55 citations · 149 across the 17 of their papers we have counts for
7 papers · 1 filter
Transformer-QEC: Quantum Error Correction Code Decoding with Transferable Transformers
Hanrui Wang, Pengyu Liu, Kevin Shao +5
Quantum computing has the potential to solve problems that are intractable for classical systems, yet the high error rates in contemporary quantum devices often exceed tolerable li…
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…
Q-Pilot: Field Programmable Qubit Array Compilation with Flying Ancillas
Hanrui Wang, Daniel Bochen Tan, Pengyu Liu +4
Neutral atom arrays have become a promising platform for quantum computing, especially the field programmable qubit array (FPQA) endowed with the unique capability of atom movement…
Atomique: A Quantum Compiler for Reconfigurable Neutral Atom Arrays
Hanrui Wang, Pengyu Liu, Daniel Bochen Tan +6
The neutral atom array has gained prominence in quantum computing for its scalability and operation fidelity. Previous works focus on fixed atom arrays (FAAs) that require extensiv…
DISQ: Dynamic Iteration Skipping for Variational Quantum Algorithms
Junyao Zhang, Hanrui Wang, Gokul Subramanian Ravi +4
This paper proposes DISQ to craft a stable landscape for VQA training and tackle the noise drift challenge. DISQ adopts a "drift detector" with a reference circuit to identify and…
Lightening-Transformer: A Dynamically-operated Optically-interconnected Photonic Transformer Accelerator
Hanqing Zhu, Jiaqi Gu, Hanrui Wang +7
The wide adoption and significant computing resource of attention-based transformers, e.g., Vision Transformers and large language models (LLM), have driven the demand for efficien…