A unified framework for calibration, decoding and resource-aware processing in hybrid quantum–classical machine learning

Abstract

Hybrid quantum–classical machine learning on noisy intermediate-scale quantum (NISQ) devices suffers from three coupled overheads: hardware miscalibration, classical syndrome-decoding latency and shot noise on gradient estimators. We present quantum calibration and observable processing (QCOP), a unified closed-loop control layer integrating a spectral calibration engine (SCE), a hierarchical syndrome decoder (HSD) and an observable compression layer (OCL) within a single GPU-accelerated pipeline. Three theoretical bounds characterize the joint effect on logical error rate (LER), convergence rate and observable variance. On device-calibrated noise-model simulators (n=20 seeds), QCOP achieves up to 2× wall-clock training speed-up and approximately 40% variational quantum eigensolver (VQE) energy error reduction relative to the unaugmented baseline. A hardware pilot on ibm_nairobi at surface-code distance d=5 (2000 syndrome volumes, 8192 shots each, single calibration window) yields a 1.6× latency reduction and 1.9× LER reduction for the HSD over minimum-weight perfect matching (MWPM)—directionally consistent with simulation but quantitatively smaller, indicating noise sources beyond the model. This pilot exercises the HSD alone: the SCE and the OCL have not been run on physical hardware, and the three modules have never been executed jointly on a device. The end-to-end QCOP benefits—speed-up, energy error reduction and variance reduction—are therefore not hardware-validated, and end-to-end hardware validation of all three modules remains future work. Code and model weights are available at https://github.com/hekimoghlu/QCOP.