8 citations · 11 across the 7 of their papers we have counts for
9 papers
ARGON: A GNN-Empowered Compilation Framework for Scalable Neutral Atom Computing
Wenjie Sun, Xiaoyu Li, Zhigang Wang +3
Neutral atom quantum systems offer a promising pathway to large-scale quantum computing due to high qubit uniformity and flexible connectivity. To exploit this architecture, compil…
Channel-Constrained Markovian Quantum Diffusion Model from Open System Perspective
Qin-Sheng Zhu, Geng Chen, Lian-Hui Yu +2
We present a channel-constrained Markovian quantum diffusion (CCMQD) model that prepares quantum states by rigorously framing the generative process within the dynamics of open qua…
DYNAMO: Dynamic Neutral Atom Multi-programming Optimizer Towards Quantum Operating Systems
Wenjie Sun, Xiaoyu Li, Zhigang Wang +3
As quantum computing advances towards practical applications, quantum operating systems become inevitable, where multi-programming -- the core functionality of operating systems --…
Physics-Aware Compilation for Parallel Quantum Circuit Execution on Neutral Atom Arrays
Geng Chen, Guowu Yang, Wenjie Sun +4
Neutral atom quantum computers are one of the most promising quantum architectures, offering advantages in scalability, dynamic reconfigurability, and potential for large-scale imp…
HAQA: A Hardware-Guided and Fidelity-Aware Strategy for Efficient Qubit Mapping Optimization
Wenjie Sun, Xiaoyu Li, Lianhui Yu +4
Quantum algorithms rely on quantum computers for implementation, but the physical connectivity constraints of modern quantum processors impede the efficient realization of quantum…
MLQM: Machine Learning Approach for Accelerating Optimal Qubit Mapping
Wenjie Sun, Xiaoyu Li, Lianhui Yu +3
Quantum circuit mapping is a critical process in quantum computing that involves adapting logical quantum circuits to adhere to hardware constraints, thereby generating physically…
Untrained Filtering with Trained Focusing for Superior Quantum Architecture Search
Lian-Hui Yu, Xiao-Yu Li, Geng Chen +3
Quantum architecture search (QAS) represents a fundamental challenge in quantum machine learning. Unlike previous methods that treat it as a static search process, from a perspecti…
QUSL: Quantum Unsupervised Image Similarity Learning with Enhanced Performance
Lian-Hui Yu, Xiao-Yu Li, Geng Chen +3
Leveraging quantum properties to enhance complex learning tasks has been proven feasible, with excellent recent achievements in the field of unsupervised learning. However, current…
A new quantum machine learning algorithm: split hidden quantum Markov model inspired by quantum conditional master equation
Xiao-Yu Li, Qin-Sheng Zhu, Yong Hu +4
The Hidden Quantum Markov Model (HQMM) has significant potential for analyzing time-series data and studying stochastic processes in the quantum domain as an upgrading option with…