu +10
Quantum state preparation, a crucial subroutine in quantum computing, involves generating a target quantum state from initialized qubits. Arbitrary state preparation algorithms can…
DGR: Tackling Drifted and Correlated Noise in Quantum Error Correction via Decoding Graph Re-weighting
Hanrui Wang, Pengyu Liu, Yilian Liu +4
Quantum hardware suffers from high error rates and noise, which makes directly running applications on them ineffective. Quantum Error Correction (QEC) is a critical technique towa…