arXiv:2607. 16030v1 Announce Type: cross Abstract: Quantum continual learning aims to train quantum models on sequential tasks without losing previously learned knowledge.
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arXiv:2511. 17228v2 Announce Type: replace-cross Abstract: Artificial intelligence in dynamic, real-world environments requires the capacity for continual learning.
The paper investigates whether quantum reinforcement learning algorithms can be matched by efficient classical methods. It focuses on a simplified reinforcement learning setting with a uniform generative model, providing finite‑sample guarantees for classical kernelized Fitted Q‑Iteration that uses kernels aligned with parameterized quantum circuits. The authors identify sufficient conditions on data encoding, kernel choice, and problem structure under which this classical approach dequantizes quantum Q‑learning, and suggest using kernelized Fitted Q‑Iteration as a heuristic when those conditions cannot be verified.
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:2608. 19306v1 Announce Type: cross Abstract: Given a set of input states, we consider the task of predicting the expectation value of a Pauli observable at the output of an unknown quantum evolution, using only a limited number of measurements.
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.