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May, 2024
预测基态性质:常数样本复杂度与深度学习算法
Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms
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Marc Wanner, Laura Lewis, Chiranjib Bhattacharyya, Devdatt Dubhashi, Alexandru Gheorghiu
TL;DR
学习地面状态性质的量子多体物理问题中,提出了两种方法来实现恒定的样本复杂度,一种是对已知兴趣属性的ML模型进行简单修改,另一种是使用深度神经网络模型进行预测。
Abstract
A fundamental problem in
quantum many-body physics
is that of finding
ground states
of local Hamiltonians. A number of recent works gave provably efficient
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