A new research article titled “Enhancing the performance of variational quantum classifiers with hybrid autoencoders” has been published in Quantum Information Processing, Volume 24, Issue 8 (Article 244).
The paper explores a hybrid classical–quantum learning framework, where autoencoders are used to enhance feature representations before quantum classification. The results demonstrate improved performance of variational quantum classifiers, contributing to more effective quantum machine learning pipelines for near-term quantum devices.
📎 Publication details:
Quantum Information Processing, Vol. 24, No. 8, Article 244
🔗 Journal access:
https://link.springer.com/journal/11128