Brain-inspired quantum machine learning architecture and its benchmarking on astronomical classification

Automated classification of stars, galaxies, and quasi-stellar objects (QSOs) is essential for large-scale astronomical surveys, where reliable classifiers must remain stable across survey releases, class imbalance, and incomplete observational inputs. In this work, we present a Brain-Inspired Quantum Machine Learning (BIQML) framework for SDSS star–galaxy–QSO classification as a step toward robust source classification for next-generation photometric pipelines. The model combines a spiking Leaky Integrate-and-Fire (LIF) controller with a classically simulated variational quantum circuit (VQC), in which the controller sequentially selects quantum gates using measurement feedback; all circuits are classically simulated, and no hardware-level quantum advantage is claimed. The framework is trained and internally tested on SDSS DR17, with SDSS DR14 used as an external cross-release test set, and is benchmarked against a broad suite of baselines including Random Forest, Extra Trees, HistGradientBoosting, Logistic Regression, Linear Support Vector Machine, and Multilayer Perceptron classifiers under a repeated ten-split protocol with paired statistical significance testing. In the full-feature setting, BIQML is statistically on par with the strongest classical baselines, reaching 97.36±0.05% accuracy (96.90% balanced) on SDSS DR17 and 98.76±0.09% accuracy (98.41% balanced) on the external SDSS DR14 test set, where the leading tree ensembles span 97.3–97.8% and 98.8–99.4%, respectively. To probe robustness when key observational channels are unavailable, we introduce a low-sensor (g,r,i) no-redshift setting in which spectroscopic redshift, the u- and z-bands, and metadata are removed. Under this reduced-feature condition, BIQML remains among the top-performing models on a single controlled split (0.805 balanced accuracy, versus 0.64–0.80 for the classical baselines), achieving 85.58% accuracy on SDSS DR17 and 93.44% accuracy on SDSS DR14, suggesting that it remains competitive, degrading no more than the strongest ensembles, when discriminative information is scarce. A component-level ablation isolates the contributions of the VQC, the spiking controller, and the measurement-feedback loop, while both an analytic noise screening and a finite-shot, IBM-calibrated device-noise benchmark show that BIQML predictions remain stable under representative depolarizing, amplitude-damping, readout, and realistic NISQ-style noise channels. Together, these results position BIQML as a competitive and robust quantum-inspired architecture for astronomical source classification under cross-release shift and limited observational information.