# Quantum-Inspired Learning

Quantum-inspired intrusion detectors are often shown through a single high score, leaving open whether that conclusion survives a different split, preprocessing pipeline or deployment budget. Q-IDS Bench evaluates nine quantum-inspired configurations on five network and IoT datasets under one leakage-aware protocol with five random seeds, all on classical hardware. Best weighted F1 ranges from 98.86% to 99.95%, but the leading architecture changes with the dataset, and only about 1.13 percentage points separate the first- and ninth-ranked configurations by five-dataset mean.

Quantum-inspired intrusion detectors are often shown through a single high score, leaving open whether that conclusion survives a different split, preprocessing pipeline or deployment budget. Q-IDS Bench evaluates nine quantum-inspired configurations on five network and IoT datasets under one leakage-aware protocol with five random seeds, all on classical hardware. Best weighted F1 ranges from 98.86% to 99.95%, but the leading architecture changes with the dataset, and only about 1.13 percentage points separate the first- and ninth-ranked configurations by five-dataset mean.