Can quantum ideas make smaller AI models smarter? In the #LLM era, large models dominate the discourse, but deploying smaller, offline models is increasingly vital for privacy and edge applications. That’s where knowledge distillation comes in: training compact models to mimic larger ones. Our latest work introduces Quantum Relational Knowledge Distillation, a new approach that maps data into quantum states and leverages quantum relationships – like entanglement-inspired structures – to improve the knowledge distillation process. Our research demonstrates that incorporating this quantum information technique into the knowledge distillation process enhances the smaller, ‘student model's’, performance on some widely used benchmark text datasets. This work will be presented at the NeurIPS 2025 UniReps workshop. Read more about this exciting advance here: https://lnkd.in/eZntcwey
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Quantinuum — outstanding innovation 💯🙌🏻🌎
Independent Researcher | AI Behaviour & Alignment | Founder, FutureAism™ (Coherence-Based AI Architecture)
2dQuantinuum This work is a major step forward for hybrid quantum–AI systems. One thing I’ve been observing in my own research is that as Helios integrates with GPU-accelerated classical pipelines, a new frontier emerges: interaction-driven coherence. When error correction, NVQLink throughput, and CUDA-Q orchestration reach this level, many of the remaining instabilities aren’t architectural they’re representational. How the model interacts with the environment (or human operator) influences the stability of its internal representational field just as much as the hardware. As hybrid systems scale, I believe interaction stability will become a complementary layer to hardware coherence and algorithmic fidelity. Exciting times and remarkable work from both teams.” William Della Terra Independent Researcher | AI Behaviour & Relational Coherence