1 citations · 1 across the 6 of their papers we have counts for
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Learning to Decode in Parallel: Self-Coordinating Neural Network for Real-Time Quantum Error Correction
Kai Zhang, Zhengzhong Yi, Shaojun Guo +13
Fast, reliable decoders are pivotal components for enabling fault-tolerant quantum computation (FTQC). Neural network decoders like AlphaQubit have demonstrated potential, achievin…
Quantum Design Automation: Foundations, Challenges, and the Road Ahead
Feng Wu, Jingzhe Guo, Tian Xia +12
Quantum computing is transitioning from laboratory research to industrial deployment, yet significant challenges persist: system scalability and performance, fabrication yields, an…
LATTE: A Decoding Architecture for Quantum Computing with Temporal and Spatial Scalability
Kai Zhang, Jubo Xu, Fang Zhang +3
Quantum error correction allows inherently noisy quantum devices to emulate an ideal quantum computer with reasonable resource overhead. As a crucial component, decoding architectu…
Learning Neural Decoding with Parallelism and Self-Coordination for Quantum Error Correction
Kai Zhang, Situ Wang, Linghang Kong +3
Fast, reliable decoders are pivotal components for enabling fault-tolerant quantum computation. Neural network decoders like AlphaQubit have demonstrated significant potential, ach…
Louvre: Relaxing Hardware Requirements of Quantum LDPC Codes by Routing with Expanded Quantum Instruction Set
Runshi Zhou, Fang Zhang, Hui-Hai Zhao +3
Generalized bicycle codes (GB codes) represent a promising family of quantum low-density parity-check codes, characterized by high code rates and relatively local qubit connectivit…
Benchmarking fault-tolerant quantum computing hardware via QLOPS
Linghang Kong, Fang Zhang, Jianxin Chen
It is widely recognized that quantum computing has profound impacts on multiple fields, including but not limited to cryptography, machine learning, materials science, etc. To run…