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The paper proposes GraphMP, a neural message-passing decoder for quantum LDPC codes that uses beam search and knowledge distillation to improve over successive model generations. Its claims of transfer across code families, sizes, and noise models are promising, but they rest on simulations, and the "decoder model factory" and coupled quantum-classical architecture remain a long-term vision. Readers should watch for benchmarks against established decoders on real hardware noise and for the latency cost of the search step.
Distilling Knowledge from Search: Decoder Model Factory for Quantum LDPC Codes
arXiv:2610.41398v1 Announce Type: new Abstract: We introduce GraphMP, a learning-based message-passing decoder for quantum low-density parity-check codes that transfers without fine-tuning across code families, code sizes, physical error rates, and noise models. GraphMP uses local message passing to keep computation scalable as the decoding graph grows. Guided by its predictions, beam search progressively refines candidate error patterns, trading additional decoding time for lower logical error rates. We develop the decoder model factory as a paradigm for training successive models on experience generated by earlier models during decoding. Model-guided search supplies verified trajectories for knowledge distillation, folding computation spent on decoding into reusable training data. Repeated collection and distillation improve the decoder across generations and adapt its strategy as the noise changes. These results motivate a coupled quantum--classical architecture with two interacting cycles: rapid decoding protects quantum information, while slower learning renews the models from operating experience. The long-term vision is a scalable decoder model factory that produces high-performance, low-latency decoders and continually adapts them to evolving quantum hardware.
Abstract:We introduce GraphMP, a learning-based message-passing decoder for quantum low-density parity-check codes that transfers without fine-tuning across code families, code sizes, physical error rates, and noise models. GraphMP uses local message passing to keep computation scalable as the decoding graph grows. Guided by its predictions, beam search progressively refines candidate error patterns, trading additional decoding time for lower logical error rates. We develop the decoder model factory as a paradigm for training successive models on experience generated by earlier models during decoding. Model-guided search supplies verified trajectories for knowledge distillation, folding computation spent on decoding into reusable training data. Repeated collection and distillation improve the decoder across generations and adapt its strategy as the noise changes. These results motivate a coupled quantum--classical architecture with two interacting cycles: rapid decoding protects quantum information, while slower learning renews the models from operating experience. The long-term vision is a scalable decoder model factory that produces high-performance, low-latency decoders and continually adapts them to evolving quantum hardware.
Quantum Physics (quant-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn); Artificial Intelligence (cs.AI)
arXiv:2610.41398 [quant-ph]
(or arXiv:2610.41398v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2610.41398 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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