Quantum-enhanced Large Language Models on Quantum Hardware via Cayley Unitary Adapters
Large language models (LLMs) have transformed artificial intelligence, yet classical architectures impose a fundamental constraint: every trainable parameter demands classical memory that scales unfavourably with model size. Quantum computing offers a qualitatively different pathway, but practical demonstrations on real hardware have remained elusive for models of practical relevance. Here we show that Cayley-parameterised unitary adapters -- quantum circuit blocks inserted into the frozen projection layers of pre-trained LLMs and executed on a 156-qubit IBM Quantum System Two superconducting processor -- improve the perplexity of Llama 3.1 8B, an 8-billion-parameter model in widespread u...
| Authors | Borja Aizpurua, Sukhbinder Singh, Augustine Kshetrimayum et al. |
|---|---|
| Primary category | quant-ph |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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