Zero-Divergence GF(2) Tensor Engines for Microsecond-Latency Quantum Error Correction Decoding

The realization of Fault-Tolerant Quantum Computing (FTQC) is currently bottlenecked not by quantum hardware, but by classical decoding latency. Surface codes require classical coprocessors to identify and correct physical qubit errors well within the decoherence time window (T₁ ≈ 15 µs). Traditional graph-based decoders suffer from severe SIMT warp divergence when executed on highly parallel architectures, leading to ALU starvation and fatal latency spikes. In this paper, we introduce a branchless GF(2) tensor engine that entirely suppresses warp divergence (0.0%). Operating directly within the L2 Cache of a consumer-grade GPU (60W TDP), our architecture processes 1,000,000 Clifford gates across 2,560 logical qubits in 5.36 seconds, achieving a sustained throughput of 0.95 G-TOPS. This translates to a decoding latency of 5.36 µs per parallel gate, successfully operating within the strict decoherence threshold and providing a scalable classical infrastructure for real-time Quantum Error Correction. For strict artifact evaluation, the source code and reproducibility harnesses are publicly available at https://github.com/MAJBS/Chronos-Dialeteia-Engine.