Neural Network Decoders for Large-Distance 2D Toric Codes
QuTech, Delft University of Technology, P.O.Box 5046, 2600 GA Delft, The Netherlands.
| Published: | 2020-08-24, volume 4, page 310 |
| Eprint: | arXiv:1809.06640v3 |
| Doi: | https://doi.org/10.22331/q-2020-08-24-310 |
| Citation: | Quantum 4, 310 (2020). |
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Abstract
We still do not have perfect decoders for topological codes that can satisfy all needs of different experimental setups. Recently, a few neural network based decoders have been studied, with the motivation that they can adapt to a wide range of noise models, and can easily run on dedicated chips without a full-fledged computer. The later feature might lead to fast speed and the ability to operate at low temperatures. However, a question which has not been addressed in previous works is whether neural network decoders can handle 2D topological codes with large distances. In this work, we provide a positive answer for the toric code [1]. The structure of our neural network decoder is inspired by the renormalization group decoder [2,3]. With a fairly strict policy on training time, when the bit-flip error rate is lower than $9\%$ and syndrome extraction is perfect, the neural network decoder performs better when code distance increases. With a less strict policy, we find it is not hard for the neural decoder to achieve a performance close to the minimum-weight perfect matching algorithm. The numerical simulation is done up to code distance $d=64$. Last but not least, we describe and analyze a few failed approaches. They guide us to the final design of our neural decoder, but also serve as a caution when we gauge the versatility of stock deep neural networks. The source code of our neural decoder can be found at [4].
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► References
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