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Exact Maximum Likelihood Decoding beyond Treewidth via Rank-Decomposition Dynamic Programming
Bin Cheng, Feng Pan
Maximum-likelihood decoding provides an optimal decoding strategy for quantum error correction under stochastic Pauli noise. However, computing logical-class probabilities is chall…
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…
Parallelizing Large-Scale Tensor Network Contraction on Multiple GPUs
Feng Pan, Hanfeng Gu, Paul Springer +1
Exact tensor network contraction underpins quantum circuit simulation, quantum error correction, combinatorial optimization, and many-body dynamics. The dominant parallelization st…
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…
Branch-and-Bound Tensor Networks for Exact Ground-State Characterization
Yijia Wang, Xuanzhao Gao, Pan Zhang +2
Characterizing the ground-state properties of disordered systems, such as spin glasses and combinatorial optimization problems, is fundamental to science and engineering. However,…
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…