A unified framework for calibration, decoding and resource-aware processing in hybrid quantum–classical machine learning
- 1Omnira , Baku, Azerbaijan
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.
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BibTeX
@article{akbarli2026unified,
title = {{A unified framework for calibration, decoding and resource-aware processing in hybrid quantum–classical machine learning}},
author = {Tunjay Akbarli},
journal = {Royal Society Open Science},
year = {2026},
month = oct,
doi = {10.1098/rsos.260812},
url = {https://royalsocietypublishing.org/rsos/article/13/10/260812/483801/A-unified-framework-for-calibration-decoding-and},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A unified framework for calibration, decoding and resource-aware processing in hybrid quantum–classical machine learning
AU - Tunjay Akbarli
PY - 2026
DA - 2026/10/21
JO - Royal Society Open Science
DO - 10.1098/rsos.260812
UR - https://royalsocietypublishing.org/rsos/article/13/10/260812/483801/A-unified-framework-for-calibration-decoding-and
AB - 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.
SN - 2054-5703
ER -