Quantum-enhanced Large Language Models on Quantum Hardware via Cayley Unitary Adapters
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866909020581789696 |
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| author | Aizpurua, Borja Singh, Sukhbinder Kshetrimayum, Augustine Jahromi, Saeed S. Orus, Roman |
| author_facet | Aizpurua, Borja Singh, Sukhbinder Kshetrimayum, Augustine Jahromi, Saeed S. Orus, Roman |
| contents | 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 use, by 1.4% with only 6,000 additional parameters and end-to-end inference validated on real Quantum Processing Unit (QPU). A systematic study on SmolLM2 (135M parameters), chosen for its tractability, reveals monotonically improving perplexity with unitary block dimension, 83% recovery of compression-induced degradation, and correct answers to questions that both classical baselines fail -- with a sharp noise-expressivity phase transition identifying the concrete path to quantum utility at larger qubit scales. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05914 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quantum-enhanced Large Language Models on Quantum Hardware via Cayley Unitary Adapters Aizpurua, Borja Singh, Sukhbinder Kshetrimayum, Augustine Jahromi, Saeed S. Orus, Roman Quantum Physics Artificial Intelligence Machine Learning 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 use, by 1.4% with only 6,000 additional parameters and end-to-end inference validated on real Quantum Processing Unit (QPU). A systematic study on SmolLM2 (135M parameters), chosen for its tractability, reveals monotonically improving perplexity with unitary block dimension, 83% recovery of compression-induced degradation, and correct answers to questions that both classical baselines fail -- with a sharp noise-expressivity phase transition identifying the concrete path to quantum utility at larger qubit scales. |
| title | Quantum-enhanced Large Language Models on Quantum Hardware via Cayley Unitary Adapters |
| topic | Quantum Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2605.05914 |