The paper introduces neural triage decoding, a new framework that uses syndrome consistency to adaptively allocate neural computation for high-rate quantum LDPC codes. By combining a local neural message-passing network with iterative refinement and a rare diversification stage, the Triage decoder achieves state‑of‑the‑art accuracy while reducing decoding latency by one to two orders of magnitude. The approach is validated on ultra‑high‑rate qLDPC codes such as lifted‑product and Kasai codes, and demonstrates robustness to mismatched error priors.
arXiv:2607. 28422v1 Announce Type: new Abstract: Fault-tolerant quantum computing (FTQC) relies on quantum error correction to suppress physical errors and preserve logical information at scale.
By Ran Miao, Rui Luo, Xiaohan Shan, Xiaoming Sun
arXiv:2510.11593v3 Announce Type: replace-cross
Abstract: For reliable large-scale quantum computation, quantum error correction (QEC) is essential to protect logical information distributed across m...
By Seong-Joon Park, Hee-Youl Kwak, Yongjune Kim
arXiv:2607. 05814v1 Announce Type: cross Abstract: Real-time decoding is a major bottleneck in scaling quantum error correction (QEC) from noisy intermediate-scale quantum (NISQ) devices to fault-tolerant quantum computing.
By Sumit Chongder
arXiv:2608. 15760v1 Announce Type: cross Abstract: Decoding is an essential component of quantum error correction (QEC), translating stabilizer measurement outcomes into corrective actions that suppress logical errors and preserve logical quantum information.
By Changwon Lee, Tak Hur, Jeongwoo Jae, Daniel K. Park
arXiv:2601. 00242v2 Announce Type: replace-cross Abstract: Realizing the full potential of quantum computation requires Quantum Error Correction (QEC).
By Yotam Peled, David Zenati, Eliya Nachmani
The paper introduces QuBA, a Quantum Bayesian graph Attention decoder that provides expressive error-pattern recognition and calibrated uncertainty estimates for quantum error correction. It also presents SAGU, a multi-phase training framework that enhances cross-domain robustness, allowing decoding beyond the training set. Experiments on bivariate bicycle codes show that both QuBA and SAGU outperform classical belief propagation, achieving up to two orders of magnitude lower logical error rates and comparable or better performance than domain-specific training approaches.
By Xiangjun Mi, Frank Mueller
arXiv:2606. 27119v1 Announce Type: cross Abstract: 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 distances.
By Ge Yan, Shanchuan Li, Shiyi Xiao, Pengyue Ma, Hanyan Cao, Feng Pan, Yuxuan Du
The paper proposes a hybrid, learning‑based strategy for selecting quantum error‑correcting codes at each concatenation level. By estimating the effective noise channel after each level, the method chooses small, non‑additive encoders when the noise has structure and switches to standard codes as the noise becomes uniform. Simulations show that this adaptive approach can achieve a target logical error rate with up to two orders of magnitude fewer qubits than using stabilizer codes alone for strongly structured noise.
By Nico Meyer, Christopher Mutschler, Dominik Seu{\ss}, Andreas Maier, Daniel D. Scherer
The paper proposes an evolutionary belief propagation (EBP) decoder optimized with a differential evolution algorithm, combined with ordered statistics decoding (OSD), to improve quantum error correction. It introduces a multi‑objective selection rule that reduces frequent OSD activation, thereby lowering complexity. Simulations on surface codes and quantum low‑density parity‑check codes show that EBP+OSD achieves better decoding performance and significantly lower complexity than the conventional BP+OSD, especially in low‑latency scenarios.
By Hee-Youl Kwak, Seong-Joon Park, Hyunwoo Jung, Jeongseok Ha, Jae-Won Kim
arXiv:2607. 19563v1 Announce Type: cross Abstract: Quantum error correction can be enhanced by post-selecting out runs that are likely to produce a logical failure, but the most accurate measures for that require costly decoder-level information.
By Tobias Haug, Askery Canabarro, Leandro Aolita
The paper introduces GraphMP, a learning-based message‑passing decoder for quantum low‑density parity‑check codes that can transfer across different code families, sizes, error rates, and noise models without fine‑tuning. GraphMP employs local message passing for scalability and uses beam search guided by its predictions to iteratively refine error patterns, balancing decoding time against logical error rates. The authors propose a decoder model factory that trains successive models on data generated by earlier models during decoding, enabling knowledge distillation and continuous adaptation to changing noise conditions.
By Anil Bilgin, Wanda Hou, Pranav Srinivas, Yi-Zhuang You