Get Started
Home
Topics
Search
Library
Research questionHow can machine-learning decoders for quantum error correction generalize to unseen codes while providing calibrated uncertainty?Machine-learning decoders can perform well on codes seen during training yet become unreliable or overconfident when the code changes. Fault-tolerant operation requires uncertainty estimates that remain meaningful during decoding of previously unseen quantum error-correcting codes.
AI
Machine Learning
Research Paper
Statistical Machine Learning
Technology
Latest papersRecent research connected to this question, newest first.Toward Uncertainty-Aware and Generalizable Neural Decoding for Quantum LDPC CodesThe evidence covers QuBA and the SAGU training framework evaluated on bivariate bicycle codes and coprime variants. Reported results include comparisons with belief propagation, confident-decision logical error rates, and performance on the coprime [[154,6,16]] code; the experiments do not establish generalization beyond these code families.research paper · Sep 2, 2026
Related questions
How can quantum error-correcting encodings adapt to device-specific noise with less overhead?How can we select quantum error-correction codes across concatenation levels as the effective noise changes?How can synthetic-noise benchmarks reliably predict decoder rankings on real quantum hardware?How can lossy speculative decoding accelerate LLM inference without distorting token distributions or degrading generation quality?