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Paper 1

Artificial Intelligence for Quantum Error Correction: A Comprehensive Review

Zihao Wang, Hao Tang

Year

2024

Journal

arXiv preprint

DOI

arXiv:2412.20380

arXiv

2412.20380

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.

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Paper 2

Efficient magic state cultivation with lattice surgery

Yutaka Hirano, Riki Toshio, Tomohiro Itogawa, Keisuke Fujii

Year

2025

Journal

arXiv preprint

DOI

arXiv:2510.24615

arXiv

2510.24615

Magic state distillation plays a crucial role in fault-tolerant quantum computation and represents a major bottleneck. In contrast to traditional logical-level distillation, physical-level distillation offers significant overhead reduction by enabling direct implementation with physical gates. Magic state cultivation is a state-of-the-art physical-level distillation protocol that is compatible with the square-grid connectivity and yields high-fidelity magic states. However, it relies on the complex grafted code, which incurs substantial spacetime overhead and complicates practical implementation. In this work, we propose an efficient cultivation-based protocol compatible with the square-grid connectivity. We reduce the spatial overhead by avoiding the grafted code and further reduce the average spacetime overhead by utilizing code expansion and enabling early rejection. Numerical simulations show that, with a color code distance of 3 and a physical error probability of $10^{-3}$, our protocol achieves a logical error probability for the resulting magic state comparable to that of magic state cultivation ($\approx 3 \times 10^{-6}$), while requiring about half the spacetime overhead. Our work provides an efficient and simple distillation protocol suitable for megaquop use cases and early fault-tolerant devices.

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