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English(EN) Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo

新的循环神经网络方法促进量子模拟

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为并行扫描循环神经网络量子态（PSR-NQS）的新方法，以提高神经网络模拟量子多体系统的可扩展性。该方法利用了传统上被视为顺序的循环神经网络，并使其在变分蒙特卡洛模拟中高效训练。PSR-NQS方法在高达52x52的二维自旋晶格上展示了准确的结果，表明循环架构是可扩展神经网络量子态模拟的可行途径。 AI

影响 引入了一种更具可扩展性的复杂量子系统模拟方法，可能加速凝聚态物理领域的研究。

排序理由 该集群包含一篇详细介绍量子系统模拟新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的循环神经网络方法促进量子模拟

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该集群包含一篇详细介绍量子系统模拟新方法的学术论文。[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) · Ehsan Khatami ·

    用于可扩展变分蒙特卡洛的并行扫描递归量子态

    Neural-network quantum states have emerged as a powerful variational framework for quantum many-body systems, with recent progress often driven by massively parallel architectures such as transformers. Recurrent neural network quantum states, however, are frequently regarded as i…

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