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

$O(d^{3})$

. We report a distributed version of the UF decoder that exploits parallel computing resources for further speedup. Using a field-programmable gate array (FPGA)-based implementation, we empirically show that this distributed UF decoder has a sublinear average time complexity with regard to

$d$

, given

$O(d^{3})$

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 544 ns per measurement round.

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