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Paper 1
All-optical cat-code quantum error correction
Jacob Hastrup, Ulrik Lund Andersen
- Year
- 2022
- Journal
- Physical Review Research
- DOI
- 10.1103/PhysRevResearch.4.043065
- arXiv
- -
The cat code is a promising encoding scheme for bosonic quantum error correction as it allows for correction against losses—the dominant error mechanism in most bosonic systems. However, it has remained unclear how the required syndrome measurement and recovery can be implemented in the optical regime. Here, we introduce a teleportation-based error-correction scheme for the cat code, using elements suitable for an optical setting. The scheme detects and corrects single-photon losses while restoring the amplitude of the cat states, thereby greatly suppressing the accumulation of errors in lossy channels.
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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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