Speaker
Description
Quantum error correction can be enhanced by rejecting runs that are likely to fail logically, but the most informative confidence metrics often require costly decoder-level likelihood information. We introduce a decoder-agnostic post-selection method that learns an abort score directly from syndrome data. A supervised classifier is trained only to distinguish syndromes sampled from low- and high-noise regimes; its output is then used to rank new runs before decoding. No logical-error labels, correction operators, or code-specific likelihood calculations are required. We apply the method to experimental logical magic-state-distillation data from a QuEra neutral-atom processor. The classifier is trained entirely on simulated syndrome data generated from a calibrated noise model and is then transferred directly to experimental data. The learned score improves experimental fidelity beyond syndrome-weight filtering and, when combined with logical-gap post-selection, improves the fidelity-throughput trade-off beyond using the logical gap alone. We have also tested the method in circuit-level simulations of the $[[144,12,12]]$ Gross bivariate-bicycle code and code-capacity simulations of the surface code, with complete numerical results reported in an accompanying preprint. These results establish syndrome-only learning as a practical machine-learning layer for reliable quantum error correction.
| Topic | Machine learning techniques for experimental quantum information science |
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