arXiv:2606. 08658v1 Announce Type: new Abstract: LLMs have revolutionized knowledge representation and retrieval, but lack the explicit modeling that knowledge ontologies possess.
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arXiv:2609.00372v1 Announce Type: cross Abstract: With quantum sensors, simulators and networks emerging, a future of quantum technology may produce quantum states as data---that is, coherently rathe...
arXiv:2608. 01194v1 Announce Type: cross Abstract: Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues.
The paper presents a quantum machine learning approach to content-based media recommendation, mapping BBC iPlayer programme synopses to variational quantum circuits within the Quantum Natural Language Processing framework. The quantum model, trained via supervised pairwise similarity learning, matches a classical BiLSTM baseline in performance while using far fewer parameters (47k vs. 17.8M). Experiments on Quantinuum's System Model H2 confirm that the similarity signal survives realistic noise, demonstrating the feasibility of deploying variational quantum circuits for recommendation tasks.
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.