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Proceedings of the VLDB Endowment

QEC lake: Data Lake for AI-Enhanced Quantum Error Correction

Aadi Patwardhan, Max de Groot, Andrei Ilinescu, Aditya Shankar, Yuandou Wang, Wenbo Sun, Floris Geerts, Rihan Hai

Published Aug 1, 2026• 0 citations
Publisher

Abstract

Quantum error correction (QEC) experiments generate large data-sets that researchers use to train AI-based decoders. However, QEC datasets are heterogeneous, scarce, and fragmented: schemas differ across code families and hardware platforms, and datasets are scattered across incompatible repositories, hindering fair comparison. We present QEC Lake , a domain-specific data lake for quantum error correction. QEC Lake continuously crawls and refreshes open QEC datasets, produces user-configurable simulated data, generates synthetic variants from real data, and manages data and metadata for discovery, query, and reuse. The system also offers AI-assisted dataset exploration and a model-oriented data preparation pipeline that converts heterogeneous QEC data into training-ready inputs for AI models, including tree-based and tabular foundation models. In the demo, attendees ingest sources, generate new datasets, explore them through structured and natural-language interfaces, and export model-ready artifacts. In this way, we demonstrate how data management can accelerate data-driven QEC analysis, benchmarking, and model development. A video demonstrating QEC Lake's workflow can be found at https://youtu.be/V8lXy5H3e6k.

DOI
10.14778/3827998.3828069

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Quantum error correction (QEC) experiments generate large data-sets that researchers use to train AI-based decoders. However, QEC datasets are heterogeneous, scarce, and fragmented: schemas differ across code families and hardware platforms, and datasets are scattered across incompatible repositories, hindering fair comparison. We present QEC Lake , a domain-specific data lake for quantum error correction. QEC Lake continuously crawls and refreshes open QEC datasets, produces user-configurable simulated data, generates synthetic variants from real data, and manages data and metadata for discovery, query, and reuse. The system also offers AI-assisted dataset exploration and a model-oriented data preparation pipeline that converts heterogeneous QEC data into training-ready inputs for AI models, including tree-based and tabular foundation models. In the demo, attendees ingest sources, generate new datasets, explore them through structured and natural-language interfaces, and export model-ready artifacts. In this way, we demonstrate how data management can accelerate data-driven QEC analysis, benchmarking, and model development. A video demonstrating QEC Lake's workflow can be found at https://youtu.be/V8lXy5H3e6k.
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Quantum Error Correction (QEC) is the process of detecting and correcting errors in quantum systems, which are prone to decoherence and quantum noise. QEC is crucial for developing stable and highly accurate quantum computing systems, therefore, several research efforts have been made to develop the best QEC strategy. Recently, Google's breakthrough shows great potential to improve the accuracy of the existing error correction methods. This survey provides a comprehensive review of advancements in the use of artificial intelligence (AI) tools to enhance QEC schemes for existing Noisy Intermediate Scale Quantum (NISQ) systems. Specifically, we focus on machine learning (ML) strategies and span from unsupervised, supervised, semi-supervised, to reinforcement learning methods. It is clear from the evidence, that these methods have recently shown superior efficiency and accuracy in the QEC pipeline compared to conventional approaches. Our review covers more than 150 relevant studies, offering a comprehensive overview of progress and perspective in this field. We organized the reviewed literature on the basis of the AI strategies employed and improvements in error correction performance. We also discuss challenges ahead such as data sparsity caused by limited quantum error datasets and scalability issues as the number of quantum bits (qubits) in quantum systems kept increasing very fast. We conclude the paper with summary of existing works and future research directions aimed at deeper integration of AI techniques into QEC strategies.
Quantum computing has the potential to solve problems that are intractable for classical systems, yet the high error rates in contemporary quantum devices often exceed tolerable limits for useful algorithm execution. Quantum Error Correction (QEC) mitigates this by employing redundancy, distributing quantum information across multiple data qubits and utilizing syndrome qubits to monitor their states for errors. The syndromes are subsequently interpreted by a decoding algorithm to identify and correct errors in the data qubits. This task is complex due to the multiplicity of error sources affecting both data and syndrome qubits as well as syndrome extraction operations. Additionally, identical syndromes can emanate from different error sources, necessitating a decoding algorithm that evaluates syndromes collectively. Although machine learning (ML) decoders such as multi-layer perceptrons (MLPs) and convolutional neural networks (CNNs) have been proposed, they often focus on local syndrome regions and require retraining when adjusting for different code distances. We introduce a transformer-based QEC decoder which employs self-attention to achieve a global receptive field across all input syndromes. It incorporates a mixed loss training approach, combining both local physical error and global parity label losses. Moreover, the transformer architecture's inherent adaptability to variable-length inputs allows for efficient transfer learning, enabling the decoder to adapt to varying code distances without retraining. Evaluation on six code distances and ten different error configurations demonstrates that our model consistently outperforms non-ML decoders, such as Union Find (UF) and Minimum Weight Perfect Matching (MWPM), and other ML decoders, thereby achieving best logical error rates. Moreover, the transfer learning can save over 10x of training cost.
Quantum error correction (QEC) via error decoding is essential towards fault-tolerant quantum computing, but extremely costly to simulate. However, existing simulators are often CPU-based and suffer from severe performance bottlenecks, especially when scaling to extensive simulations with large code distances or low physical error rate. In this work, we introduce Syndrilla, a PyTorch-based across-platform QEC simulation framework. The framework is highly modular, allowing flexible integration and customization of the error model, syndrome model, decoding configuration (algorithm and data format), and logical check type. Experimental results show that Syndrilla achieves10×∼20×10 \times \sim 20 \times10×∼20×speedup over CPU, when running on both AMD and NVIDIA GPUs, and decoding data format does not degrade accuracy, demonstrating the practicality and efficiency of Syndrilla to instigate future QEC research.