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新的P-GONE方法优化量子电路分解

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为P-GONE的新方法，该方法使用条件扩散模型和图神经网络来优化量子计算的Trotter Suzuki分解。该方法联合学习分组、顺序和时间步长分配，与现有方法相比，显著压缩了电路深度。P-GONE在电路深度方面实现了高达19.4倍的压缩，并在标准去极化噪声模型下显示出2倍的噪声保真度提升。 AI

影响 优化量子电路分解，可能在NISQ硬件上实现更复杂的模拟。

排序理由 该集群包含一篇详细介绍量子物理优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的P-GONE方法优化量子电路分解

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该集群包含一篇详细介绍量子物理优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · WenBin Yan ·

    物理引导生成优化用于Trotter Suzuki分解

    arXiv:2605.13268v2 Announce Type: replace-cross Abstract: Trotter Suzuki product formulas are the standard route to Hamiltonian evolution on noisy intermediate-scale quantum (\NISQ{}) hardware, but their accuracy depends on three coupled choices: term grouping, product-formula or…

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