quantum-machine-learning

This paper proposes a hybrid quantum-classical workflow for plant phenomics classification under small-data regimes, using supervised latent restructuring (PCA + LDA) to improve geometric separability before quantum kernel alignment. Experiments show improved separability but highlight compression trade-offs and the difficulty of achieving strong quantum performance.

A survey on quantum adversarial machine learning, covering attacks, defenses, and theoretical underpinnings.

This paper presents empirical evidence that quantum entanglement provides a measurable advantage in multi-agent reinforcement learning, using the CHSH game and cooperative navigation tasks to demonstrate performance improvements over classical baselines.