Quantum Computer hooked to AI
“The results reported here constitute, to our knowledge, the first demonstration of end-to-end quantum enhancement of a production-scale, widely-deployed LLM on real superconducting quantum hardware for autoregressive language generation,” the scientists wrote in the study. “Their significance lies not in the magnitude of the perplexity improvements — which will grow with hardware fidelity and qubit count — but in the fact that they exist at all.”
IBM researchers have demonstrated a new way of using a quantum computer to fine-tune a pretrained large language models (LLM) and have achieved measurable improvements in the model’s ability to forecast text.
It did this by a quantum-assisted optimization technique that reduced the model’s “perplexity” (the metric for how well an AI predicts the next word). The model became more accurate at answering questions — including some it previously got wrong.
This is an amazing development not only for the possibility of quantum-enhanced LLM’s – but is probably the first real demonstration of quantum computing demonstrating a clear advantage of classical computing. Quantum computing has struggled to demonstrate its advantages in real-world applications. This appears to be the first real-world application that showed clear improvements.
It makes me wonder if this works so well because LLMs and AI rely on the fuzzy statistical domains that quantum computing’s big issue of stability matches well with. Perhaps it could even flip things on it’s head. Perhaps an specially trained AI system might turn out to be the perfect API (if you will) for determining outcomes from fuzzy quantum computations.
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