论文标题
解决与神经网络量子状态的核配对模型
Solving the nuclear pairing model with neural network quantum states
论文作者
论文摘要
我们提出了一种跨蒙特卡洛方法，该方法解决了利用地面波功能的人工神经网络表示的职业数量形式主义中的核多体问题。开发了随机重新配置算法的记忆效率版本，以通过最大程度地减少哈密顿量的期望值来训练网络。我们通过求解用于描述不同类型的相互作用和相互作用强度不同值配对的模型来对广泛使用核多体方法进行基准测试。尽管其多项式计算成本，但我们的方法的表现优于群集群集，并提供与数值脱离的完整配置相互作用值非常吻合的能量。
We present a variational Monte Carlo method that solves the nuclear many-body problem in the occupation number formalism exploiting an artificial neural network representation of the ground-state wave function. A memory-efficient version of the stochastic reconfiguration algorithm is developed to train the network by minimizing the expectation value of the Hamiltonian. We benchmark this approach against widely used nuclear many-body methods by solving a model used to describe pairing in nuclei for different types of interaction and different values of the interaction strength. Despite its polynomial computational cost, our method outperforms coupled-cluster and provides energies that are in excellent agreement with the numerically-exact full configuration interaction values.
