大型语言模型自动化量子电路设计，新型梯度估计器提升训练效率

报道来源 [4]

1. arXiv cs.AI TIER_1 English(EN) · Kenya Sakka, Wataru Mizukami, Kosuke Mitarai · 2026-06-12 04:00

   用于自主变分量子电路设计的LLM系统

   arXiv:2606.13380v1 Announce Type: cross Abstract: The design of high performing quantum circuits remains largely dependent on human expertise. We introduce an autonomous agentic framework that employs large language models (LLMs) to conduct iterative quantum circuit designs under…

2. arXiv cs.AI TIER_1 English(EN) · Kosuke Mitarai · 2026-06-11 14:08

   用于自主变分量子电路设计的语言模型系统

   The design of high performing quantum circuits remains largely dependent on human expertise. We introduce an autonomous agentic framework that employs large language models (LLMs) to conduct iterative quantum circuit designs under explicit design constraints. Our system integrate…

3. arXiv cs.LG TIER_1 (CA) · Brian Coyle, Snehal Raj, Virag Umathe, El Amine Cherrat, Elham Kashefi · 2026-06-09 04:00

   参数化量子电路的自适应方向梯度

   arXiv:2606.09734v1 Announce Type: cross Abstract: Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linearly in the number of trainable parameters and domi…

4. arXiv cs.LG TIER_1 (CA) · Elham Kashefi · 2026-06-08 16:59

   参数化量子电路的自适应方向梯度

   Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linearly in the number of trainable parameters and dominates the total shot budget of training at scale. …