Public articles linked to the same research event.
arXiv The work introduces QuLoC, a photonic quantum-assisted LLM compression algorithm that uses quantum circuit outputs to gate the retained low-rank components during training and recovers performance through local functional reconstruction followed by end-to-end knowledge distillation; after training the gating coefficients are absorbed into the low-rank factors so the compressed model runs on classical hardware without executing quantum circuits at inference, achieving a 9.73% relative improvement in average accuracy over state-of-the-art baselines on Qwen3.5-4B and comparable or higher average downstream accuracy on LLaMA-7B across different parameter compression ratios.
The work introduces QuLoC, a photonic quantum-assisted LLM compression algorithm that uses quantum circuit outputs to gate the retained low-rank components during training and recovers performance through local functional reconstruction followed by end-to-end knowledge distillation; after training the gating coefficients are absorbed into the low-rank factors so the compressed model runs on classical hardware without executing quantum circuits at inference, achieving a 9.73% relative improvement in average accuracy over state-of-the-art baselines on Qwen3.5-4B and comparable or higher average downstream accuracy on LLaMA-7B across different parameter compression ratios.
The work introduces QuLoC, a photonic quantum-assisted LLM compression algorithm that uses quantum circuit outputs to gate the retained low-rank components during training and recovers performance through local functional reconstruction followed by end-to-end knowledge distillation; after training the gating coefficients are absorbed into the low-rank factors so the compressed model runs on classical hardware without executing quantum circuits at inference, achieving a 9.73% relative improvement in average accuracy over state-of-the-art baselines on Qwen3.5-4B and comparable or higher average downstream accuracy on LLaMA-7B across different parameter compression ratios.
The work introduces QuLoC, a photonic quantum-assisted LLM compression algorithm that uses quantum circuit outputs to gate the retained low-rank components during training and recovers performance through local functional reconstruction followed by end-to-end knowledge distillation; after training the gating coefficients are absorbed into the low-rank factors so the compressed model runs on classical hardware without executing quantum circuits at inference, achieving a 9.73% relative improvement in average accuracy over state-of-the-art baselines on Qwen3.5-4B and comparable or higher average downstream accuracy on LLaMA-7B across different parameter compression ratios.