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Paper 1
Fibre bundle framework for unitary quantum fault tolerance
Daniel Gottesman, Lucy Liuxuan Zhang
- Year
- 2013
- Journal
- arXiv preprint
- DOI
- arXiv:1309.7062
- arXiv
- 1309.7062
We introduce a differential geometric framework for describing families of quantum error-correcting codes and for understanding quantum fault tolerance. This work unifies the notion of topological fault tolerance with fault tolerance in other kinds of quantum error-correcting codes. In particular, we use fibre bundles with a natural flat projective connection to study the transformation of codewords under unitary fault-tolerant evolutions. We show that the fault-tolerant logical operations are given by the monodromy group for either of two bundles, both of which have flat projective connections. As concrete realizations of the general framework, we construct the bundles explicitly for two examples of fault-tolerant families of operations, the qudit transversal gates and the string operators in the toric code.
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ADaPT: Adaptive-window Decoding for Practical fault-Tolerance
Tina Oberoi, Joshua Viszlai, Frederic T. Chong
- Year
- 2026
- Journal
- arXiv preprint
- DOI
- arXiv:2605.01149
- arXiv
- 2605.01149
Window decoding, first proposed to reduce decoding complexity for real-time decoding, is an essential component to realize scalable, universal-fault tolerant computation. Prior work has focused on improving throughput through parallelization and reducing reaction time via speculation on window boundaries. However, these methods use a fixed window size d, paying a fixed decoding time overhead for each window. In practice, we find this overhead of a fixed window size unnecessary in many cases due to the sparsity of average-case errors in QEC. Leveraging this insight, in this paper we propose an adaptive window decoding technique based on decoder confidence. This technique reduces the overhead in decoding time thus reducing reaction time without compromising on logical error rates. We benchmark adaptive window decoding across different codes and hardware inspired noise models. Our results show that this adaptive technique reaches the target error rate while maintaining a low decoding time overhead across different codes, and under different noise models.
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