Zero-Overhead Syndrome Decoding on Real Quantum Hardware via a Centering-Matrix SNN Syndrome Error Corrector

We present noisebridge, an open-source quantum error correction (QEC) SDK implementing a Syndrome Neural Network (SNN) decoder based on a centering-matrix architecture. We report the first published result of zero decoder overhead on real IBM quantum hardware: on IBM Kingston (156-qubit Heron r2 processor, heavy-hex topology), a Z-only rotated surface code of distance d=3 achieves LER_SNN = LER_raw = 0.0422 at p=0.25 — the SNN decoder introduces no additional logical error rate beyond the irreducible hardware noise floor. At p=0.10 the SNN achieves a 1.72x improvement over the standard minimum-weight lookup decoder (LER_SNN=0.0457 vs LER_std=0.0784). A critical architectural insight enables these results: the initial state |0>^9 is not an eigenstate of X-stabilizers, rendering X-syndrome measurement pure noise that inflates circuit depth from 26 to 242 in standard implementations. By restricting to Z-stabilizer measurement only (13 qubits, depth 26, CZ=25, SWAP=0), syndrome accuracy reaches 83.3%, allowing the SNN to bridge the remaining gap to the fault-tolerance threshold. Key results:- Circuit depth: 26 (vs 242 in prior versions, -89%)- Two-qubit gates: CZ=25 (vs CZ=153, -84%)- Z-syndrome accuracy: 83.3% at p=0.10 / 82.5% at p=0.25- LER_SNN = 0.0457 (1.72x over std decoder) at p=0.10- LER_SNN = LER_raw = 0.0422 (zero overhead) at p=0.25- Hardware: IBM Kingston, 13 May 2026 All experiments executed on live IBM hardware. Code available at github.com/noisebridge under MIT license.