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arXiv AI

Evolutionary BP+OSD Decoding for Low-Latency Quantum Error Correction

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.

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arXiv AI
6d ago

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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Hugging Face Trending Papers
Oct 29

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

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.

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arXiv Machine Learning
Sep 3

Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction

The paper introduces a new objective function for designing quantum error correction codes that maximizes the distinguishability of quantum states after a noise channel. This approach, called variational quantum error correction (VarQEC), uses a distinguishability loss function as a machine learning objective to discover encoding circuits tailored to specific noise characteristics. The authors demonstrate that VarQEC produces resource‑efficient codes that outperform standard codes and provide proof‑of‑concept experiments on IBM and IQM hardware.

By Nico Meyer, Christopher Mutschler, Andreas Maier, Daniel D. Scherer
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arXiv Machine Learning
Sep 4

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 AI
3d ago

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.

By Anil Bilgin, Wanda Hou, Pranav Srinivas, Yi-Zhuang You
fine-tuningefficiency
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