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English(EN) DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search

DreamQAS 框架提高了量子架构搜索的效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 DreamQAS，这是一个新颖的强化学习框架，旨在更有效地优化量子架构搜索。这种基于模型的方法从昂贵的 VQE 后反馈中学习，使用集成来预测无神谕分数，并支持在显式合法电路上的多步策略学习。在多个分子任务上，DreamQAS 在降低平均冻结策略能量误差和显著减少实际 VQE 调用次数方面，均优于现有方法。 AI

排序理由 该集群包含一篇关于量子架构搜索新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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DreamQAS 框架提高了量子架构搜索的效率

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该集群包含一篇关于量子架构搜索新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jiayang Niu, Yan Wang, Jie Li, Ke Deng, Azadeh Alavi, Muhammad Usman, Yongli Ren ·

    DreamQAS：学习用于VQE高效量子架构搜索的决策有用世界模型

    arXiv:2607.29491v1 Announce Type: cross Abstract: Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly optimizes a variational quantum eigensolver (VQE) after extending a circuit, although circuit construction and action legality are deterministic and know…

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