arXiv:2606. 08658v1 Announce Type: new Abstract: LLMs have revolutionized knowledge representation and retrieval, but lack the explicit modeling that knowledge ontologies possess.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2608. 01194v1 Announce Type: cross Abstract: Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues.
arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.
Quantum machine learning has recently emerged as a promising paradigm that leverages the expressive power of quantum circuits to address complex learning tasks. In this work, we investigate the applicability of hybrid quantum-classical neural networks to sentiment analysis, a central problem in natural language processing.
arXiv:2607. 13847v1 Announce Type: cross Abstract: Many datasets encountered across a wide range of domains possess rich geometric and topological structure that is difficult to capture using conventional vector-based representations.
arXiv:2607. 01943v1 Announce Type: new Abstract: Quantum machine learning has recently emerged as a promising paradigm that leverages the expressive power of quantum circuits to address complex learning tasks.
arXiv:2606. 13811v1 Announce Type: cross Abstract: Can Large Language Models (LLMs) understand and reason about quantum operators?