86 citations · 105 across the 10 of their papers we have counts for
10 papers
Robust Non-Adiabatic Holonomic Gating in Qutrits via Inverse-Engineered Pulse Shaping and Error Compensation
Jie Lu, Ji-Ze Han, Jie-Dong Huang +3
Systematic Rabi-amplitude and detuning errors remain important sources of infidelity in high-fidelity quantum gates. We develop a robust pulse-engineering scheme for non-adiabatic…
AiDE-Q: Synthetic Labeled Datasets Can Enhance Learning Models for Quantum Property Estimation
Xinbiao Wang, Yuxuan Du, Zihan Lou +5
Quantum many-body problems are central to various scientific disciplines, yet their ground-state properties are intrinsically challenging to estimate. Recent advances in deep learn…
Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
Yuxuan Du, Xinbiao Wang, Naixu Guo +6
This tutorial intends to introduce readers with a background in AI to quantum machine learning (QML) -- a rapidly evolving field that seeks to leverage the power of quantum compute…
MG-Net: Learn to Customize QAOA with Circuit Depth Awareness
Yang Qian, Xinbiao Wang, Yuxuan Du +2
Quantum Approximate Optimization Algorithm (QAOA) and its variants exhibit immense potential in tackling combinatorial optimization challenges. However, their practical realization…
Multimodal deep representation learning for quantum cross-platform verification
Yang Qian, Yuxuan Du, Zhenliang He +2
Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executi…
Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reduction
Yang Qian, Yuxuan Du, Dacheng Tao
The variational quantum eigensolver (VQE) is a leading strategy that exploits noisy intermediate-scale quantum (NISQ) machines to tackle chemical problems outperforming classical a…
Quantum circuit architecture search on a superconducting processor
Kehuan Linghu, Yang Qian, Ruixia Wang +14
Variational quantum algorithms (VQAs) have shown strong evidences to gain provable computational advantages for diverse fields such as finance, machine learning, and chemistry. How…
Accelerating variational quantum algorithms with multiple quantum processors
Yuxuan Du, Yang Qian, Dacheng Tao
Variational quantum algorithms (VQAs) have the potential of utilizing near-term quantum machines to gain certain computational advantages over classical methods. Nevertheless, mode…
The dilemma of quantum neural networks
Yang Qian, Xinbiao Wang, Yuxuan Du +2
The core of quantum machine learning is to devise quantum models with good trainability and low generalization error bound than their classical counterparts to ensure better reliab…
Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
Sau Lan Wu, Shaojun Sun, Wen Guan +20
Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in High Energy Physics by offering computational speed-ups. In…