- Article
Revealing Quantum Information Encoded in Classical Images
- Otmane Ainelkitane,
- Brian Recktenwall-Calvet and
- Carlos C. N. Kuhn
- + 1 author
We study a minimal quantum pre-processing filter for image feature extraction built from angle embeddings and two Control-NOT (CNOT) gates. Our goal is to assess whether such a lightweight quantum front-end can benefit classical classifiers and to investigate whether its induced entanglement—measured via average single-qubit von Neumann entropy—relates to predictive performance. The circuit admits three spatially symmetric layouts (diagonal, vertical, and horizontal), each producing distinct feature transformations. Experiments show that the filter can provide modest gains in shallow learning settings, but it does not consistently outperform strong classical baselines. Notably, we find no reliable relationship between entanglement and classification accuracy: variations in average entropy fail to consistently track performance. These results suggest that the utility of simple quantum filters is determined more by dataset structure and model capacity than by entanglement magnitude, offering practical guidance for the design of hybrid quantum–classical learning pipelines.
9 June 2026


