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arXiv AI

Distilling Knowledge from Search: Decoder Model Factory for Quantum LDPC Codes

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.

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Fast and accurate neural triage decoding of ultra-high-rate quantum LDPC codes

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arXiv AI
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Fast and accurate neural triage decoding of ultra-high-rate quantum LDPC codes

The paper introduces neural triage decoding, a new framework for high‑rate quantum LDPC codes that adaptively uses neural message‑passing to predict error corrections, followed by iterative refinement and a rare diversification step. It demonstrates that the Triage decoder can handle ultra‑high‑rate codes encoding hundreds of logical qubits, achieving near‑optimal accuracy while reducing decoding latency by one to two orders of magnitude compared to existing decoders. The approach is grounded in a rigorous analysis of bitwise marginal decoding and shows robustness to mismatched error priors.

By Hong-Ye Hu, Zhenlai Wang, Binzhao Luo, Tao Xiang, Chen Zhao
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arXiv Machine Learning
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Edge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Era

arXiv:2602. 16018v2 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) are a powerful framework for learning representations from graph-structured data, but their direct implementation on near-term quantum hardware remains challenging due to circuit depth, multi-qubit interactions, and qubit scalability constraints.

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Quantum Graph Convolutional Networks: Implementation and Trainability Analysis

The paper implements two quantum graph neural network architectures—Simplified Graph Convolution (SGC) and Linear Graph Convolution (LGC)—and evaluates them on benchmark graph datasets for semi‑supervised learning using quantum simulation. It compares their predictive performance and optimization behavior to classical baselines, finding that the quantum models achieve competitive results with fewer parameters. Additionally, the study provides a cost‑gradient analysis to identify trainable tasks and a classical simulability investigation to determine regimes where the circuits remain robust during training.

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arXiv Machine Learning
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Learning to Concatenate Quantum Codes

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
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arXiv Machine Learning
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Toward Uncertainty-Aware and Generalizable Neural Decoding for Quantum LDPC Codes

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.

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