# FPGA-based Distributed Union-Find Decoder for Surface Codes

arXiv: https://arxiv.org/abs/2406.08491 · HTML: https://mlsanity.com/paper/2406.08491/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2024.

## Built on by (most cited first, 2 of 2)

- [Rethink the Role of Neural Decoders in Quantum Error Correction](https://mlsanity.com/paper/2605.12046/) (2026)
- [Ultrafast On-Chip Online Learning via Spline Locality in Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2602.02056/) (2026)

## Abstract (arXiv)

A fault-tolerant quantum computer must decode and correct errors faster than they appear to prevent exponential slowdown due to error correction. The Union-Find (UF) decoder is promising with an average time complexity slightly higher than O(d3). We report a distributed version of the UF decoder that exploits parallel computing resources for further speedup. Using an FPGA-based implementation, we empirically show that this distributed UF decoder has a sublinear average time complexity with regard to d, given O(d3) parallel computing resources. The decoding time per measurement round decreases as d increases, the first time for a quantum error decoder. The implementation employs a scalable architecture called Helios that organizes parallel computing resources into a hybrid tree-grid structure. Using a Xilinx VCU129 FPGA, we successfully implement d up to 21 with an average decoding time of 11.5 ns per measurement round under 0.1% phenomenological noise, and 23.7 ns for d=17 under equivalent circuit-level noise. This performance is significantly faster than any existing decoder implementation. Furthermore, we show that Helios can optimize for resource efficiency by decoding d=51 on a Xilinx VCU129 FPGA with an average latency of 544ns per measurement round.