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Paper 1
Nonadiabatic holonomic multiqubit controlled gates
P. Z. Zhao, G. F. Xu, D. M. Tong
- Year
- 2019
- Journal
- arXiv preprint
- DOI
- arXiv:1912.09796
- arXiv
- 1912.09796
Previous schemes of nonadiabatic holonomic quantum computation were focused mainly on realizing a universal set of elementary gates. Multiqubit controlled gates could be built by decomposing them into a series of the universal gates. In this article, we propose an approach for realizing nonadiabatic holonomic multiqubit controlled gates in which a $(n+1)$-qubit controlled-$(\boldsymbol{\mathrm{n}\cdot \mathrmσ})$ gate is realized by $(2n-1)$ basic operations instead of decomposing it into the universal gates, whereas an $(n+1)$-qubit controlled arbitrary rotation gate can be obtained by combining only two such controlled-$(\boldsymbol{\mathrm{n}\cdot \mathrmσ})$ gates. Our scheme greatly reduces the operations of nonadiabatic holonomic quantum computation.
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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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