Citation: Acharya, Rajeev, et al. "Quantum error correction below the surface code threshold." arXiv preprint arXiv:2408.13687 (2024).
Web: https://arxiv.org/abs/2408.13687
Tags: Physical, Hardware, Error-correcting-codes
This is the latest paper from Google quantum AI on fault tolerant quantum computation. In my eyes, this is the first solid demonstration of below-threshold behavior in a quantum computer! The work is done using a distance 5 and distance 7 code. The lifetime of the qubits is increased by a factor of 2.4! Using the exponential suppression of errors, one cam imagine achieving arbitrary low logical error rates by scaling the code. This is a fantastic demonstration of the fact that error correction theory does, indeed, work in practice.
The previous work by google quantum AI claimed the same result by comparing a distance 3 and distance 5 surface code, which was very impressive, but less conclusive:
> "Suppressing quantum errors by scaling a surface code logical qubit." Nature 614, no. 7949 (2023): 676-681.
Before this, Google quantum AI came out with several papers where they first simulated the abelian anyons in the toric code, and then correctly simulated the non-abelian boundary defects as well:
> Satzinger, K. J., et al. "Realizing topologically ordered states on a quantum processor." Science 374.6572 (2021): 1237-1241.
> "Non-Abelian braiding of graph vertices in a superconducting processor." Nature 618, no. 7964 (2023): 264-269.
The first experiment by Google quantum AI which demonstrated any sort of exponential suppression of errors using error correction was a simple 1D bit-flip code:
> "Exponential suppression of bit or phase errors with cyclic error correction." Nature 595, no. 7867 (2021): 383-387.
Prior to this focus on error correction, the first efforts by Google quantum AI were to make a computer which could demonstrate any sort of post-classical feat. This took several years, culminating in the following seminal paper:
> Arute, Frank, et al. "Quantum supremacy using a programmable superconducting processor." Nature 574.7779 (2019): 505-510.
Overall, the google quantum AI team has been working hard since 2013. It took them about 10 years, but they finally did it! They still have a long ways to go, though.