3 citations · 4 across the 7 of their papers we have counts for
7 papers
Efficient foundation decoders for fault-tolerant quantum computing
Ge Yan, Shanchuan Li, Shiyi Xiao +4
Foundation decoders, a class of high-capacity neural decoders, are leading candidates for fault-tolerant quantum computing, with accurate and efficient decoding at large code dista…
Maximum Likelihood Decoding of Quantum Error Correction Codes
Hanyan Cao, Ge Yan, Yuxuan Du +1
Quantum error correction (QEC) is indispensable for realizing fault-tolerant quantum computation, yet its effectiveness hinges critically on the classical decoding algorithm that i…
Differentiable Maximum Likelihood Noise Estimation for Quantum Error Correction
Hanyan Cao, Dongyang Feng, Cheng Ye +2
Accurate noise estimation is essential for fault-tolerant quantum computing, since decoding per formance depends critically on the fidelity of circuit-level noise parameters. We in…
Integrating Neural Networks and Tensor Networks for Computing Free Energy
Hanyan Cao, Yijia Wang, Feng Pan +1
Computing free energy is a fundamental problem in statistical physics. Recently, two distinct methods have been developed and have demonstrated remarkable success: the tensor-netwo…
Generative Decoding for Quantum Error-correcting Codes
Hanyan Cao, Feng Pan, Dongyang Feng +2
Efficient and accurate decoding of quantum error-correcting codes is essential for fault-tolerant quantum computation, however, it is challenging due to the degeneracy of errors, t…
Exact Decoding of Repetition Code under Circuit Level Noise
Hanyan Cao, Shoukuan Zhao, Dongyang Feng +8
Repetition code forms a fundamental basis for quantum error correction experiments. To date, it stands as the sole code that has achieved large distances and extremely low error ra…
qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformers
Hanyan Cao, Feng Pan, Yijia Wang +1
We propose a general framework for decoding quantum error-correcting codes with generative modeling. The model utilizes autoregressive neural networks, specifically Transformers, t…