arXiv:2606. 30520v1 Announce Type: cross Abstract: Visual environments are a demanding setting for quantum reinforcement learning (QRL): high-dimensional observations, unstable RL optimisation, and constrained variational quantum circuits (VQCs) are difficult to train jointly.
arXiv:2603. 10289v2 Announce Type: replace-cross Abstract: Whether uniquely quantum resources confer advantages in fully classical, competitive environments remains an open question.
arXiv:2406. 07884v3 Announce Type: replace-cross Abstract: Using partial knowledge of a quantum state to control multiqubit entanglement is a largely unexplored paradigm in the emerging field of quantum interactive dynamics with the potential to address outstanding challenges in quantum state preparation and compression, quantum control, and quantum complexity.
arXiv:2511. 17228v2 Announce Type: replace-cross Abstract: Artificial intelligence in dynamic, real-world environments requires the capacity for continual learning.
arXiv:2608. 11884v1 Announce Type: cross Abstract: Quantum generative adversarial networks (QGANs) have attracted increasing attention for image generation using parameterized quantum circuits.
arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.
arXiv:2607. 09422v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning is well suited to problems with large parameter spaces and exploitable local structure, such as the tuning of electrostatically-defined quantum-dot arrays.
arXiv:2607. 29491v1 Announce Type: cross Abstract: Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly optimizes a variational quantum eigensolver (VQE) after extending a circuit, although circuit construction and action legality are deterministic and known.
arXiv:2606. 09778v1 Announce Type: cross Abstract: Hard safety filters are increasingly placed downstream of learned controllers to guarantee constraint satisfaction at run time.
arXiv:2511. 12482v2 Announce Type: replace-cross Abstract: Quantum error correction is essential for fault-tolerant quantum computing.
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:2606. 08276v1 Announce Type: cross Abstract: Quantum reinforcement learning (QRL) is a promising approach to learn effective decision strategies across several applications with stochastic environments.
arXiv:2606. 29966v1 Announce Type: cross Abstract: Quantum computing provides a powerful paradigm for representing and transforming high-dimensional information through superposition, entanglement, and measurement-induced nonlinear features.