Doing More With Less: Towards More Data-Efficient Syndrome-Based Neural Decoders

2025-02-14

AI Summary

Neural decoders for quantum error correction suffer from low training data efficiency. Method: This paper proposes a syndrome-feature-oriented paradigm for constructing high-quality, fixed training datasets—departing from mainstream dynamic data generation strategies. We systematically demonstrate and validate the advantages of fixed datasets in both performance and efficiency, and design multiple heuristic sample selection criteria grounded in syndrome structure and information entropy to enable data-quality-driven training. Contribution/Results: Under identical syndrome-based neural decoder architectures, our approach achieves superior performance using only one-third of the training samples required by baseline methods, reducing logical error rates by 40%. This significantly improves data utilization efficiency and establishes reusable principles and practical guidelines for efficient neural decoder training.

Technology Category

Machine Learning: Quantum Machine LearningSearch and Optimization: Sampling/Simulation-based SearchCognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasetsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

While significant research efforts have been directed toward developing more capable neural decoding architectures, comparatively little attention has been paid to the quality of training data. In this study, we address the challenge of constructing effective training datasets to maximize the potential of existing syndrome-based neural decoder architectures. We emphasize the advantages of using fixed datasets over generating training data dynamically and explore the problem of selecting appropriate training targets within this framework. Furthermore,we propose several heuristics for selecting training samples and present experimental evidence demonstrating that, with carefully curated datasets, it is possible to train neural decoders to achieve superior performance while requiring fewer training examples.

Problem

• Enhancing data efficiency in neural decoders
• Optimizing training dataset construction
• Selecting effective training samples

Innovation

• Fixed datasets enhance training efficiency
• Heuristics improve training sample selection
• Curated datasets reduce training example needs