arXiv:2607. 16030v1 Announce Type: cross Abstract: Quantum continual learning aims to train quantum models on sequential tasks without losing previously learned knowledge.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2511. 17228v2 Announce Type: replace-cross Abstract: Artificial intelligence in dynamic, real-world environments requires the capacity for continual learning.
arXiv:2509. 14026v2 Announce Type: replace-cross Abstract: Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power of learnable activation functions.
arXiv:2606. 24933v1 Announce Type: cross Abstract: Recent advances in quantum machine learning have motivated efficient models for sequential data processing.
arXiv:2608. 10464v1 Announce Type: new Abstract: Incremental learning models are required to learn new classes sequentially without catastrophic forgetting, while operating under parameter and memory constraints.
arXiv:2608. 17069v1 Announce Type: cross Abstract: Federated deployments of variational quantum classifiers are attractive for cross-organisation risk prediction in supply chains, because raw data never leaves the client, yet data-protection regulations such as the GDPR grant clients a right to request that their contribution be removed from a trained model after the fact.
arXiv:2606. 26312v1 Announce Type: cross Abstract: Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning compact, structured representations.