标题： 研究具有量子神经网络驱动的迭代Harrow-Hassidim-Lloyd算法的少量簇共振 显示英文标题

标题： Studying few cluster resonances with quantum neural network driven iterative Harrow-Hassidim-Lloyd algorithm

摘要： 通过使用量子计算，可以在微观团簇模型中研究超核${}^5_{\Lambda}$He、${}^{\ 6}_{{\Lambda\Lambda}}$He 和${}^9_{\Lambda}$Be 的性质。我们的方法将量子神经网络（QNN）与迭代Harrow-Hassidim-Lloyd（IHHL）算法（简称为QNN-IHHL）相结合，以解决量子多体问题。为了有效描述共振现象，我们采用复数缩放和特征向量延续技术，为在量子计算中识别少团簇共振参数提供了稳健的框架。为了验证我们的量子算法，选择$4^{+}$状态的${}^9_{\Lambda}$Be 作为核心例子。通过QNN-IHHL算法，我们实现了完全量子的工作流程，这为探索复杂核多体系统中的共振特性提供了新的框架和一些基础工作。 显示英文摘要

摘要： By using the quantum computing the properties of hypernuclei ${}^5_{\Lambda}$He, ${}^{\ 6}_{{\Lambda\Lambda}}$He and ${}^9_{\Lambda}$Be can be investigated within microscopic cluster model. Our approach combines quantum neural network (QNN) with iterative Harrow-Hassidim-Lloyd (IHHL) algorithm (abbreviated as QNN-IHHL) to solve the quantum many-body problem. To efficiently describe resonance phenomena, we employ complex scaling and eigenvector continuation techniques, providing a robust framework for identifying few-cluster resonance parameters within quantum computing. To validate our quantum algorithm, the resonant $4^{+}$ state of ${}^9_{\Lambda}$Be is chosen as a core example. With QNN-IHHL algorithm we realize a fully quantum workflow, which provides a novel framework and some ground work for exploring resonance properties in complex nuclear many-body systems.